{"schedule": {"version": "0.20", "base_url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/schedule/", "conference": {"acronym": "opengeohub-summer-school-2023", "title": "OpenGeoHub Summer School 2023", "start": "2023-08-27", "end": "2023-09-02", "daysCount": 7, "timeslot_duration": "00:05", "rooms": [{"name": "Room 21 (Sala 21)", "guid": null, "description": null, "capacity": 124}, {"name": "Room 18 (Sala 18)", "guid": null, "description": null, "capacity": 49}, {"name": "Room 17 (Sala 17)", "guid": null, "description": "Computer room", "capacity": 24}, {"name": "Room 19 (Sala 19)", "guid": null, "description": null, "capacity": 24}, {"name": "Other locations", "guid": null, "description": null, "capacity": null}], "days": [{"index": 1, "date": "2023-08-27", "day_start": "2023-08-27T04:00:00+02:00", "day_end": "2023-08-28T03:59:00+02:00", "rooms": {"Other locations": [{"id": 112, "guid": "f1070a68-1a8d-5e90-bcdb-2ba6c6718ed4", "logo": "", "date": "2023-08-27T17:00:00+02:00", "start": "17:00", "duration": "03:00", "room": "Other locations", "slug": "opengeohub-summer-school-2023-112-ice-breaker", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/KDCGDG/", "title": "Ice breaker", "subtitle": "", "track": null, "type": "Social event", "language": "en", "abstract": null, "description": "Ice breaker Ice breaker Ice breaker Ice breaker", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}]}}, {"index": 2, "date": "2023-08-28", "day_start": "2023-08-28T04:00:00+02:00", "day_end": "2023-08-29T03:59:00+02:00", "rooms": {"Room 21 (Sala 21)": [{"id": 88, "guid": "5ff0d7d6-140b-5f3b-89ac-be953c0489d0", "logo": "", "date": "2023-08-28T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-88-plenary-introduction-including-introduction-to-the-hackathons", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/SAUGBJ/", "title": "Plenary introduction, including introduction to the hackathons", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Plenary introduction, including an introduction of the lecturers", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}, {"id": 78, "guid": "c59e80bb-4044-57c4-9ff0-f329575699f9", "logo": "", "date": "2023-08-28T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-78-tidy-geographic-data-with-sf-dplyr-ggplot2-geos-and-friends-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/7JN3FV/", "title": "Tidy geographic data with sf, dplyr, ggplot2, geos and friends (part 1)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "This lecture will provide an introduction to working with geographic data using R in a \u2018tidy\u2019 way. It will focus on using the sf package to read, write, manipulate, and plot geographic data in combination with the tidyverse metapackage. Why use the sf package with the tidyverse? The lecture will outline some of the ideas underlying the tidyverse and how they can speed-up data analysis pipelines, while making data analysis code easier to read and write. We will see how the following lines\r\n\r\nlibrary(sf)\r\nlibrary(tidyverse)\r\n\r\ncan provide a foundation on which the many geographic data analysis problems can be solved. The lecture will also cover on more recently developed packages that integrate with the tidyverse to a greater and lesser extent. We will look at how the geos package, which provides a simple and high-performance interface to the GEOS library for performing geometric operations on geographic data, integrates with the tidyverse. The tidyverse is not the right tool for every data analysis task and we touch on alternatives for working with raster data, with reference to the terra package, and alternative frameworks such as data.table. Finally, we will also look at how the \u2018tidy\u2019 philosophy could be implemented in other programming languages, such as Python.\r\n\r\nThe focus throughout will be on practical skills and using packages effectively within the wider context of project management tools, integrated development environments (we recommend VS Code with appropriate extensions or RStudio), and version control systems.", "recording_license": "", "do_not_record": false, "persons": [{"id": 122, "code": "PFGJ3V", "public_name": "Robin Lovelace", "biography": "Robin Lovelace is Associate Professor of Transport Data Science at the Leeds Institute for Transport Studies (ITS) and Head of Data at the government agency Active Travel England. Robin specializes in geocomputation with a focus on developing geographic methods applied to modeling transport systems, active travel, and decarbonisation. Robin has experience not only researching but deploying transport models in inform sustainable policies and more effective use of transport investment, including as Lead Developer of the Propensity to Cycle Tool (see www.pct.bike), the basis of strategic cycle network plans nationwide. Robin has led numerous data science projects for organizations ranging from the Department for Transport to the World Bank. \r\n\r\nRobin is author of popular open source software packages including R packages stplanr, stats19 and abstr. He has authored three reproducible and open source textbooks, Microsimulation with R, Efficient R Programming, and Geocomputation with R.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 91, "guid": "9d2f4d54-7e1a-503b-8f33-6b599fb6ca8d", "logo": "", "date": "2023-08-28T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-91-tidy-geographic-data-with-sf-dplyr-ggplot2-geos-and-friends-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/XPBVY3/", "title": "Tidy geographic data with sf, dplyr, ggplot2, geos and friends (part 2)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "This lecture will provide an introduction to working with geographic data using R in a \u2018tidy\u2019 way. It will focus on using the sf package to read, write, manipulate, and plot geographic data in combination with the tidyverse metapackage. Why use the sf package with the tidyverse? The lecture will outline some of the ideas underlying the tidyverse and how they can speed-up data analysis pipelines, while making data analysis code easier to read and write. We will see how the following lines\r\n\r\nlibrary(sf)\r\nlibrary(tidyverse)\r\n\r\ncan provide a foundation on which the many geographic data analysis problems can be solved. The lecture will also cover on more recently developed packages that integrate with the tidyverse to a greater and lesser extent. We will look at how the geos package, which provides a simple and high-performance interface to the GEOS library for performing geometric operations on geographic data, integrates with the tidyverse. The tidyverse is not the right tool for every data analysis task and we touch on alternatives for working with raster data, with reference to the terra package, and alternative frameworks such as data.table. Finally, we will also look at how the \u2018tidy\u2019 philosophy could be implemented in other programming languages, such as Python.\r\n\r\nThe focus throughout will be on practical skills and using packages effectively within the wider context of project management tools, integrated development environments (we recommend VS Code with appropriate extensions or RStudio), and version control systems.", "recording_license": "", "do_not_record": false, "persons": [{"id": 122, "code": "PFGJ3V", "public_name": "Robin Lovelace", "biography": "Robin Lovelace is Associate Professor of Transport Data Science at the Leeds Institute for Transport Studies (ITS) and Head of Data at the government agency Active Travel England. Robin specializes in geocomputation with a focus on developing geographic methods applied to modeling transport systems, active travel, and decarbonisation. Robin has experience not only researching but deploying transport models in inform sustainable policies and more effective use of transport investment, including as Lead Developer of the Propensity to Cycle Tool (see www.pct.bike), the basis of strategic cycle network plans nationwide. Robin has led numerous data science projects for organizations ranging from the Department for Transport to the World Bank. \r\n\r\nRobin is author of popular open source software packages including R packages stplanr, stats19 and abstr. He has authored three reproducible and open source textbooks, Microsimulation with R, Efficient R Programming, and Geocomputation with R.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 104, "guid": "ca04e43b-7f8b-54bc-807c-8aa8974ba5fc", "logo": "", "date": "2023-08-28T15:30:00+02:00", "start": "15:30", "duration": "00:45", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-104-hackathon-workshop", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/DBXBQG/", "title": "Hackathon workshop", "subtitle": "", "track": "Hackathons", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Spatial data processing is an essential part of modern data analysis. In the course of the summer school, you will learn many new tools and techniques to process spatial data. However, you really only know something if you implement it in a concrete situation. \r\n\r\nFor this reason, we are conducting a hackathon during the summer school. You can choose between two topics and apply all the state of the art skills you are learning during the week. \r\n\r\nTo help you better understand the question and relevant domain, we will present the topics in an introductory workshop which will help get you started with the task and the submission process. \r\n\r\nBy participating, you will learn how spatial data analysis can be applied to solve real-world problems. You will gain practical skills in using the tools and techniques to analyse, interpret and model spatial data, and to use this knowledge in your respective field.", "recording_license": "", "do_not_record": false, "persons": [{"id": 113, "code": "FKXFNK", "public_name": "Deleted User", "biography": "I am a PhD student in the field of Earth and environmental sciences at the Adam Mickiewicz University in Pozna\u0144. I am interested in spatial data analysis, remote sensing and programming in R, particularly in agriculture, and I was involved in several R&D projects related to crop classification, yield prediction, and soil mapping. I also contributed to the development of R-spatial packages.", "answers": []}, {"id": 120, "code": "AQXTYJ", "public_name": "Nils Ratnaweera", "biography": "I\u2019m a freelance data scientist (see [ratnaweera.xyz/](https://www.ratnaweera.xyz/)) and researcher at the Zurich University of Applied Sciences ([ZHAW](https://www.zhaw.ch/en/about-us/person/rata/)) . I enjoy using different programming languages to solve complex, real world problems and answer interesting questions. My tools of choice include R, python, gdal, ogr2ogr, PostgresSQL, PostGIS and more.