torchgeo and geoai
TorchGeo provides low-level PyTorch primitives (datasets, samplers, transforms) for geospatial machine learning, while GeoAI appears to offer higher-level geospatial AI applications and workflows, making them complementary tools that could be used together in a stack.
About torchgeo
torchgeo/torchgeo
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
This tool helps remote sensing experts and machine learning practitioners prepare diverse geospatial imagery for analysis. It allows you to combine satellite images (like Landsat) with other geographic data (like agricultural land use maps) and extract specific regions of interest. The output is ready-to-use image patches and corresponding labels, tailored for training machine learning models.
About geoai
opengeos/geoai
GeoAI: Artificial Intelligence for Geospatial Data
This tool helps geospatial researchers, environmental scientists, and urban planners use AI to analyze satellite imagery, aerial photographs, and other geographic data. You can input various geospatial datasets like GeoTIFFs or Shapefiles, train AI models for tasks like land cover classification or building detection, and get maps or classified images as output. It streamlines complex AI workflows for practitioners who need to extract insights from spatial information.
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