{"id":18497366,"url":"https://github.com/satellite-image-deep-learning/software","last_synced_at":"2026-01-25T07:37:31.094Z","repository":{"id":90026124,"uuid":"593161851","full_name":"satellite-image-deep-learning/software","owner":"satellite-image-deep-learning","description":"Software for working with satellite \u0026 aerial imagery ML 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href=\"https://www.satellite-image-deep-learning.com/\"\u003e\n        \u003cimg src=\"logo.png\" width=\"700\"\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n  \u003ch2\u003eSoftware for working with satellite \u0026 aerial imagery ML datasets.\u003c/h2\u003e\n\n# 👉 [satellite-image-deep-learning.com](https://www.satellite-image-deep-learning.com/) 👈\n\n\u003c/div\u003e\n\n**How to use this repository:** if you know exactly what you are looking for (e.g. you have the paper name) you can `Control+F` to search for it in this page (or search in the raw markdown).\n\n## Contents\n* [Deep learning frameworks](https://github.com/satellite-image-deep-learning/software#deep-learning-frameworks)\n* [Image dataset creation](https://github.com/satellite-image-deep-learning/software#image-dataset-creation)\n* [Image chipping/tiling \u0026 merging predictions](https://github.com/satellite-image-deep-learning/software#image-chippingtiling--merging-predictions)\n* [Image processing, handling, manipulation](https://github.com/satellite-image-deep-learning/software#image-processing-handling-manipulation)\n* [Image augmentation packages](https://github.com/satellite-image-deep-learning/software#image-augmentation-packages)\n* [SpatioTemporal Asset Catalog specification (STAC)](https://github.com/satellite-image-deep-learning/software#spatiotemporal-asset-catalog-specification-stac)\n* [OpenStreetMap](https://github.com/satellite-image-deep-learning/software#openstreetmap)\n* [QGIS](https://github.com/satellite-image-deep-learning/software#qgis)\n* [Jupyter](https://github.com/satellite-image-deep-learning/software#jupyter)\n* [Streamlit](https://github.com/satellite-image-deep-learning/software#streamlit)\n\n# Deep learning frameworks\n* [TorchGeo](https://github.com/microsoft/torchgeo) -\u003e PyTorch library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data. 📺 YouTube: [TorchGeo with Caleb Robinson](https://youtu.be/ET8Hb_HqNJQ)\n* [rastervision](https://rastervision.io/) -\u003e An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets. 📺 YouTube: [Raster Vision with Adeel Hassan](https://youtu.be/hH59fQ-HhZg)\n* [rslearn](https://github.com/allenai/rslearn) -\u003e from Allenai, a tool for developing remote sensing datasets and models.\n* [jeo](https://github.com/google-deepmind/jeo) -\u003e Jax model training lib for Earth Observation, by Deepmind\n* [segmentation_gym](https://github.com/Doodleverse/segmentation_gym) -\u003e A neural gym for training deep learning models to carry out geoscientific image segmentation, uses keras. 📺 YouTube: [Satellite image segmentation using the Doodleverse segmentation gym with Dan Buscombe](https://youtu.be/0I1TOOGfdZ0)\n* [GeoDeep](https://github.com/uav4geo/GeoDeep) -\u003e Free and open source library for AI object detection and semantic segmentation in geospatial rasters.\n* [sits](https://github.com/e-sensing/sits) -\u003e Satellite image time series in R. 📺 YouTube: [Satellite image time series with Gilberto Camara](https://youtu.be/0_wt_m6DoyI)\n* [ConfigILM](https://github.com/lhackel-tub/ConfigILM) -\u003e A configurable framework for Image-Language Models within pytorch with a focus on Visual Question Answering for Remote Sensing. 📺 YouTube: [AICube auf EO-Lab - ConfigILM Python-Bibliothek](https://www.youtube.com/watch?v=eKGmVtkJjF0)\n* [torchrs](https://github.com/isaaccorley/torchrs) -\u003e PyTorch implementation of popular datasets and models in remote sensing\n* [pytorch-enhance](https://github.com/isaaccorley/pytorch-enhance) -\u003e Open-source Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use\n* [GeoTorchAI](https://github.com/DataSystemsLab/GeoTorchAI) -\u003e A Deep Learning and Scalable Data Processing Framework for Raster and Spatio-Temporal Datasets, uses PyTorch and Apache Sedona\n* [EarthNets](https://earthnets.nicepage.io/) -\u003e includes a database of 400 baseline models, and tutorial examples of common deep learning tasks on satellite imagery\n* [PaddleRS](https://github.com/PaddlePaddle/PaddleRS) -\u003e remote sensing image processing development kit based on PaddlePaddle. For English see README_EN.md\n* [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) -\u003e Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen \u0026 iSAID\n* [mmrotate](https://github.com/open-mmlab/mmrotate) -\u003e Open-source toolbox for rotated object detection which is great for detecting randomly oriented objects in huge satellite images\n* [Myria3D](https://github.com/IGNF/myria3d) -\u003e Myria3D is a deep learning library designed with a focused scope: the multiclass semantic segmentation of large scale, high density aerial Lidar points cloud.