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 89, "guid": "a99e44ad-b8bd-5545-a3a8-0b5e79e5a7be", "logo": "", "date": "2023-08-28T16:15:00+02:00", "start": "16:15", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-89-research-speed-dating", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/RLZK7K/", "title": "Research Speed Dating", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Pre-order the group by their broader interest into subgroups. Split each subgroup equally where one half will be stationary and the other half be moving \u201cclockwise\u201d through the room. Pairs of people sit down for a given amount of time (~7 mins) and present their research topic to each other.", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}], "Room 18 (Sala 18)": [{"id": 64, "guid": "9728e43f-8d51-5896-a364-e2654cf86886", "logo": "", "date": "2023-08-28T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-64-introduction-to-working-with-spatial-data-in-python-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/W3998X/", "title": "Introduction to working with spatial data in Python (part 1)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Python is an extremely popular general-purpose programming language. It is used in a wide range of settings and for various purposes, including for spatial data processing and analysis. \r\n\r\nThe aim of this tutorial is to give an introduction to methods of working with spatial data using Python. The tutorial will be split into two parts, introducing two central Python packages:\r\n\r\n* `geopandas`---For working with vector layers\r\n* `rasterio`---For working with rasters\r\n\r\nThe tutorial will demonstrate typical basic workflows of processing spatial data: data input, processing, geo-computation, and exporting of the results. We will use realistic datasets, such as GTFS public transport data and remote sensing products.\r\n\r\nBy the end of the tutorial, the participants will be able to:\r\n\r\n* Import spatial data from files\r\n* Subset and process the data\r\n* Graphically display the data\r\n* Perform spatial calculations (such as calculating distances, or applying raster algebra operators)\r\n* Export the results\r\n\r\nTo follow along and reproduce the results on your own computer, the prerequisite is to be able to run Python code in a Jupyter Notebook interface, linked to a Python environment with the two above-mentioned packages installed. Instructions will be sent in advance.", "recording_license": "", "do_not_record": false, "persons": [{"id": 107, "code": "FJ7JQE", "public_name": "Michael Dorman", "biography": "Michael Dorman is a programmer (since 2016) and lecturer (since 2013) at the Department of Geography and Environmental Development, Ben-Gurion University of the Negev. He is working with researchers and students of the Department in developing computational workflows such as data processing, spatial analysis, geostatistics, development of web applications, and web maps, etc., mostly through programming in R, Python, and JavaScript. Michael holds a Ph.D. in Geography and a M.Sc. in Life Sciences from the Ben-Gurion University of the Negev, and a B.Sc. in Plant Sciences in Agriculture from The Hebrew University of Jerusalem. He published two books, \"Learning R for Geospatial Analysis\" (2014) and \"Introduction to Web Mapping\" (2020) and authored or co-authored 55 papers in the scientific literature.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 90, "guid": "5567248e-e9d2-5d86-a81e-c6138602f608", "logo": "", "date": "2023-08-28T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-90-introduction-to-working-with-spatial-data-in-python-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/PMARL3/", "title": "Introduction to working with spatial data in Python (part 2)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Python is an extremely popular general-purpose programming language. It is used in a wide range of settings and for various purposes, including for spatial data processing and analysis. \r\n\r\nThe aim of this tutorial is to give an introduction to methods of working with spatial data using Python. The tutorial will be split into two parts, introducing two central Python packages:\r\n\r\n* `geopandas`---For working with vector layers\r\n* `rasterio`---For working with rasters\r\n\r\nThe tutorial will demonstrate typical basic workflows of processing spatial data: data input, processing, geo-computation, and exporting of the results. We will use realistic datasets, such as GTFS public transport data and remote sensing products.\r\n\r\nBy the end of the tutorial, the participants will be able to:\r\n\r\n* Import spatial data from files\r\n* Subset and process the data\r\n* Graphically display the data\r\n* Perform spatial calculations (such as calculating distances, or applying raster algebra operators)\r\n* Export the results\r\n\r\nTo follow along and reproduce the results on your own computer, the prerequisite is to be able to run Python code in a Jupyter Notebook interface, linked to a Python environment with the two above-mentioned packages installed. Instructions will be sent in advance.", "recording_license": "", "do_not_record": false, "persons": [{"id": 107, "code": "FJ7JQE", "public_name": "Michael Dorman", "biography": "Michael Dorman is a programmer (since 2016) and lecturer (since 2013) at the Department of Geography and Environmental Development, Ben-Gurion University of the Negev. He is working with researchers and students of the Department in developing computational workflows such as data processing, spatial analysis, geostatistics, development of web applications, and web maps, etc., mostly through programming in R, Python, and JavaScript. Michael holds a Ph.D. in Geography and a M.Sc. in Life Sciences from the Ben-Gurion University of the Negev, and a B.Sc. in Plant Sciences in Agriculture from The Hebrew University of Jerusalem. He published two books, \"Learning R for Geospatial Analysis\" (2014) and \"Introduction to Web Mapping\" (2020) and authored or co-authored 55 papers in the scientific literature.", "answers": []}], "links": [], "attachments": [], "answers": []}]}}, {"index": 3, "date": "2023-08-29", "day_start": "2023-08-29T04:00:00+02:00", "day_end": "2023-08-30T03:59:00+02:00", "rooms": {"Room 21 (Sala 21)": [{"id": 84, "guid": "d4b069ac-f53a-5bc1-94f3-1935af493e9c", "logo": "/media/opengeohub-summer-school-2023/submissions/HBWCDX/Screenshot_from_2023-06-16_12-11-07_A2Z2ozq.png", "date": "2023-08-29T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-84-raster-and-vector-data-cubes-in-r-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/HBWCDX/", "title": "Raster and vector data cubes in R (part 1)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "A common challenge with raster datasets is not only that they come in large files (single Sentinel-2 tiles are around 1 GB), but that many of these files, potentially thousands or millions, are needed to address the area and time period of interest. In 2022, Copernicus, the program that runs all Sentinel satellites, published 160 TB of images per day. This means that a classic pattern in using R consisting of downloading data to local disc, loading the data in memory, and analysing it is not going to work. This lectures describes how large spatial and spatiotemporal datasets can be handled with R, with a focus on packages **sf** and **stars**. For practical use, we classify large datasets as too large:\r\n- to fit in working memory,\r\n- to fit on the local hard drive, or\r\n- to download to locally managed infrastructure (such as network attached storage)\r\nThese three categories may (today) correspond very roughly to Gigabyte-, Terabyte- and Petabyte-sized datasets. Besides size considerations, access and processing speed also play a role, in particular for larger datasets or interactive applications. Cloud native geospatial formats are formats optimised with processing on cloud infrastructure in mind, where costs of computing and storage need to be considered and optimised.", "recording_license": "", "do_not_record": false, "persons": [{"id": 129, "code": "YJSWUN", "public_name": "Edzer Pebesma", "biography": "Affiliation: University of M\u00fcnster\r\nResearch interests: Spatial Statistics, Geoinformatics, Spatial Data Science, Reproducible Research, R\r\nAbout: I lead the spatio-temporal modelling laboratory at the institute for geoinformatics, and am currently head of institute.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 93, "guid": "627cc63f-ee55-5271-aa2b-1ef29b237131", "logo": "", "date": "2023-08-29T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-93-raster-and-vector-data-cubes-in-r-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/YZHQQS/", "title": "Raster and vector data cubes in R (part 2)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "A common challenge with raster datasets is not only that they come in large files (single Sentinel-2 tiles are around 1 GB), but that many of these files, potentially thousands or millions, are needed to address the area and time period of interest. In 2022, Copernicus, the program that runs all Sentinel satellites, published 160 TB of images per day. This means that a classic pattern in using R consisting of downloading data to local disc, loading the data in memory, and analysing it is not going to work. This lectures describes how large spatial and spatiotemporal datasets can be handled with R, with a focus on packages **sf** and **stars**. For practical use, we classify large datasets as too large:\r\n- to fit in working memory,\r\n- to fit on the local hard drive, or\r\n- to download to locally managed infrastructure (such as network attached storage)\r\nThese three categories may (today) correspond very roughly to Gigabyte-, Terabyte- and Petabyte-sized datasets. Besides size considerations, access and processing speed also play a role, in particular for larger datasets or interactive applications. Cloud native geospatial formats are formats optimised with processing on cloud infrastructure in mind, where costs of computing and storage need to be considered and optimised.", "recording_license": "", "do_not_record": false, "persons": [{"id": 129, "code": "YJSWUN", "public_name": "Edzer Pebesma", "biography": "Affiliation: University of M\u00fcnster\r\nResearch interests: Spatial Statistics, Geoinformatics, Spatial Data Science, Reproducible Research, R\r\nAbout: I lead the spatio-temporal modelling laboratory at the institute for geoinformatics, and am currently head of institute.