2025-09-03, 13:30–15:00 (Europe/Amsterdam), HugoTECH
This tutorial will offer a practical, step-by-step guide on accessing and using Landsat ARCO data for space-time mapping. Participants will work with real data to generate 30-meter resolution maps of the Netherlands from 2000 to 2024. The session will cover the entire workflow, including data acquisition, preprocessing, spatial and temporal analysis, and visualization. All code will be based on the Scikit-map library.
The workshop is designed to be interactive, with participants using either Google Colab or Docker containers to follow along. Key topics will include retrieving Landsat ARCO data, handling large-scale remote sensing datasets, applying spatial and temporal filters, and analyzing changes in land cover over time. The focus will be on implementing a reproducible workflow that can be adapted for similar projects.
This session is intended for participants with some experience in remote sensing or geospatial analysis. Prior knowledge of Python and GIS tools will be useful but not strictly required. Throughout the session, there will be opportunities for discussion and troubleshooting. By the end of the workshop, participants will have a working pipeline for generating and interpreting time-series maps from Landsat ARCO data. All provided input data will also be openly available at global scale for large scale applications.
This tutorial will offer a practical, step-by-step guide on accessing and using Landsat ARCO data for space-time mapping. Participants will work with real data to generate 30-meter resolution maps of the Netherlands from 2000 to 2024. The session will cover the entire workflow, including data acquisition, preprocessing, spatial and temporal analysis, and visualization. All code will be based on the Scikit-map library.
The workshop is designed to be interactive, with participants using either Google Colab or Docker containers to follow along. Key topics will include retrieving Landsat ARCO data, handling large-scale remote sensing datasets, applying spatial and temporal filters, and analyzing changes in land cover over time. The focus will be on implementing a reproducible workflow that can be adapted for similar projects.
This session is intended for participants with some experience in remote sensing or geospatial analysis. Prior knowledge of Python and GIS tools will be useful but not strictly required. Throughout the session, there will be opportunities for discussion and troubleshooting. By the end of the workshop, participants will have a working pipeline for generating and interpreting time-series maps from Landsat ARCO data. All provided input data will also be openly available at global scale for large scale applications.
Post-doctoral researcher at OpenGeoHub Foundation. Working cross-projects to large scale space-time mapping with high-throughput computing.