\n* [Open3D-ML](https://github.com/isl-org/Open3D-ML) -\u003e Open3D-ML focuses on applications such as semantic point cloud segmentation and provides pretrained models that can be applied to common tasks as well as pipelines for training. It works with TensorFlow and PyTorch.\n* [DeepHyperX](https://github.com/eecn/Hyperspectral-Classification) -\u003e A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets\n* [DELTA](https://github.com/nasa/delta) -\u003e Deep Earth Learning, Tools, and Analysis, by NASA is a framework for deep learning on satellite imagery, based on Tensorflow \u0026 using MLflow for tracking experiments\n* [pytorch_eo](https://github.com/earthpulse/pytorch_eo) -\u003e aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike\n* [NGVEO](https://github.com/ESA-PhiLab/NGVEO) -\u003e applying convolutional neural networks (CNN) to Earth Observation (EO) data from Sentinel 1 and 2 using python and PyTorch\n* [chip-n-scale-queue-arranger by developmentseed](https://github.com/developmentseed/chip-n-scale-queue-arranger) -\u003e an orchestration pipeline for running machine learning inference at scale. [Supports fastai models](https://github.com/developmentseed/fastai-serving)\n* [TorchSat](https://github.com/sshuair/torchsat) is an open-source deep learning framework for satellite imagery analysis based on PyTorch (no activity since June 2020)\n* [DeepNetsForEO](https://github.com/nshaud/DeepNetsForEO) -\u003e Uses SegNET for working on remote sensing images using deep learning (no activity since 2019)\n* [RoboSat](https://github.com/mapbox/robosat) -\u003e semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds (no longer maintained)\n* [DeepOSM](https://github.com/trailbehind/DeepOSM) -\u003e Train a deep learning net with OpenStreetMap features and satellite imagery (no activity since 2017)\n* [mapwith.ai](https://mapwith.ai/) -\u003e AI assisted mapping of roads with OpenStreetMap. Part of [Open-Mapping-At-Facebook](https://github.com/facebookmicrosites/Open-Mapping-At-Facebook)\n* [terragpu](https://github.com/nasa-cisto-ai/terragpu) -\u003e Python library to process and classify remote sensing imagery by means of GPUs and AI/ML\n* [EOTorchLoader](https://github.com/ndavid/EOTorchLoader) -\u003e Pytorch dataloader and pytorch lightning datamodule for Earth Observation imagery\n* [satellighte](https://github.com/canturan10/satellighte) -\u003e an image classification library that consist state-of-the-art deep learning methods, using PyTorch Lightning\n* [rsi-semantic-segmentation](https://github.com/xdu-jjgs/rsi-semantic-segmentation) -\u003e A unified PyTorch framework for semantic segmentation from remote sensing imagery, in pytorch, uses DeepLabV3ResNet\n* [ODEON landcover](https://github.com/IGNF/odeon-landcover) -\u003e a set of command-line tools performing semantic segmentation on remote sensing images (aerial and/or satellite) with as many layers as you wish\n* [AiTLAS](https://github.com/biasvariancelabs/aitlas) -\u003e implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images\n* [aitlas-arena](https://github.com/biasvariancelabs/aitlas-arena) -\u003e An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)\n* [RocketML Deep Neural Networks](https://github.com/rocketmlhq/rmldnn) -\u003e read [Satellite Image Classification](https://github.com/rocketmlhq/rmldnn/tree/main/tutorials/satellite_image_classification) using rmldnn and Sentinel 2 data\n* [raster4ml](https://github.com/remotesensinglab/raster4ml) -\u003e A geospatial raster processing library for machine learning\n* [moonshine](https://github.com/moonshinelabs-ai/moonshine) -\u003e a Python package that makes it easier to train models on remote sensing data like satellite imagery\n* [SemanticSeg4EO](https://github.com/aleguillou1/SemanticSeg4EO) -\u003e A unified PyTorch framework for semantic segmentation from remote sensing imagery\n\n# Image dataset creation\nMany datasets on kaggle \u0026 elsewhere have been created by screen-clipping Google Maps or browsing web portals. The tools below are to create datasets programatically\n* [MapTilesDownloader](https://github.com/AliFlux/MapTilesDownloader) -\u003e A super easy to use map tiles downloader built using Python\n* [jimutmap](https://github.com/Jimut123/jimutmap) -\u003e get enormous amount of high resolution satellite images from apple / google maps quickly through multi-threading\n* [google-maps-downloader](https://github.com/yildirimcagatay34/google-maps-downloader) -\u003e A short python script that downloads satellite imagery from Google Maps\n* [ExtractSatelliteImagesFromCSV](https://github.com/thewati/ExtractSatelliteImagesFromCSV) -\u003e extract satellite images using a CSV file that contains latitude and longitude, uses mapbox\n* [sentinelsat](https://github.com/sentinelsat/sentinelsat) -\u003e Search and download Copernicus