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 65, "guid": "15609da1-f681-56ba-b64a-77acc0ca055f", "logo": "", "date": "2023-08-29T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-65-unsupervised-classification-clustering-of-satellite-images-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/DDABRT/", "title": "Unsupervised classification (clustering) of satellite images (part 1)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Unsupervised classification of satellite images is the process of grouping similar pixels on an image into homogeneous clusters based primarily on their spectral characteristics. This approach does not require reference (labeled) data, unlike supervised classification, therefore it can be used as a method of first choice. Satellite image classification is commonly used in a variety of fields, including environmental monitoring, land cover mapping, and disaster management. The generated thematic maps can be used to identify and monitor changes in land use, and assess the impact of natural disasters.\r\n\r\nDuring this workshop, participants will gain practical knowledge and skills to perform unsupervised classification of Landsat data using the R language. It will be demonstrated step by step how to use and prepare raster data for analysis, popular grouping methods will be discussed and finally we will prepare a land cover map with interpretation of the results. The workshop will also cover the challenges and limitations of unsupervised classification, such as subjective interpretation of results difficulty of selecting the optimal number of clusters, and validation methods for ensuring the accuracy and reliability of results.\r\n\r\nThe workshop is aimed at beginners, but basic knowledge of GIS and satellite remote sensing is required.", "recording_license": "", "do_not_record": false, "persons": [{"id": 113, "code": "FKXFNK", "public_name": "Deleted User", "biography": "I am a PhD student in the field of Earth and environmental sciences at the Adam Mickiewicz University in Pozna\u0144. I am interested in spatial data analysis, remote sensing and programming in R, particularly in agriculture, and I was involved in several R&D projects related to crop classification, yield prediction, and soil mapping. I also contributed to the development of R-spatial packages.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 95, "guid": "9032ea22-c092-5f67-8039-68aeed10576d", "logo": "", "date": "2023-08-29T15:30:00+02:00", "start": "15:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-95-unsupervised-classification-clustering-of-satellite-images-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/WN9LGG/", "title": "Unsupervised classification (clustering) of satellite images (part 2)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Unsupervised classification of satellite images is the process of grouping similar pixels on an image into homogeneous clusters based primarily on their spectral characteristics. This approach does not require reference (labeled) data, unlike supervised classification, therefore it can be used as a method of first choice. Satellite image classification is commonly used in a variety of fields, including environmental monitoring, land cover mapping, and disaster management. The generated thematic maps can be used to identify and monitor changes in land use, and assess the impact of natural disasters.\r\n\r\nDuring this workshop, participants will gain practical knowledge and skills to perform unsupervised classification of Landsat data using the R language. It will be demonstrated step by step how to use and prepare raster data for analysis, popular grouping methods will be discussed and finally we will prepare a land cover map with interpretation of the results. The workshop will also cover the challenges and limitations of unsupervised classification, such as subjective interpretation of results difficulty of selecting the optimal number of clusters, and validation methods for ensuring the accuracy and reliability of results.\r\n\r\nThe workshop is aimed at beginners, but basic knowledge of GIS and satellite remote sensing is required.", "recording_license": "", "do_not_record": false, "persons": [{"id": 113, "code": "FKXFNK", "public_name": "Deleted User", "biography": "I am a PhD student in the field of Earth and environmental sciences at the Adam Mickiewicz University in Pozna\u0144. I am interested in spatial data analysis, remote sensing and programming in R, particularly in agriculture, and I was involved in several R&D projects related to crop classification, yield prediction, and soil mapping. I also contributed to the development of R-spatial packages.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 76, "guid": "2ea69b08-dd74-52b2-a879-28a97c5ee47c", "logo": "", "date": "2023-08-29T17:00:00+02:00", "start": "17:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-76-hackathons-consultations-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/JEV9XU/", "title": "Hackathons (consultations)", "subtitle": "", "track": "Hackathons", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Spatial data processing is an essential part of modern data analysis. In the course of the summer school, you will learn many new tools and techniques to process spatial data. However, you really only know something if you implement it in a concrete situation. \r\n\r\nFor this reason, we are conducting a hackathon during the summer school. You can choose between two topics and apply all the state of the art skills you are learning during the week. \r\n\r\nTo help you better understand the question and relevant domain, we will present the topics in an introductory workshop which will help get you started with the task and the submission process. \r\n\r\nBy participating, you will learn how spatial data analysis can be applied to solve real-world problems. You will gain practical skills in using the tools and techniques to analyse, interpret and model spatial data, and to use this knowledge in your respective field.", "recording_license": "", "do_not_record": false, "persons": [{"id": 113, "code": "FKXFNK", "public_name": "Deleted User", "biography": "I am a PhD student in the field of Earth and environmental sciences at the Adam Mickiewicz University in Pozna\u0144. I am interested in spatial data analysis, remote sensing and programming in R, particularly in agriculture, and I was involved in several R&D projects related to crop classification, yield prediction, and soil mapping. I also contributed to the development of R-spatial packages.", "answers": []}, {"id": 120, "code": "AQXTYJ", "public_name": "Nils Ratnaweera", "biography": "I\u2019m a freelance data scientist (see [ratnaweera.xyz/](https://www.ratnaweera.xyz/)) and researcher at the Zurich University of Applied Sciences ([ZHAW](https://www.zhaw.ch/en/about-us/person/rata/)) . I enjoy using different programming languages to solve complex, real world problems and answer interesting questions. My tools of choice include R, python, gdal, ogr2ogr, PostgresSQL, PostGIS and more.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Room 18 (Sala 18)": [{"id": 86, "guid": "a71c6aa5-4db3-5a9c-bbb4-b1ac7df65c72", "logo": "/media/opengeohub-summer-school-2023/submissions/N338TW/Screenshot_from_2023-06-16_12-22-26_BukZ115.png", "date": "2023-08-29T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-86-processing-geospatial-data-using-juliageo-framework-julia-tutorial-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/N338TW/", "title": "Processing geospatial data using JuliaGeo framework (Julia tutorial) (part 1)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Julia is a programming language that is simple to write and scriptable like Python and R, but fast like C or C++. At 10 years, it\u2019s a young language, so the ecosystem isn\u2019t as large and mature as you want it to be. Maarten Pronk was an early adopter of the language in his research at Deltares, a Dutch research institute. In this lecture(s) he will introduce Julia, his motivation to use it and his OSS journey. Half of the lecture will be non-spatial, while the latter half will focus on the JuliaGeo ecosystem and showcased some of the possibilities of the Julia language.\r\nThe JuliaGeo GitHub organization is intended primarily for the collaborative development of packages that are generally applicable across the geospatial and geosciences domains. For dealing with geospatial data, packages from the JuliaGeometry and JuliaImages organizations may also be of interest, and we will aim for good integration with those. Since the JuliaGeo organization aims to provide mostly general tools, more domain specific packages may be better suited for development in domain specific organizations. JuliaClimate is a nice example of such an organization that will be especially interesting to climate, atmosphere and ocean scientists. EcoJulia also provides some tools for generating and downloading spatial data sets, with a focus on ecological applications.", "recording_license": "", "do_not_record": false, "persons": [{"id": 130, "code": "VV3KTG", "public_name": "Maarten Pronk", "biography": "GeoData Scientist at Deltares", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 92, "guid": "6b1ccd55-04fa-512a-a95c-efa98d1bf173", "logo": "", "date": "2023-08-29T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-92-processing-geospatial-data-using-juliageo-framework-julia-tutorial-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/H8PAZC/", "title": "Processing geospatial data using JuliaGeo framework (Julia tutorial) (part 2)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Julia is a programming language that is simple to write and scriptable like Python and R, but fast like C or C++. At 10 years, it\u2019s a young language, so the ecosystem isn\u2019t as large and mature as you want it to be. Maarten Pronk was an early adopter of the language in his research at Deltares, a Dutch research institute. In this lecture(s) he will introduce Julia, his motivation to use it and his OSS journey. Half of the lecture will be non-spatial, while the latter half will focus on the JuliaGeo ecosystem and showcased some of the possibilities of the Julia language.