Luís Moreira de Sousa
Luís Moreira de Sousa completed the Degree on Informatics and Computers
Engineering at the University of Lisbon in 2002, which he complemented with a
Masters on Geographic Information Systems in 2005. In the Department of Civil
Engineering of this university he was a junior researcher from 2002 to 2008,
contributing to applied research projects with Instituto da Água and Estradas
de Portugal. After a year as consultant with SIQuant at the National Laboratory
of Civil Engineering (LNEC), in 2009 he became an independent consultant,
supporting de development of the spatial data infrastructure of the newly
created Administração da Região Hidrográfica do Tejo (ARH-Tejo).
In 2011 he joined the Henri Tudor institute in Luxembourg as Research Engineer,
where he contributed to the development of Spatial Decision Support Systems on
the domains of Energy and Urban Planning. At the time he was already a PhD
candidate on Informatics Engineering at the University of Lisbon, promoted by
Prof. Alberto Silva. In 2016 he concluded this graduation with a thesis on
domain specific languages for the development of spatial simulation programmes.
Still that year he started a post-doc position at EAWAG (Swiss Federal
Institute of Research on the Water Domain) during which he conducted research
on rapid flood modelling and storm water network management. In 2017 Luís
joined ISRIC - World Soil Information in The Netherlands where he conducted
research on the geography and ontology of world soils. In July of 2024 Luís
returned to the University of Lisbon, this time as Assistant Professor.
Sessions
Discrete Global Grid Systems (DGGS) have emerged as a transformative approach to minimizing spatial distortions in geospatial data processing. Unlike traditional methods that merely name locations, DGGS offer a highly efficient data structure capable of reducing storage requirements by up to 33% compared to the current Sentinel-2 UTM tiling grid. The performance of operations on DGGS native data cubes is intrinsically linked to the cell index, which plays a crucial role in data management and retrieval.
Most DGGS implementations utilize a hierarchical one-dimensional index to name and sort cells, optimizing them for parent-child queries. This structure is particularly beneficial for operations such as upscaling and downscaling, which are essential for integrating data with varying spatial resolutions. However, many real-world applications, such as visualization, fluid dynamics, and convolutions, require efficient handling of distant neighbor queries based on spatial distances. These applications often rely on bounding boxes or moving windows, which are not optimally supported by traditional DGGS implementations.
In response to these challenges, we introduce DGGS.jl, a Julia package specifically developed to create and utilize DGGS native data cubes optimized for distant neighbor queries. Our package employs the DGGRID Q2DI index to store data on a hexagonal ISEA4H grid, enabling compact and efficient data cube arrays. We have implemented methods to seamlessly convert raster data between geographic and Q2DI coordinates, access neighbor disks around a given cell, and visualize these data on a global scale.
To demonstrate the practical application of DGGS.jl, we present a hands-on workshop of our Julia package DGGS.jl to convert traditional geographical data cubes into DGGS native data cubes. Furthermore, we show how to process those data cubes and how to access neighbors and regions around given coordinates.
Project, Mapset, Region. Types of modules and their documentation. Pipeline programming
Scripting with GRASS: Bash, R, Python, Julia (?), matching students’ preferences and EO theme
This introduction talk will introduce advantages, technical challenges and currently available softwares.