Sentinel satellite images\n* [SentinelDownloader](https://github.com/cordmaur/SentinelDownloader) -\u003e a high level wrapper to the SentinelSat that provides an object oriented interface, asynchronous downloading, quickview \u0026 simpler searching methods\n* [GEES2Downloader](https://github.com/cordmaur/GEES2Downloader) -\u003e Downloader for GEE S2 bands\n* [Sentinel-2 satellite tiles images downloader from Copernicus](https://github.com/flaviostutz/sentinelloader) -\u003e Minimizes data download and combines multiple tiles to return a single area of interest\n* [felicette](https://github.com/plant99/felicette) -\u003e Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data\n* [Easy Landsat Download](https://github.com/dgketchum/Landsat578)\n* [A simple python scrapper to get satellite images of Africa, Europe and Oceania's weather using the Sat24 website](https://github.com/luistripa/sat24-image-scrapper)\n* [RGISTools](https://github.com/spatialstatisticsupna/RGISTools) -\u003e Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel\n* [DeepSatData](https://github.com/michaeltrs/DeepSatData) -\u003e Automatically create machine learning datasets from satellite images\n* [landsat_ingestor](https://github.com/landsat-pds/landsat_ingestor) -\u003e Scripts and other artifacts for landsat data ingestion into Amazon public hosting\n* [satpy](https://github.com/pytroll/satpy) -\u003e a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats\n* [GIBS-Downloader](https://github.com/spaceml-org/GIBS-Downloader) -\u003e a command-line tool which facilitates the downloading of NASA satellite imagery and offers different functionalities in order to prepare the images for training in a machine learning pipeline\n* [eodag](https://github.com/CS-SI/eodag) -\u003e Earth Observation Data Access Gateway\n* [pylandsat](https://github.com/yannforget/pylandsat) -\u003e Search, download, and preprocess Landsat imagery\n* [landsatxplore](https://github.com/yannforget/landsatxplore) -\u003e Search and download Landsat scenes from EarthExplorer\n* [OpenSarToolkit](https://github.com/ESA-PhiLab/OpenSarToolkit) -\u003e High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language\n* [lsru](https://github.com/loicdtx/lsru) -\u003e Query and Order Landsat Surface Reflectance data via ESPA\n* [eoreader](https://github.com/sertit/eoreader) -\u003e Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way\n* [Export thumbnails from Earth Engine](https://gorelick.medium.com/fast-er-downloads-a2abd512aa26)\n* [deepsentinel-osm](https://github.com/Lkruitwagen/deepsentinel-osm) -\u003e A repository to generate land cover labels from OpenStreetMap\n* [img2dataset](https://github.com/rom1504/img2dataset) -\u003e Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine\n* [ohsome2label](https://github.com/GIScience/ohsome2label) -\u003e Historical OpenStreetMap (OSM) Objects to Machine Learning Training Samples\n* [Label Maker](https://github.com/developmentseed/label-maker) -\u003e a library for creating machine-learning ready data by pairing satellite images with OpenStreetMap (OSM) vector data\n* [sentinel2tools](https://github.com/QuantuMobileSoftware/sentinel2tools) -\u003e downloading \u0026 basic processing of Sentinel 2 imagesry. Read [Sentinel2tools: simple lib for downloading Sentinel-2 satellite images](https://medium.com/geekculture/sentinel2tools-simple-lib-for-downloading-sentinel-2-satellite-images-f8a6be3ee894)\n* [Aerial-Satellite-Imagery-Retrieval](https://github.com/chiragkhandhar/Aerial-Satellite-Imagery-Retrieval) -\u003e A program using Bing maps tile system to automatically download Aerial / Satellite Imagery given a lat/lon bounding box and level of detail\n* [google-maps-at-88-mph](https://github.com/doersino/google-maps-at-88-mph) -\u003e Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF\n* [srtmDownloader](https://github.com/Abdi-Ghasem/srtmDownloader) -\u003e Python library (multi-threaded) for retrieving SRTM elevation map of CGIAR-CSI\n* [ImageDatasetViz](https://github.com/vfdev-5/ImageDatasetViz) -\u003e create a mosaic of images in a dataset for previewing purposes\n* [landsatlinks](https://github.com/ernstste/landsatlinks) -\u003e A simple CLI interface to generate download urls for Landsat Collection 2 Level 1 product bundles\n* [pyeo](https://github.com/clcr/pyeo) -\u003e a set of portable, extensible and modular Python scripts for machine learning in earth observation and GIS, including downloading, preprocessing, creation of base layers, classification and validation.