\r\nThe JuliaGeo GitHub organization is intended primarily for the collaborative development of packages that are generally applicable across the geospatial and geosciences domains. For dealing with geospatial data, packages from the JuliaGeometry and JuliaImages organizations may also be of interest, and we will aim for good integration with those. Since the JuliaGeo organization aims to provide mostly general tools, more domain specific packages may be better suited for development in domain specific organizations. JuliaClimate is a nice example of such an organization that will be especially interesting to climate, atmosphere and ocean scientists. EcoJulia also provides some tools for generating and downloading spatial data sets, with a focus on ecological applications.", "recording_license": "", "do_not_record": false, "persons": [{"id": 130, "code": "VV3KTG", "public_name": "Maarten Pronk", "biography": "GeoData Scientist at Deltares", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 106, "guid": "fe34a588-3844-51f4-b401-4aaec44f9078", "logo": "", "date": "2023-08-29T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-106-progress-in-modernizing-and-replacing-infrastructure-packages-in-r-spatial-workflows", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/FZNK8J/", "title": "Progress in modernizing and replacing infrastructure packages in R-spatial workflows", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Until June 2023, maintainers of legacy packages using rgdal, rgeos and/or maptools were encouraged to migrate to sf/stars or terra, as described in https://github.com/r-spatial/evolution and blogs listed there: https://r-spatial.org/r/2022/04/12/evolution.html, https://r-spatial.org/r/2022/12/14/evolution2.html, https://r-spatial.org/r/2023/04/10/evolution3.html.\r\n\r\nIn June 2023, sp switches from using rgdal by default for sp::CRS and sp::spTransform to using sf functionality by default. \r\n\r\nFrom October 2023, retiring packages rgdal, rgeos and maptools will be archived on CRAN. This means that residual installations of retiring packages will continue for R 4.2 and R 4.3, but will not be updated after October 2023, nor will they be available for R 4.4. \r\n\r\nThe workshop will present current status, and may assist participants with affected workflows to adapt; the same applies to key affected packages needed in participants' workflows. \r\n\r\nParticipants are invited to contact the presenter with practical ideas to packages to adapt, and time may also be used to prepare non-maintainer update candidates for non-responsive packages. The count of affected packages was over 800, but the severity of the impact of the withdrawal of the retiring packages varies by the dependency category, strong dependence as Depends or Imports, weak dependence as Suggests.", "recording_license": "", "do_not_record": false, "persons": [{"id": 105, "code": "JGHHB9", "public_name": "Roger Bivand", "biography": "Roger Bivand is a geographer, emeritus professor at the Department of Economics of the Norwegian School of Economics, Bergen, Norway. His specialties are Geographical Information Analysis, Statistical programming and Spatial econometrics. Roger is author of numerous R packages and was the main author of the Applied Spatial Data Analysis with R book. He is an Ordinary Member of the R Foundation. He has worked with spatial autocorrelation since the 1970\u2019s, and is a Fellow of the Spatial Econometrics Association.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 63, "guid": "2a5569b4-a5e2-5cc6-b058-1e9a2801b8fe", "logo": "", "date": "2023-08-29T15:30:00+02:00", "start": "15:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-63-progress-in-modernizing-and-replacing-infrastructure-packages-in-r-spatial-workflows", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/TATSS7/", "title": "Progress in modernizing and replacing infrastructure packages in R-spatial workflows", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Until June 2023, maintainers of legacy packages using rgdal, rgeos and/or maptools were encouraged to migrate to sf/stars or terra, as described in https://github.com/r-spatial/evolution and blogs listed there: https://r-spatial.org/r/2022/04/12/evolution.html, https://r-spatial.org/r/2022/12/14/evolution2.html, https://r-spatial.org/r/2023/04/10/evolution3.html.\r\n\r\nIn June 2023, sp switches from using rgdal by default for sp::CRS and sp::spTransform to using sf functionality by default. \r\n\r\nFrom October 2023, retiring packages rgdal, rgeos and maptools will be archived on CRAN. This means that residual installations of retiring packages will continue for R 4.2 and R 4.3, but will not be updated after October 2023, nor will they be available for R 4.4. \r\n\r\nThe workshop will present current status, and may assist participants with affected workflows to adapt; the same applies to key affected packages needed in participants' workflows. \r\n\r\nParticipants are invited to contact the presenter with practical ideas to packages to adapt, and time may also be used to prepare non-maintainer update candidates for non-responsive packages. The count of affected packages was over 800, but the severity of the impact of the withdrawal of the retiring packages varies by the dependency category, strong dependence as Depends or Imports, weak dependence as Suggests.", "recording_license": "", "do_not_record": false, "persons": [{"id": 105, "code": "JGHHB9", "public_name": "Roger Bivand", "biography": "Roger Bivand is a geographer, emeritus professor at the Department of Economics of the Norwegian School of Economics, Bergen, Norway. His specialties are Geographical Information Analysis, Statistical programming and Spatial econometrics. Roger is author of numerous R packages and was the main author of the Applied Spatial Data Analysis with R book. He is an Ordinary Member of the R Foundation. He has worked with spatial autocorrelation since the 1970\u2019s, and is a Fellow of the Spatial Econometrics Association.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Other locations": [{"id": 111, "guid": "a0f91200-ad94-5ea3-8cce-849022a9cbc2", "logo": "", "date": "2023-08-29T17:15:00+02:00", "start": "17:15", "duration": "02:30", "room": "Other locations", "slug": "opengeohub-summer-school-2023-111-short-excursion", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/NEVMZ3/", "title": "Short excursion", "subtitle": "", "track": null, "type": "Short excursion", "language": "en", "abstract": null, "description": "The event includes seeing a large excavated meteorite in the in-place museum and then a 2-hour trip to the meteorite craters in the Morasko Preserve.", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}]}}, {"index": 4, "date": "2023-08-30", "day_start": "2023-08-30T04:00:00+02:00", "day_end": "2023-08-31T03:59:00+02:00", "rooms": {"Room 21 (Sala 21)": [{"id": 85, "guid": "6dab93f3-2399-5d7b-85ee-daf5411828fb", "logo": "/media/opengeohub-summer-school-2023/submissions/XCVDSS/Screenshot_from_2023-06-16_12-18-30_IyRuTo0.png", "date": "2023-08-30T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-85-cloud-based-analysis-of-earth-observation-data-using-openeo-platform-r-and-python-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/XCVDSS/", "title": "Cloud-based analysis of Earth Observation data using openEO Platform, R and Python (part 1)", "subtitle": "", "track": "Cross-platform", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "openEO Platform holds a large amount of free and open as well as commercial Earth Observation (EO) data which can be accessed and analysed with openEO, an open API that enables cloud computing and EO data access in a unified and reproducible way. Additionally, client libraries are available in R, Python and Javascript. A JupterLab environment and the Web Editor, a graphical interface, allow a direct and interactive development of processing workflows. The platform is developed with a strong user focus and various use cases have been implemented to illustrate the platform capabilities. Currently, three federated backends support the analysis of EO data from pixel to continental scale.\r\nThe future evolution of openEO Platform in terms of data availability and processing capabilities closely linked to community requirements, facilitated by feature requests from users who design their workflows for environmental monitoring and reproducible research purposes. This presentation provides an overview of the completed use cases, the newly added functionalities such as user code sharing, and user interface updates based on the new use cases and user requests. openEO Platform exemplifies how the processing and analysing large amounts of EO data to meaningful information products is becoming easier and largely compliant with FAIR data principles supporting the EO community at large.", "recording_license": "", "do_not_record": false, "persons": [{"id": 129, "code": "YJSWUN", "public_name": "Edzer Pebesma", "biography": "Affiliation: University of M\u00fcnster\r\nResearch interests: Spatial Statistics, Geoinformatics, Spatial Data Science, Reproducible Research, R\r\nAbout: I lead the spatio-temporal modelling laboratory at the institute for geoinformatics, and am currently head of institute.