Alen Mangafić
I work at the Geodetic Institute of Slovenia in Ljubljana, contributing to various projects as a Data Scientist, Remote Sensing Analyst, GIS Coordinator, and Specialist. My work primarily revolves around the analysis of multispectral, hyperspectral, and SAR imagery, as well as LiDAR point clouds - but I enjoy tackling data problems of all kinds. I rely heavily on Python, GRASS, GDAL, PDAL, QGIS and PostgreSQL for data torturing and distribution. I love Linux. I currently serve as the secretary of the Slovenian OSGeo Local Chapter.
Sessions
GRASS is an open source geoprocessing engine for efficient spatio-temporal data management, analysis, and modeling. The software comes with C / Python / R API, command line and graphical user interfaces.
In this talk we will give a comprehensive overview of the latest GRASS developments and upcoming new features.
This paper introduces a scalable open-source system using GRASS, TorchGeo, Python libraries, and HDF5 to map impervious surfaces from orthophotos and Sentinel-2 imagery. Outputs are vectorized, compared with agricultural and forested lands, and analyzed for environmental impacts like soil sealing to support sustainable land management and restoration.
We present i.hyper, a multimodular toolset for processing hyperspectral satellite imagery in GRASS. It supports the import of PRISMA, EnMAP and Tanager products through a dedicated import module and provides preprocessing, visualization and export. The i.hyper addon is available in the official GRASS Addons repository.