\n* [metaearth](https://github.com/bair-climate-initiative/metaearth) -\u003e Download and access remote sensing data from any platform\n* [geoget](https://github.com/mnpinto/geoget) -\u003e Download geodata for anywhere in Earth via ladsweb.modaps.eosdis.nasa.gov\n* [geeml](https://github.com/Geethen/geeml) -\u003e A python package to extract Google Earth Engine data for machine learning\n* [xlandsat](https://github.com/compgeolab/xlandsat) -\u003e A Python package for handling Landsat scenes from EarthExplorer with xarray\n* [tms2geotiff](https://github.com/gumblex/tms2geotiff) -\u003e Download tiles from Tile Map Server (online maps) and make a large geo-referenced image\n* [s2-chips](https://github.com/tharlestsa/s2-chips) -\u003e efficiently extracts satellite imagery chips from Sentinel-2 datasets based on given geo-coordinates from a GeoJSON file. Uses Ray for parallel processing\n* [geetiles](https://github.com/rramosp/geetiles) -\u003e download Google Earth Engine datasets to tiles as geotiff arrays\n* [terragon](https://github.com/drnhhl/terragon) -\u003e Terragon is a Python library to download and process satellite imagery from multiple sources such as Microsoft Planetary Computer, Google Earth Engine and Copernicus Data Space Ecosystem\n* [geefetch](https://github.com/gbelouze/geefetch) -\u003e A library/CLI to download data from Google Earth Engine.\n\n# Image chipping/tiling \u0026 merging predictions\nDue to the large size of raw images, it is often necessary to chip or tile them into smaller patches before annotation and training. Similarly, models typically make predictions on these smaller patches, and it is essential to merge these predictions to reconstruct the full-sized image accurately.\n* [image_slicer](https://github.com/samdobson/image_slicer) -\u003e Split images into tiles. Join the tiles back together\n* [tiler by nuno-faria](https://github.com/nuno-faria/tiler) -\u003e split images into tiles and merge tiles into a large image\n* [tiler by the-lay](https://github.com/the-lay/tiler) -\u003e N-dimensional NumPy array tiling and merging with overlapping, padding and tapering\n* [xbatcher](https://github.com/pangeo-data/xbatcher) -\u003e Xbatcher is a small library for iterating xarray DataArrays in batches. The goal is to make it easy to feed xarray datasets to machine learning libraries such as Keras\n* [GeoTagged_ImageChip](https://github.com/Hejarshahabi/GeoTagged_ImageChip) -\u003e A simple script to create geo tagged image chips from high resolution RS iamges for training deep learning models such as Unet\n* [geotiff-crop-dataset](https://github.com/tayden/geotiff-crop-dataset) -\u003e A Pytorch Dataloader for tif image files that dynamically crops the image\n* [Train-Test-Validation-Dataset-Generation](https://github.com/salarghaffarian/Train-Test-Validation-Dataset-Generation) -\u003e  app to crop images and create small patches of a large image e.g. Satellite/Aerial Images, which will then be used for training and testing Deep Learning models specifically semantic segmentation models\n* [satproc](https://github.com/dymaxionlabs/satproc) -\u003e Python library and CLI tools for processing geospatial imagery for ML\n* [Sliding Window](https://github.com/adamrehn/slidingwindow) -\u003e  break large images into a series of smaller chunks\n* [patchify](https://github.com/dovahcrow/patchify.py) -\u003e A library that helps you split image into small, overlappable patches, and merge patches into original image\n* [split-rs-data](https://github.com/Youssef-Harby/split-rs-data) -\u003e Divide remote sensing images and their labels into data sets of specified size\n* [image-reconstructor-patches](https://github.com/marijavella/image-reconstructor-patches) -\u003e Reconstruct Image from Patches with a Variable Stride\n* [rpc_cropper](https://github.com/carlodef/rpc_cropper) -\u003e A small standalone tool to crop satellite images and their RPC\n* [geotile](https://github.com/iamtekson/geotile) -\u003e python library for tiling the geographic raster data\n* [GeoPatch](https://github.com/Hejarshahabi/GeoPatch) -\u003e generating patches from remote sensing data\n* [ImageTilingUtils](https://github.com/vfdev-5/ImageTilingUtils) -\u003e Minimalistic set of image reader agnostic tools to easily iterate over large images\n* [split_raster](https://github.com/cuicaihao/split_raster) -\u003e Creates a tiled output from an input raster dataset. pip installable\n* [SAHI](https://github.com/obss/sahi) -\u003e Utilties for performing sliced inference and generating merged predictions. Also checkout [supervision](https://supervision.roboflow.com/latest/) which additionally supports rotated bounding boxes.\n* [geo2ml](https://github.com/mayrajeo/geo2ml) -\u003e Python library and module for converting earth observation data to be suitable for machine learning models, Converting vector data to COCO and YOLO formats and creating required dataset files, Rasterizing polygon geometries for semantic segmentation tasks, Tiling larger rasters and shapefiles into smaller patches\n* [FlipnSlide](https://github.com/elliesch/flipnslide) -\u003e a concise tiling and augmentation strategy to prepare large scientific images for use with GPU-enabled algorithms. Outputs PyTorch-ready preprocessed datasets from a single large image.