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 98, "guid": "6e8c2cbc-b034-5a7f-8ed9-2855cd2d3a2d", "logo": "", "date": "2023-08-30T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-98-cloud-based-analysis-of-earth-observation-data-using-openeo-platform-r-and-python-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/A8UFQA/", "title": "Cloud-based analysis of Earth Observation data using openEO Platform, R and Python (part 2)", "subtitle": "", "track": "Cross-platform", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "openEO Platform holds a large amount of free and open as well as commercial Earth Observation (EO) data which can be accessed and analysed with openEO, an open API that enables cloud computing and EO data access in a unified and reproducible way. Additionally, client libraries are available in R, Python and Javascript. A JupterLab environment and the Web Editor, a graphical interface, allow a direct and interactive development of processing workflows. The platform is developed with a strong user focus and various use cases have been implemented to illustrate the platform capabilities. Currently, three federated backends support the analysis of EO data from pixel to continental scale.", "recording_license": "", "do_not_record": false, "persons": [{"id": 129, "code": "YJSWUN", "public_name": "Edzer Pebesma", "biography": "Affiliation: University of M\u00fcnster\r\nResearch interests: Spatial Statistics, Geoinformatics, Spatial Data Science, Reproducible Research, R\r\nAbout: I lead the spatio-temporal modelling laboratory at the institute for geoinformatics, and am currently head of institute.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 77, "guid": "6336e305-169d-5e8c-a8f6-a771658785b6", "logo": "", "date": "2023-08-30T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-77-environmental-analysis-using-satellite-image-time-series-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/7BDZSY/", "title": "Environmental analysis using satellite image time series (part 1)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Satellite imagery time series offer a powerful means to detect and analyze both short- and long-term changes in the environment. In particular, the availability of open-access data from missions like Landsat (since 1972) and Sentinel (since 2015) has significantly enhanced our ability to study these changes. This workshop aims to explore the use of time series of indices derived from satellite imagery for analyzing various types of land cover changes using the programming language R. The workshop will cover essential preprocessing steps, including outlier removal and handling missing observations, to ensure the quality of the data. Participants will learn how to effectively model time series using different methods. Additionally, the workshop will provide insights into detecting trends and breaks within the time series data. The analysis will focus on a range of objects and encompass both abrupt and gradual changes. Examples of the types of changes that will be explored include urban growth or vegetation succession.\r\n\r\nThe repository fot this workshop: https://github.com/egrabska/OGH2023 \r\nAnd the instructions for this workshop are here: https://egrabska.github.io/OGH2023/\r\n\r\nHowever, this repository may be also useful for dealing with other time series, not only satellite imagery!", "recording_license": "", "do_not_record": false, "persons": [{"id": 121, "code": "GLEAP3", "public_name": "Ewa Grabska-Szwagrzyk", "biography": "I am a geographer with a PhD specializing in remote sensing analysis of forests & R enthusiast.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 96, "guid": "c2970ef7-f93c-516b-9cd1-9aa6e54b8606", "logo": "", "date": "2023-08-30T15:30:00+02:00", "start": "15:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-96-environmental-analysis-using-satellite-image-time-series-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/PES8RJ/", "title": "Environmental analysis using satellite image time series (part 2)", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Satellite imagery time series offer a powerful means to detect and analyze both short- and long-term changes in the environment. In particular, the availability of open-access data from missions like Landsat (since 1972) and Sentinel (since 2015) has significantly enhanced our ability to study these changes. This workshop aims to explore the use of time series of indices derived from satellite imagery for analyzing various types of land cover changes using the programming language R. The workshop will cover essential preprocessing steps, including outlier removal and handling missing observations, to ensure the quality of the data. Participants will learn how to effectively model time series using different methods. Additionally, the workshop will provide insights into detecting trends and breaks within the time series data. The analysis will focus on a range of objects and encompass both abrupt and gradual changes. Examples of the types of changes that will be explored include urban growth or vegetation succession.\r\n\r\nThe repository fot this workshop: https://github.com/egrabska/OGH2023 \r\nAnd the instructions for this workshop are here: https://egrabska.github.io/OGH2023/\r\n\r\nHowever, this repository may be also useful for dealing with other time series, not only satellite imagery!", "recording_license": "", "do_not_record": false, "persons": [{"id": 121, "code": "GLEAP3", "public_name": "Ewa Grabska-Szwagrzyk", "biography": "I am a geographer with a PhD specializing in remote sensing analysis of forests & R enthusiast.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 103, "guid": "2664d502-6045-5b27-bf45-b81274e19f61", "logo": "", "date": "2023-08-30T17:15:00+02:00", "start": "17:15", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-103-discussion-panel-what-can-r-python-and-julia-development-communities-do-to-combat-the-climate-crisis-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/ZQWFQQ/", "title": "Discussion panel: What can R, Python, and Julia development communities do to combat the climate crisis?", "subtitle": "", "track": "Theoretical sessions", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "What can R, Python, and Julia development communities do to combat the climate crisis. Let\u2019s explore solutions, together with leading experts in each programming language. Join this interactive discussion, ask us your questions, and answer ours online!\r\n\r\nDuring this year\u2019s discussion forum, we will have the pleasure to listen to expert presentations by\r\n\r\n    Anita Graser, Spatial data scientist, Austrian Institute of Technology in Vienna\r\n    Edzer Pebesma, Director of the Institute for Geoinformatics, University of M\u00fcnster\r\n    Maarten Pronk, GeoData Scientist, Deltares\r\n    Lorena Abad, Researcher at Z_GIS \u2013 Department of Geoinformatics, University of Salzburg\r\n    Tomislav Hengl, Data Scientist and Technical Director of OpenGeoHub Foundation", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}], "Room 18 (Sala 18)": [{"id": 66, "guid": "d67bd1ac-349d-54ad-8857-b65864dbfb46", "logo": "/media/opengeohub-summer-school-2023/submissions/8QXTGE/benchmark_small_p15oZVF.png", "date": "2023-08-30T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-66-parallelization-of-geoprocessing-workflows-in-grass-gis-and-python-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/8QXTGE/", "title": "Parallelization of geoprocessing workflows in GRASS GIS and Python (part 1)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "High-resolution, continental-scale modeling enabled by modern, massive datasets, requires development of scalable geoprocessing workflows. To enable participants to effectively use available computational resources (laptop, desktop, institutional HPC), we will introduce basic parallelization concepts such as parallelization efficiency and scaling. We will explain various approaches to parallelization in GRASS GIS, an open source geoprocessing engine, that rely on OpenMP, Python and Bash.\r\nIn the hands-on part, participants will speed up an urban growth model by parallelizing different parts of this complex geoprocessing workflow using techniques that are easily applicable to a wide range of analyses and computational resources. The workshop will be running in a Jupyter Notebook environment using GRASS GIS Python API to run GRASS tools and visualize results of the analysis in a reproducible way.\r\nParticipants will be able to either run the workshop on their laptops (see instructions) or in a cloud environment (using WholeTale, no installation required).", "recording_license": "", "do_not_record": false, "persons": [{"id": 114, "code": "Z3YQTK", "public_name": "Caitlin Haedrich", "biography": "Caitlin is a 3rd year doctoral student in the GeoForAll Lab at North Carolina State University in Raleigh, NC, USA. She has been working on improving the integration of GRASS GIS and Jupyter Notebooks.", "answers": []}, {"id": 115, "code": "9JAX9L", "public_name": "Anna Petrasova", "biography": "Anna is a geospatial research software engineer with PhD in Geospatial Analytics. She develops spatio-temporal models of urbanization and pest spread across landscape. As a member of the OSGeo Foundation and the GRASS GIS Project Steering Committee, Anna advocates the use of open source software in research and education.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 97, "guid": "a0111707-1011-5450-a06d-0517f08354f6", "logo": "", "date": "2023-08-30T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-97-parallelization-of-geoprocessing-workflows-in-grass-gis-and-python-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/3RARUC/", "title": "Parallelization of geoprocessing workflows in GRASS GIS and Python (part 2)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "High-resolution, continental-scale modeling enabled by modern, massive datasets, requires development of scalable geoprocessing workflows. To enable participants to effectively use available computational resources (laptop, desktop, institutional HPC), we will introduce basic parallelization concepts such as parallelization efficiency and scaling. We will explain various approaches to parallelization in GRASS GIS, an open source geoprocessing engine, that rely on OpenMP, Python and Bash.