{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"},{"label":"GIS","url":"https://skillfed.io/packages/category/scientific-engineering-gis"}],"enrichment":{"capability":"TorchGeo provides PyTorch datasets, samplers, transforms, and pre-trained models for geospatial and remote sensing data, handling multispectral satellite imagery with automatic coordinate reference system alignment.","skillfed_tags":["geospatial-ml","satellite-imagery","remote-sensing"],"use_cases":["Train semantic segmentation models on Landsat or Sentinel satellite imagery with automatic CRS alignment and band selection.","Build object detection systems on high-resolution aerial imagery using the VHR-10 benchmark dataset.","Combine multispectral satellite data from different sensors (e.g., Landsat 7 and 8) into a single training dataset.","Sample random geographic patches from continental-scale raster data like the Cropland Data Layer for efficient batch training.","Transfer-learn from pre-trained models adapted for multispectral channels beyond standard RGB imagery.","Prototype change detection or land-use classification pipelines on real remote sensing data with minimal data wrangling."],"what_it_does":"TorchGeo is a domain library built on PyTorch that bridges machine learning and geospatial data science. It provides curated datasets for satellite imagery (Landsat, Sentinel, and others), samplers that intelligently extract patches from large geographic regions, and pre-trained models adapted for multispectral remote sensing tasks. The library automatically handles the complexity of geospatial work\u2014reprojecting data into matching coordinate reference systems, aligning different satellite sources with different spectral bands, and composing datasets through set operations (union and intersection) based on geographic coverage.\n\nThe package targets two audiences: ML practitioners who want to work with satellite and earth observation data without wrestling with geospatial coordinate systems, and remote sensing experts exploring deep learning solutions. It includes both large-scale geospatial datasets (where you sample patches from continental-scale imagery) and benchmark datasets with pre-labeled examples for tasks like object detection, semantic segmentation, and change detection. All datasets integrate seamlessly with PyTorch data loaders and Lightning training pipelines.","worth_installing":"Yes, if you are working with satellite or geospatial imagery in a PyTorch workflow. The library solves real problems\u2014automatic CRS reprojection, multispectral band alignment, and intelligent patch sampling from large rasters\u2014that would otherwise require substantial custom code. Active maintenance, MIT license, and integration with Lightning make it a solid foundation. The 22 runtime dependencies are heavy but standard for this domain; install time and disk footprint are expected costs. Not necessary if your geospatial work is small-scale or already handled by other tools."},"id":"torchgeo","links":{"html":"https://skillfed.io/packages/torchgeo","md":"https://skillfed.io/packages/torchgeo.md","pypi":"https://pypi.org/project/torchgeo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":"MIT","license_treatment":"permissive","name":"torchgeo","python_support":"supports_current","summary":"TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data"},"popularity":{"monthly_downloads":79184,"position":14375,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.10.0"}