\n* [segmenteverygrain](https://github.com/zsylvester/segmenteverygrain) -\u003e implements merging of segmentation predictions with patching and weighted merging in `predict_big_image`\n* [image-bbox-slicer](https://image-bbox-slicer.readthedocs.io/en/latest/) -\u003e slice images and their bounding box annotations into smaller tiles, both into specific sizes and into any arbitrary number of equal parts\n* [stacchip](https://github.com/Clay-foundation/stacchip) -\u003e Dynamically create image chips from STAC items\n* [seamless-seg](https://github.com/Multihuntr/seamless-seg) -\u003e Removes tiling artifacts created from stitching together adjacent tiles in large segmentation tasks.\n* [PolyGoneNMS](https://github.com/WolodjaZ/PolyGoneNMS) -\u003e efficient region-level nonmax-suppression (NMS), used in [tree-detection-framework](https://github.com/open-forest-observatory/tree-detection-framework)\n* [geodataset](https://github.com/hugobaudchon/geodataset) -\u003e tools for cutting rasters and their labels into smaller tiles. Provides datasets compatible with pytorch.\n* [MultiClean](https://github.com/DPIRD-DMA/MultiClean) -\u003e morphological cleaning of multiclass 2D numpy arrays (segmentation masks and classification rasters)\n* [EarthObservationTiles](https://github.com/SBCV/EarthObservationTiles) -\u003e Divides a set of earth observation images with inhomogeneous properties into tiles with consistent real world extent. Provides refined control over tile granularity, tile stride, and image boundary alignment (e.g. to perform a tile-specific data augmentation during training). Overlapping segmentation labels may be fused and stored as geojson files.\n\n## Image processing, handling, manipulation\n* [Pillow is the Python Imaging Library](https://pillow.readthedocs.io/en/stable/) -\u003e this will be your go-to package for image manipulation in python\n* [opencv-python](https://github.com/opencv/opencv-python) is pre-built CPU-only OpenCV packages for Python\n* [kornia](https://github.com/kornia/kornia) is a differentiable computer vision library for PyTorch, like openCV but on the GPU. Perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.\n* [tifffile](https://github.com/cgohlke/tifffile) -\u003e Read and write TIFF files\n* [xtiff](https://github.com/BodenmillerGroup/xtiff) -\u003e A small Python 3 library for writing multi-channel TIFF stacks\n* [geotiff](https://github.com/Open-Source-Agriculture/geotiff) -\u003e A noGDAL tool for reading and writing geotiff files\n* [geolabel-maker](https://github.com/makinacorpus/geolabel-maker) -\u003e combine satellite or aerial imagery with vector spatial data to create your own ground-truth dataset in the COCO format for deep-learning models\n* [imagehash](https://github.com/JohannesBuchner/imagehash) -\u003e Image hashes tell whether two images look nearly identical\n* [fake-geo-images](https://github.com/up42/fake-geo-images) -\u003e A module to programmatically create geotiff images which can be used for unit tests\n* [imagededup](https://github.com/idealo/imagededup) -\u003e Finding duplicate images made easy! Uses perceptual hashing\n* [duplicate-img-detection](https://github.com/mattpodolak/duplicate-img-detection) -\u003e A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash\n* [rmstripes](https://github.com/DHI-GRAS/rmstripes) -\u003e Remove stripes from images with a combined wavelet/FFT approach\n* [activeloopai Hub](https://github.com/activeloopai/hub) -\u003e The fastest way to store, access \u0026 manage datasets with version-control for PyTorch/TensorFlow. Works locally or on any cloud. Scalable data pipelines.\n* [sewar](https://github.com/andrewekhalel/sewar) -\u003e All image quality metrics you need in one package\n* [Satellite imagery label tool](https://github.com/calebrob6/labeling-tool) -\u003e provides an easy way to collect a random sample of labels over a given scene of satellite imagery\n* [Missing-Pixel-Filler](https://github.com/spaceml-org/Missing-Pixel-Filler) -\u003e given images that may contain missing data regions (like satellite imagery with swath gaps), returns these images with the regions filled\n* [color_range_filter](https://github.com/developmentseed/color_range_filter) -\u003e a script that allows us to find range of colors in images using openCV, and then convert them into geo vectors\n* [eo4ai](https://github.com/ESA-PhiLab/eo4ai) -\u003e easy-to-use tools for preprocessing datasets for image segmentation tasks in Earth Observation\n* [rasterix](https://github.com/mogasw/rasterix) -\u003e a cross-platform utility built around the GDAL library and the Qt framework designed to process geospatial raster data\n* [datumaro](https://github.com/openvinotoolkit/datumaro) -\u003e Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets\n* [sentinelPot](https://github.com/LLeiSong/sentinelPot) -\u003e a python package to preprocess Sentinel 1\u00262 imagery\n* [ImageAnalysis](https://github.com/UASLab/ImageAnalysis) -\u003e Aerial imagery analysis, processing, and presentation scripts.