\r\nIn the hands-on part, participants will speed up an urban growth model by parallelizing different parts of this complex geoprocessing workflow using techniques that are easily applicable to a wide range of analyses and computational resources. The workshop will be running in a Jupyter Notebook environment using GRASS GIS Python API to run GRASS tools and visualize results of the analysis in a reproducible way.\r\nParticipants will be able to either run the workshop on their laptops (see instructions) or in a cloud environment (using WholeTale, no installation required).", "recording_license": "", "do_not_record": false, "persons": [{"id": 114, "code": "Z3YQTK", "public_name": "Caitlin Haedrich", "biography": "Caitlin is a 3rd year doctoral student in the GeoForAll Lab at North Carolina State University in Raleigh, NC, USA. She has been working on improving the integration of GRASS GIS and Jupyter Notebooks.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 81, "guid": "32e5efff-054a-5ffb-bb01-1652d40314f6", "logo": "", "date": "2023-08-30T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-81-xcube-for-spatiotemporal-data-analysis-and-visualization-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/HFXFCB/", "title": "xcube for spatiotemporal data analysis and visualization (part 1)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "xcube is an open-source xarray-based Python package and toolkit that has been developed to provide Earth observation (EO) data in an analysis-ready form to users. xcube achieves this by carefully converting EO data sources into self-contained data cubes that can be published in the cloud.   \r\nIn this session you will learn about the ecosystem around xcube, which allows to access different data sources and turning the inputs into data cubes. These data cubes can then be easily used for spatiotemporal data analysis and visualization. After a brief introduction about the software components, we will go step by step though some example Jupyter notebooks and finally we will dive into a hands-on session with a little challenge.   \r\nFor the session you will need a laptop with an internet connection, some basic knowledge about Python and already installed miniconda (https://docs.conda.io/en/latest/miniconda.html) which is used to download the necessary Python packages for the session. Prior experience with Jupyter notebooks will be helpful, but not mandatory.", "recording_license": "", "do_not_record": false, "persons": [{"id": 123, "code": "TWM8YZ", "public_name": "Alicja Balfanz", "biography": "I am part of the Environmental Informatics Team at Brockmann Consult GmbH. Brockmann Consult GmbH develops software for the exploitation of environmental data from Earth Observation and other sources, provides information and consultancy services to businesses, public institutions, and national as well as intergovernmental agencies.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 102, "guid": "85934109-c8fc-5b9a-868a-5aa40fa81be6", "logo": "", "date": "2023-08-30T15:30:00+02:00", "start": "15:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-102-xcube-for-spatiotemporal-data-analysis-and-visualization-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/37CK9C/", "title": "xcube for spatiotemporal data analysis and visualization (part 2)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "xcube is an open-source xarray-based Python package and toolkit that has been developed to provide Earth observation (EO) data in an analysis-ready form to users. xcube achieves this by carefully converting EO data sources into self-contained data cubes that can be published in the cloud.   \r\nIn this session you will learn about the ecosystem around xcube, which allows to access different data sources and turning the inputs into data cubes. These data cubes can then be easily used for spatiotemporal data analysis and visualization. After a brief introduction about the software components, we will go step by step though some example Jupyter notebooks and finally we will dive into a hands-on session with a little challenge.   \r\nFor the session you will need a laptop with an internet connection, some basic knowledge about Python and already installed miniconda (https://docs.conda.io/en/latest/miniconda.html) which is used to download the necessary Python packages for the session. Prior experience with Jupyter notebooks will be helpful, but not mandatory.", "recording_license": "", "do_not_record": false, "persons": [{"id": 123, "code": "TWM8YZ", "public_name": "Alicja Balfanz", "biography": "I am part of the Environmental Informatics Team at Brockmann Consult GmbH. Brockmann Consult GmbH develops software for the exploitation of environmental data from Earth Observation and other sources, provides information and consultancy services to businesses, public institutions, and national as well as intergovernmental agencies.", "answers": []}], "links": [], "attachments": [], "answers": []}]}}, {"index": 5, "date": "2023-08-31", "day_start": "2023-08-31T04:00:00+02:00", "day_end": "2023-09-01T03:59:00+02:00", "rooms": {"Room 21 (Sala 21)": [{"id": 87, "guid": "7d9f1c07-8219-516a-aac5-0952489654ac", "logo": "/media/opengeohub-summer-school-2023/submissions/RFXDZW/Screenshot_from_2023-06-16_12-25-52_68zOoRA.png", "date": "2023-08-31T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-87-spatial-ml-model-assessment-and-interpretation-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/RFXDZW/", "title": "Spatial ML model assessment and interpretation (part 1)", "subtitle": "", "track": "Theoretical sessions", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "While significant progress has been made towards explaining black-box machine-learning (ML) models, there is still a distinct lack of diagnostic tools that elucidate the spatial behaviour of ML models in terms of predictive skill and variable importance. This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools for spatial prediction models with a focus on prediction distance. Their suitability is demonstrated in two case studies representing a regionalization task in an environmental-science context, and a classification task from remotely-sensed land cover classification. In these case studies, the SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences but also relevant similarities. Limitations of related cross-validation techniques are outlined, and the case is made that modelers should focus their model assessment and interpretation on the intended spatial prediction horizon. The range of autocorrelation, in contrast, is not a suitable criterion for defining spatial cross-validation test sets. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.", "recording_license": "", "do_not_record": false, "persons": [{"id": 131, "code": "7NMUSE", "public_name": "Alexander Brenning", "biography": "I\u2019m a full professor of geographic information science in the Department of Geography of Friedrich Schiller University Jena, Germany. My research interests include geospatial modeling of Earth surface processes, machine learning, natural hazards, and remote-sensing data analysis. I enjoy contributing to the open-source data analysis ecosystem of  with my geocomputing-related packages.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 100, "guid": "516686e9-3a4e-5a55-af3b-dc973ffdec3c", "logo": "", "date": "2023-08-31T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-100-spatial-ml-model-assessment-and-interpretation-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/GNBK8S/", "title": "Spatial ML model assessment and interpretation (part 2)", "subtitle": "", "track": "Theoretical sessions", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "While significant progress has been made towards explaining black-box machine-learning (ML) models, there is still a distinct lack of diagnostic tools that elucidate the spatial behaviour of ML models in terms of predictive skill and variable importance. This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools for spatial prediction models with a focus on prediction distance. Their suitability is demonstrated in two case studies representing a regionalization task in an environmental-science context, and a classification task from remotely-sensed land cover classification. In these case studies, the SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences but also relevant similarities. Limitations of related cross-validation techniques are outlined, and the case is made that modelers should focus their model assessment and interpretation on the intended spatial prediction horizon. The range of autocorrelation, in contrast, is not a suitable criterion for defining spatial cross-validation test sets. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.", "recording_license": "", "do_not_record": false, "persons": [{"id": 131, "code": "7NMUSE", "public_name": "Alexander Brenning", "biography": "I\u2019m a full professor of geographic information science in the Department of Geography of Friedrich Schiller University Jena, Germany. My research interests include geospatial modeling of Earth surface processes, machine learning, natural hazards, and remote-sensing data analysis. I enjoy contributing to the open-source data analysis ecosystem of  with my geocomputing-related packages.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 79, "guid": "f573c759-8e9b-520e-974b-3a7ffce51114", "logo": "", "date": "2023-08-31T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-79-tools-and-packages-to-query-and-process-sentinel-1-and-sentinel-2-data-with-r-and-python-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/HR3RWF/", "title": "Tools and packages to query and process Sentinel-1 and Sentinel-2 data with R and Python (part 1)", "subtitle": "", "track": "Cross-platform", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "This session focuses on the products from the ESA Copernicus program Sentinel-1 and Sentinel-2. These products can be freely accessed in several manners and through different portals. We will take a look at packages to query the data you need for your analyses using Python and R, switching between platforms when relevant. An introduction on how to process Sentinel data with both platforms will also be covered focusing on the particularities of the sensors.