\n* [rastertodataframe](https://github.com/mblackgeo/rastertodataframe) -\u003e Convert any GDAL compatible raster to a Pandas DataFrame\n* [yeoda](https://github.com/TUW-GEO/yeoda) -\u003e provides lower and higher-level data cube classes to work with well-defined and structured earth observation data\n* [tiles-to-tiff](https://github.com/jimutt/tiles-to-tiff) -\u003e Python script for converting XYZ raster tiles for slippy maps to a georeferenced TIFF image\n* [telluric](https://github.com/satellogic/telluric) -\u003e a Python library to manage vector and raster geospatial data in an interactive and easy way\n* [Sniffer](https://github.com/2320sharon/Sniffer) -\u003e A python application for sorting through geospatial imagery\n* [pyjeo](https://github.com/ec-jrc/jeolib-pyjeo) -\u003e a library for image processing for geospatial data implemented in JRC Ispra, with [paper](https://www.mdpi.com/2220-9964/8/10/461)\n* [vpv](https://github.com/kidanger/vpv) -\u003e Image viewer designed for image processing experts\n* [arop](https://github.com/george-silva/arop) -\u003e Automated Registration and Orthorectification Package\n* [satellite_image](https://github.com/dgketchum/satellite_image) -\u003e Python package to process images from Landsat satellites and return geographic information, cloud mask, numpy array, geotiff\n* [large_image](https://github.com/girder/large_image) -\u003e Python modules to work with large multiresolution images\n* [ResizeRight](https://github.com/assafshocher/ResizeRight) -\u003e The correct way to resize images or tensors. For Numpy or Pytorch (differentiable)\n* [pysat](https://github.com/pysat/pysat) -\u003e a package providing a simple and flexible interface for downloading, loading, cleaning, managing, processing, and analyzing scientific measurements\n* [plcompositor](https://github.com/planetlabs/plcompositor) -\u003e c++ tool from Planet to create seamless and cloudless image mosaics from deep stacks of satellite imagery\n* [georeader](https://github.com/spaceml-org/georeader) -\u003e a package to process raster data from different satellite missions\n* [rico-hdl](https://github.com/kai-tub/rico-hdl) -\u003e A fast and easy-to-use Remote sensing Image format COnverter for High-throughput Deep-Learning (rico-hdl)\n* [rastereasy](https://github.com/pythonraster/rastereasy/) -\u003e simplify geospatial workflows by offering tools for reading and processing raster and vector files, resampling, cropping, reprojecting, stacking, filtering, etc\n\n## Image augmentation packages\nImage augmentation is a technique used to expand a training dataset in order to improve ability of the model to generalise\n* [AugLy](https://github.com/facebookresearch/AugLy) -\u003e A data augmentations library for audio, image, text, and video. By Facebook\n* [albumentations](https://github.com/albumentations-team/albumentations) -\u003e Fast image augmentation library and an easy-to-use wrapper around other libraries\n* [FoHIS](https://github.com/noahzn/FoHIS) -\u003e Towards Simulating Foggy and Hazy Images and Evaluating their Authenticity\n* [Kornia](https://kornia.readthedocs.io/en/latest/augmentation.html) provides augmentation on the GPU\n* [toolbox by ming71](https://github.com/ming71/toolbox) -\u003e various cv tools, such as label tools, data augmentation, label conversion, etc.\n* [AstroAugmentations](https://github.com/mb010/AstroAugmentations) -\u003e augmentations designed around astronomical instruments\n* [Chessmix](https://github.com/matheusbarrosp/chessmix) -\u003e data augmentation method for remote sensing semantic segmentation\n* [satellite_object_augmentation](https://github.com/LanaLana/satellite_object_augmentation) -\u003e Object-based augmentation for remote sensing images segmentation via CNN\n* [hypernet](https://github.com/ESA-PhiLab/hypernet) -\u003e hyperspectral data augmentation\n* [AdverseWeatherSimulation](https://github.com/RicardooYoung/AdverseWeatherSimulation) -\u003e a simulator that generates foggy, rainy, smoky and cloudy image over a clear remote sensing image\n\n# SpatioTemporal Asset Catalog specification (STAC)\nThe STAC specification provides a common metadata specification, API, and catalog format to describe geospatial assets, so they can more easily indexed and discovered.\n* Spec at https://github.com/radiantearth/stac-spec\n* [STAC 1.0.0: The State of the STAC Software Ecosystem](https://medium.com/radiant-earth-insights/stac-1-0-0-software-ecosystem-updates-da4e800a4973)\n* [Getting Started with STAC APIs](https://www.azavea.com/blog/2021/04/05/getting-started-with-stac-apis/) intro article\n* [SpatioTemporal Asset Catalog API specification](https://github.com/radiantearth/stac-api-spec) -\u003e an API to make geospatial assets openly searchable and crawlable\n* [stacindex](https://stacindex.org/) -\u003e STAC Catalogs, Collections, APIs, Software and Tools\n* Several useful repos on https://github.com/sat-utils\n* [Intake-STAC](https://github.com/intake/intake-stac) -\u003e Intake-STAC provides an opinionated way for users to load Assets from STAC catalogs into the scientific Python ecosystem. It uses the intake-xarray plugin and supports several file formats including GeoTIFF, netCDF, GRIB, and OpenDAP.\n* [sat-utils/sat-search](https://github.com/sat-utils/sat-search) -\u003e Sat-search is a Python 3 library and a command line tool for discovering and downloading publicly available satellite imagery using STAC compliant API\n* [franklin](https://github.com/azavea/franklin) -\u003e A STAC/OGC API Features Web Service focused on ease-of-use for end-users.\n* [stacframes](https://github.com/azavea/stacframes) -\u003e A Python library for working with STAC Catalogs via Pandas DataFrames\n* [sat-api-pg](https://github.com/developmentseed/sat-api-pg) -\u003e A Postgres backed STAC API\n* [stactools](https://github.com/stac-utils/stactools) -\u003e Command line utility and Python library for STAC\n* [pystac](https://github.com/stac-utils/pystac) -\u003e Python library for working with any STAC Catalog\n* [STAC Examples for Nightlights data](https://github.com/developmentseed/nightlights_stac_examples) -\u003e minimal example STAC implementation for the [Light Every Night](https://registry.opendata.aws/wb-light-every-night/) dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data\n* [stackstac](https://github.com/gjoseph92/stackstac) -\u003e Turn a STAC catalog into a dask-based xarray\n* [stac-fastapi](https://github.com/stac-utils/stac-fastapi) -\u003e STAC API implementation with FastAPI\n* [stac-fastapi-elasticsearch](https://github.com/stac-utils/stac-fastapi-elasticsearch) -\u003e Elasticsearch backend for stac-fastapi\n* [ml-aoi](https://github.com/stac-extensions/ml-aoi) -\u003e An Item and Collection extension to provide labeled training data for machine learning models\n* Discoverable and Reusable ML Workflows for Earth Observation -\u003e [part 1](https://medium.com/radiant-earth-insights/discoverable-and-reusable-ml-workflows-for-earth-observation-part-1-e198507b5eaa) and [part 2](https://medium.com/radiant-earth-insights/discoverable-and-reusable-ml-workflows-for-earth-observation-part-2-ebe2b4812d5a) with the Geospatial Machine Learning Model Catalog (GMLMC)\n* [eoAPI](https://github.com/developmentseed/eoAPI) -\u003e Earth Observation API with STAC + dynamic Raster/Vector Tiler\n* [stac-nb](https://github.com/darrenwiens/stac-nb) -\u003e STAC in Jupyter Notebooks\n* [xstac](https://github.com/TomAugspurger/xstac) -\u003e Generate STAC Collections from xarray datasets\n* [qgis-stac-plugin](https://github.com/stac-utils/qgis-stac-plugin) -\u003e QGIS plugin for reading STAC APIs\n* [cirrus-geo](https://github.com/cirrus-geo/cirrus-geo) -\u003e a STAC-based processing pipeline\n* [stac-interactive-search](https://github.com/calebrob6/stac-interactive-search) -\u003e A simple (browser based) UI for searching STAC APIs\n* [easystac](https://github.com/cloudsen12/easystac) -\u003e A Python package for simple STAC queries\n* [stacmap](https://github.com/aazuspan/stacmap) -\u003e Explore STAC items with an interactive map\n* [odc-stac](https://github.com/opendatacube/odc-stac) -\u003e Load STAC items into xarray Datasets. Process locally or distribute data loading and computation with Dask.\n* [AWS Lambda SenCloud Monitoring](https://github.com/ahuarte47/aws-sencloud-monitoring) -\u003e keep up-to-date your own derived data from the Sentinel-2 COG imagery archive using AWS lambda\n* [stac-geoparquet](https://github.com/TomAugspurger/stac-geoparquet) -\u003e Convert STAC items to geoparquet\n* [labs-gpt-stac](https://github.com/developmentseed/labs-gpt-stac) -\u003e connect ChatGPT to a STAC API backend\n* [stac_ipyleaflet](https://github.com/MAAP-Project/stac_ipyleaflet) -\u003e stac_ipyleaflet is a customized version of ipyleaflet built to be an in-jupyter-notebook interactive mapping library that prioritizes access to STAC catalog data\n* [prefect-planetary-computer](https://github.com/giorgiobasile/prefect-planetary-computer) -\u003e Prefect integrations with Microsoft Planetary Computer\n* [STAC Machine Learning Model (MLM) Extension](https://github.com/stac-extensions/mlm) -\u003e to describe ML models, their training details, and inference runtime requirements.\n* [STAC Label Extension](https://github.com/stac-extensions/label) -\u003e facilitates the integration of labeled Areas of Interest (AOIs) with the STAC for machine learning and other applications.\n* [STAC-RAG](https://github.com/bmcandr/stac-rag-demo) -\u003e how to use LLMs to improve discoverability of Earth observation datasets.