\r\n\r\nFor Sentinel-1 data access, we will use the [ASF Data Service](https://search.asf.alaska.edu/) to query data. There is no need for credentials for querying the data but if you want to try the downloading steps, you will need [Earthdata Login](https://urs.earthdata.nasa.gov/) credentials.", "recording_license": "", "do_not_record": false, "persons": [{"id": 106, "code": "WJPGC3", "public_name": "Lorena Abad", "biography": "I am a PhD candidate at the Geoinformatics Department of the University of Salzburg. I focus on bigEO and remote sensing analysis for landscape dynamics and geomorphological applications. I have worked with FOSS4G for my research focusing on Sentinel products. I really like biking and hiking in my spare time :)", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 119, "guid": "82ef7bf4-11d3-5ba0-8854-8c45b47f1c23", "logo": "", "date": "2023-08-31T15:15:00+02:00", "start": "15:15", "duration": "00:15", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-119-introduction-to-city-location-based-game-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/RYXWKQ/", "title": "Introduction to \"City, location-based, game\"", "subtitle": "", "track": null, "type": "Social event", "language": "en", "abstract": null, "description": "Michal will explain the rules and give you all the materials needed to participate. You will be divided into random groups of 5-6 people. The introduction will take 15 min. If you want to join and you haven't declared this - come and join us, we have one spare group just for you.", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}], "Room 18 (Sala 18)": [{"id": 70, "guid": "48e62b13-1f60-5dfc-b421-478815d5afd7", "logo": "", "date": "2023-08-31T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-70-mapping-explanation-python-toolchaing-for-spatial-interpretative-machine-learning-part-1-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/9NUVKY/", "title": "Mapping explanation - Python toolchaing for spatial interpretative machine learning (part 1)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "The course will present applications of interpretive machine learning methods to geospatial analysis. Interpretive machine learning is a new branch of machine learning that allows the decomposition of black box models. It allows complex, non-linear models to explain the criteria that lead to a result. In the case of geospatial data, it can be used to search for patterns of spatial explanatory factors. The course covers the entire toolchain from data preparation, model training, and the data transformation process, through data analysis and interpretation of the results, to spatial visualization. The toolchain includes tools such as the shap library, selected components of the scikit-learn, geopandas, and matplotlib packages. The course includes a theoretical introduction to interpretive machine learning and how it can be applied to geospatial data. The practical part is built around analysing the U.S. presidential election results Clinton vs. Trump. In the first step, explanatory variables are collected and transformed into `shapely numbers`. The data transformed in this way will determine the relevance of the explanatory variables and their actual impact on the election outcome in each county. The advantage of shapely numbers is that the variables are automatically weighted, allowing for efficient clustering. The shapely numbers and their clustering results reveal interesting spatial patterns in the electoral process.", "recording_license": "", "do_not_record": false, "persons": [{"id": 118, "code": "9KAXGS", "public_name": "Jarek Jasiewicz", "biography": "Graduate in geology from the Adam Mickiewicz University in Poznan (Ph.D 2000). Works on computer vision of geospatial data and geospatial machine learning, author of dozens of publications. Researcher and head of the Laboratory of Applied Geoinformatics.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 101, "guid": "d5a4bc36-e689-5768-b828-0f0728e7b202", "logo": "", "date": "2023-08-31T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-101-mapping-explanation-python-toolchaing-for-spatial-interpretative-machine-learning-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/KEWCDZ/", "title": "Mapping explanation - Python toolchaing for spatial interpretative machine learning (part 2)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "The course will present applications of interpretive machine learning methods to geospatial analysis. Interpretive machine learning is a new branch of machine learning that allows the decomposition of black box models. It allows complex, non-linear models to explain the criteria that lead to a result. In the case of geospatial data, it can be used to search for patterns of spatial explanatory factors. The course covers the entire toolchain from data preparation, model training, and the data transformation process, through data analysis and interpretation of the results, to spatial visualization. The toolchain includes tools such as the shap library, selected components of the scikit-learn, geopandas, and matplotlib packages. The course includes a theoretical introduction to interpretive machine learning and how it can be applied to geospatial data. The practical part is built around analysing the U.S. presidential election results Clinton vs. Trump. In the first step, explanatory variables are collected and transformed into `shapely numbers`. The data transformed in this way will determine the relevance of the explanatory variables and their actual impact on the election outcome in each county. The advantage of shapely numbers is that the variables are automatically weighted, allowing for efficient clustering. The shapely numbers and their clustering results reveal interesting spatial patterns in the electoral process.", "recording_license": "", "do_not_record": false, "persons": [{"id": 118, "code": "9KAXGS", "public_name": "Jarek Jasiewicz", "biography": "Graduate in geology from the Adam Mickiewicz University in Poznan (Ph.D 2000). Works on computer vision of geospatial data and geospatial machine learning, author of dozens of publications. Researcher and head of the Laboratory of Applied Geoinformatics.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 67, "guid": "38e4cef8-8625-5dba-955d-3c083f378985", "logo": "/media/opengeohub-summer-school-2023/submissions/YKZKSA/movingpandas-logo-250_gZUhPUi.png", "date": "2023-08-31T13:30:00+02:00", "start": "13:30", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-67-data-engineering-for-mobility-data-science-with-python-and-dvc-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/YKZKSA/", "title": "Data engineering for Mobility Data Science (with Python and DVC)", "subtitle": "", "track": "Python / Julia", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "This session introduces [MovingPandas](https://movingpandas.org) and [DVC](https://dvc.org) for Mobility Data Science. \r\n\r\nMovingPandas is a Python library for the analysis and visualization of movement data. It is built on top of GeoPandas and provides functions to analyze, manipulate and plot trajectories. To get a better idea of the type of analytics that MovingPandas supports, visit: https://movingpandas.org/examples\r\n\r\nDVC is a data version control (and machine learning experiment tracking) library. It follows a similar logic to source code version control systems (such as Git) and is typically used together with Git to keep track of data and experiments while Git keeps track of the source code. \r\n\r\nIn this session, we will use DVC to keep track of our movement data analytics workflow. Participants are expected to come prepared with a working MovingPandas & DVC Python environment. Basic previous experience with (Geo)Pandas and version control systems (i.e. how pull, commit, push works in Git) is expected.", "recording_license": "", "do_not_record": false, "persons": [{"id": 112, "code": "FMPLPZ", "public_name": "Anita Graser", "biography": "Anita Graser in an expert in spatial data science for mobility and transport applications. She graduated with an MSc in Information Technology specializing in Geomatics in 2010 and received her PhD in Applied Geoinformatics from the University of Salzburg in 2021. Since 2007, she works in applied research at the AIT, focusing on spatial analysis of transport and movement data. Furthermore, Anita currently teaches at UNIGIS Salzburg, serves on the project steering committees of the open-source projects QGIS and MobilityDB, and is the lead developer of the open-source software library MovingPandas. She is an internationally sought-after speaker, has published more than 40 scientific articles, and several books. In 2020, she has been awarded the international OSGeo Sol Katz award for her contributions to open-source geographic information systems as well as the national Futurezone Women in Tech Award.", "answers": []}], "links": [], "attachments": [], "answers": []}], "Other locations": [{"id": 115, "guid": "a8665bf5-b9bc-5153-ad7c-a98219784d07", "logo": "", "date": "2023-08-31T16:00:00+02:00", "start": "16:00", "duration": "02:00", "room": "Other locations", "slug": "opengeohub-summer-school-2023-115-city-location-based-game", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/7UQJMV/", "title": "City, location-based, game", "subtitle": "", "track": null, "type": "Social event", "language": "en", "abstract": null, "description": "A city (location-based) game after the regular summer school workshops on Thursday, August 31st. It will happen in the Poznan city center between 4:00 and 6:00 PM. We cannot share more details, but it will definitely be fun.", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}, {"id": 114, "guid": "4f700b64-642d-5b9a-a103-b7bcb54213d6", "logo": "", "date": "2023-08-31T18:00:00+02:00", "start": "18:00", "duration": "03:00", "room": "Other locations", "slug": "opengeohub-summer-school-2023-114-happy-hour", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/KB3U8T/", "title": "Happy hour", "subtitle": "", "track": null, "type": "Social event", "language": "en", "abstract": null, "description": "Drinks and light snacks in a pub in the city center", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}]}}, {"index": 6, "date": "2023-09-01", "day_start": "2023-09-01T04:00:00+02:00", "day_end": "2023-09-02T03:59:00+02:00", "rooms": {"Room 21 (Sala 21)": [{"id": 94, "guid": "b8e815ad-6394-5947-b7d7-d4a370f46b58", "logo": "", "date": "2023-09-01T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-94-tools-and-packages-to-query-and-process-sentinel-1-and-sentinel-2-data-with-r-and-python-part-2-", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/HPDNL9/", "title": "Tools and packages to query and process Sentinel-1 and Sentinel-2 data with R and Python (part 2)", "subtitle": "", "track": "Cross-platform", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "This session focuses on the products from the ESA Copernicus program Sentinel-1 and Sentinel-2. These products can be freely accessed in several manners and through different portals. We will take a look at packages to query the data you need for your analyses using Python and R, switching between platforms when relevant. An introduction on how to process Sentinel data with both platforms will also be covered focusing on the particularities of the sensors.\r\n\r\nFor Sentinel-1 data access, we will use the [ASF Data Service](https://search.asf.alaska.edu/) to query data. There is no need for credentials for querying the data but if you want to try the downloading steps, you will need [Earthdata Login](https://urs.earthdata.nasa.gov/) credentials.", "recording_license": "", "do_not_record": false, "persons": [{"id": 106, "code": "WJPGC3", "public_name": "Lorena Abad", "biography": "I am a PhD candidate at the Geoinformatics Department of the University of Salzburg. I focus on bigEO and remote sensing analysis for landscape dynamics and geomorphological applications. I have worked with FOSS4G for my research focusing on Sentinel products. I really like biking and hiking in my spare time :)", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 117, "guid": "173da570-43b4-515c-b776-1481e37046b2", "logo": "", "date": "2023-09-01T11:00:00+02:00", "start": "11:00", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-117-sharing-your-geospatial-knowledge-in-the-open", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/QVQZQC/", "title": "Sharing your geospatial knowledge in the open", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "It will be (mostly) improvised session showing how to create a new online book with geospatial content.", "recording_license": "", "do_not_record": false, "persons": [{"id": 103, "code": "CW9V8A", "public_name": "Jakub Nowosad", "biography": null, "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 108, "guid": "25fbe89e-e29f-57b4-b481-f0c999027152", "logo": "", "date": "2023-09-01T13:30:00+02:00", "start": "13:30", "duration": "01:00", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-108-oemc-hackathons-launch", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/ANP8DA/", "title": "OEMC Hackathons Launch", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "OEMC Hackathons Launch: Land Cover Mapping EU-LUCAS and Global-FAPAR by Leandro Parente and Julia Hackl\u00e4nder", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}, {"id": 118, "guid": "e8c21fd9-1133-55db-bbb7-ca5cf512e4b1", "logo": "", "date": "2023-09-01T14:30:00+02:00", "start": "14:30", "duration": "00:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-118-announcement-of-the-hackathons-winners", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/8F9CU3/", "title": "Announcement of the hackathons winners", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Announcement of the hackathons winners, prizes and photos", "recording_license": "", "do_not_record": false, "persons": [{"id": 113, "code": "FKXFNK", "public_name": "Deleted User", "biography": "I am a PhD student in the field of Earth and environmental sciences at the Adam Mickiewicz University in Pozna\u0144. I am interested in spatial data analysis, remote sensing and programming in R, particularly in agriculture, and I was involved in several R&D projects related to crop classification, yield prediction, and soil mapping. I also contributed to the development of R-spatial packages.", "answers": []}, {"id": 120, "code": "AQXTYJ", "public_name": "Nils Ratnaweera", "biography": "I\u2019m a freelance data scientist (see [ratnaweera.xyz/](https://www.ratnaweera.xyz/)) and researcher at the Zurich University of Applied Sciences ([ZHAW](https://www.zhaw.ch/en/about-us/person/rata/)) . I enjoy using different programming languages to solve complex, real world problems and answer interesting questions. My tools of choice include R, python, gdal, ogr2ogr, PostgresSQL, PostGIS and more.", "answers": []}], "links": [], "attachments": [], "answers": []}, {"id": 116, "guid": "979c44b2-9da6-5e04-926b-5b66d2e3738c", "logo": "", "date": "2023-09-01T15:30:00+02:00", "start": "15:30", "duration": "01:30", "room": "Room 21 (Sala 21)", "slug": "opengeohub-summer-school-2023-116-summer-school-final-session", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/EY88FF/", "title": "Summer school final session", "subtitle": "", "track": null, "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "Getting feedback, summarizing the summer school, and more", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}], "Room 18 (Sala 18)": [{"id": 99, "guid": "6536af13-0034-555d-82f9-afa3877d40bd", "logo": "", "date": "2023-09-01T09:00:00+02:00", "start": "09:00", "duration": "01:30", "room": "Room 18 (Sala 18)", "slug": "opengeohub-summer-school-2023-99-processing-large-openstreetmap-datasets-for-geocomputational-research", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/SRMZVJ/", "title": "Processing large OpenStreetMap datasets for geocomputational research", "subtitle": "", "track": "R training", "type": "Theoretical and training sessions", "language": "en", "abstract": null, "description": "OpenStreetMap (OSM) is a free and openly editable map of the world. Like\r\nWikipedia and unlike government or corperation maintained datasets, OSM\r\nis created and maintained by a community of volunteers, making it the\r\npremier decentralized and fastest evolving source of geographic vector\r\ndata focussed on features relevant to human activity (e.g. roads,\r\nbuildings, cafes) on planet Earth. Unlike Wikipedia, every data point in\r\nOSM has a geographic location and attributes must be structured as\r\nkey-value pairs. OSM is a rich source of data for geocomputational\r\nresearch, but the decentralized nature of the project and the sheer\r\nvolume of data. \u2018Planet.osm\u2019 now has more nodes than there are people on\r\nEarth, with more than 8 billion\r\n[nodes](https://wiki.openstreetmap.org/wiki/Node), and the rate of data\r\ncreation is increasing as the community grows, to [10 million\r\nusers](https://wiki.openstreetmap.org/wiki/Stats) in early 2023. The\r\nsize and rapid evolution of OSM are great strengths, democratising\r\ngeographic knowledge and ensuring resilience. However, these features\r\ncan make it difficult to work with OSM data.\r\n\r\nThis lecture will provide an introduction to working with OSM and will\r\ncover the following:\r\n\r\n- How and where to download OSM data\r\n- How to process small amounts of OSM data using the `osmdata` R package\r\n- How to process large OSM \u2018extracts\u2019 data with the `osmextract` R\r\n  package\r\n- Other command line tools for working with OSM data, including the\r\n  mature and widely used `osmium` tool, the `pyrosm` Python package and\r\n  the [`osm2streets`](https://github.com/a-b-street/osm2streets) web\r\n  application and Rust codebase\r\n\r\nFinally, the lecture will outline ideas for using OSM data. It will\r\nconclude with a call to action, inspiring the use of this rich resource\r\nto support policy objectives such as the fast and fair decarbonisation\r\nof the global economy as societies transition away from inefficient,\r\npolluting and costly fossil fuels.", "recording_license": "", "do_not_record": false, "persons": [{"id": 122, "code": "PFGJ3V", "public_name": "Robin Lovelace", "biography": "Robin Lovelace is Associate Professor of Transport Data Science at the Leeds Institute for Transport Studies (ITS) and Head of Data at the government agency Active Travel England. Robin specializes in geocomputation with a focus on developing geographic methods applied to modeling transport systems, active travel, and decarbonisation. Robin has experience not only researching but deploying transport models in inform sustainable policies and more effective use of transport investment, including as Lead Developer of the Propensity to Cycle Tool (see www.pct.bike), the basis of strategic cycle network plans nationwide. Robin has led numerous data science projects for organizations ranging from the Department for Transport to the World Bank. \r\n\r\nRobin is author of popular open source software packages including R packages stplanr, stats19 and abstr. He has authored three reproducible and open source textbooks, Microsimulation with R, Efficient R Programming, and Geocomputation with R.", "answers": []}], "links": [], "attachments": [], "answers": []}]}}, {"index": 7, "date": "2023-09-02", "day_start": "2023-09-02T04:00:00+02:00", "day_end": "2023-09-03T03:59:00+02:00", "rooms": {"Other locations": [{"id": 110, "guid": "cde2f07c-b1ba-5e76-9fa6-bbf3153b49a0", "logo": "", "date": "2023-09-02T10:00:00+02:00", "start": "10:00", "duration": "07:00", "room": "Other locations", "slug": "opengeohub-summer-school-2023-110-excursion", "url": "https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/ANJVSU/", "title": "Excursion", "subtitle": "", "track": null, "type": "Excursion", "language": "en", "abstract": null, "description": "Long, one-day excursion: it includes a tour of the most fascinating attractions of the Pozna\u0144 city center. We start at 10AM in front of the Pozna\u0144 Cathedral (https://goo.gl/maps/ZMrLkSuhAzQPBqVb8)", "recording_license": "", "do_not_record": false, "persons": [], "links": [], "attachments": [], "answers": []}]}}]}}}