\n* [Earth-Copilot](https://github.com/microsoft/Earth-Copilot) -\u003e Leverage LLMs to query spatiotemporal datasets via natural language, from Microsoft\n* [stac-mcp](https://github.com/Wayfinder-Foundry/stac-mcp) -\u003e An MCP Server for STAC requests\n\n# OpenStreetMap\n[OpenStreetMap](https://www.openstreetmap.org/) (OSM) is a map of the world, created by people like you and free to use under an open license. Quite a few publications use OSM data for annotations \u0026 ground truth. Note that the data is created by volunteers and the quality can be variable\n* [osmnx](https://github.com/gboeing/osmnx) -\u003e Retrieve, model, analyze, and visualize data from OpenStreetMap\n* [ohsome2label](https://github.com/GIScience/ohsome2label) -\u003e Historical OpenStreetMap Objects to Machine Learning Training Samples\n* [Label Maker](https://github.com/developmentseed/label-maker) -\u003e downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an `.npz` file for use in machine learning training. This should be used instead of the deprecated [skynet-data](https://github.com/developmentseed/skynet-data)\n* [prettymaps](https://github.com/marceloprates/prettymaps) -\u003e A small set of Python functions to draw pretty maps from OpenStreetMap data\n* [Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps](https://arxiv.org/abs/1705.06057) -\u003e fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper\n* [Identifying Buildings in Satellite Images with Machine Learning and Quilt](https://github.com/jyamaoka/LandUse) -\u003e NDVI \u0026 edge detection via gaussian blur as features, fed to TPOT for training with labels from OpenStreetMap, modelled as a two class problem, “Buildings” and “Nature”\n* [Import OpenStreetMap data into Unreal Engine 4](https://github.com/ue4plugins/StreetMap)\n* [OSMDeepOD](https://github.com/geometalab/OSMDeepOD) -\u003e  perform object detection with retinanet\n* [Match Bing Map Aerial Imagery with OpenStreetMap roads](https://github.com/whywww/Aerial-Imagery-and-OpenStreetMap-Retrieval)\n* [Computer Vision With OpenStreetMap and SpaceNet — A Comparison](https://medium.com/the-downlinq/computer-vision-with-openstreetmap-and-spacenet-a-comparison-cc70353d0ace)\n* [url-map](https://simonwillison.net/2022/Jun/12/url-map/) -\u003e A tiny web app to create images from OpenStreetMap maps\n* [Label Maker](https://github.com/developmentseed/label-maker) -\u003e a library for creating machine-learning ready data by pairing satellite images with OpenStreetMap (OSM) vector data\n* [baremaps](https://github.com/baremaps/baremaps) -\u003e Create custom vector tiles from OpenStreetMap and other data sources with Postgis and Java.\n* [osm2streets](https://github.com/a-b-street/osm2streets) -\u003e Convert OSM to street networks with detailed geometry\n* [ohsome-planet](https://github.com/GIScience/ohsome-planet) -\u003e Transform OSM (history) PBF files into GeoParquet. Enrich with OSM changeset metadata and country information.\n\n# QGIS\n[QGIS](https://qgis.org/en/site/) is a desktop appication written in python and extended with plugins which are essentially python scripts\n* Create, edit, visualise, analyse and publish geospatial information. Open source alternative to ArcGIS.\n* [Python scripting](https://docs.qgis.org/testing/en/docs/pyqgis_developer_cookbook/intro.html#scripting-in-the-python-console)\n* Create your own plugins using the [QGIS Plugin Builder](http://g-sherman.github.io/Qgis-Plugin-Builder/)\n* [DeepLearningTools plugin](https://plugins.qgis.org/plugins/DeepLearningTools/) -\u003e aid training Deep Learning Models\n* [Mapflow.ai plugin](https://www.gislounge.com/run-ai-mapping-in-qgis-over-high-resolution-satellite-imagery/) -\u003e various models to extract building footprints etc from Maxar imagery\n* [dzetsaka plugin](https://github.com/nkarasiak/dzetsaka) -\u003e classify different kind of vegetation\n* [Coregistration-Qgis-processing](https://github.com/SMByC/Coregistration-Qgis-processing) -\u003e Qgis processing plugin for image co-registration; projection and pixel alignment based on a target image, uses Arosics\n* [qgis-stac-plugin](https://github.com/stac-utils/qgis-stac-plugin) -\u003e QGIS plugin for reading STAC APIs\n* [buildseg](https://github.com/deepbands/buildseg) -\u003e a building extraction plugin of QGIS based on ONNX\n* [deep-learning-datasets-maker](https://github.com/deepbands/deep-learning-datasets-maker) -\u003e a QGIS plugin to make datasets creation easier for raster and vector data\n* [Modzy-QGIS-Plugin](https://github.com/modzy/Modzy-QGIS-Plugin) -\u003e demos Vehicle Detection model\n* [kart](https://plugins.qgis.org/plugins/kart/) -\u003e provides modern, open source, distributed version-control for geospatial and tabular datasets\n* [Plugin for Landcover Classification](https://github.com/atishayjn/QGIS-Plugin) -\u003e capable of implementing machine learning algorithms such as Random forest, SVM and CNN algorithms such as UNET through a simple GUI framework.\n* [pg_tileserv])(https://github.com/CrunchyData/pg_tileserv) -\u003e A very thin PostGIS-only tile server in Go. Takes in HTTP tile requests, executes SQL, returns MVT tiles.\n* [pg_featureserv](https://github.com/CrunchyData/pg_featureserv) -\u003e Lightweight RESTful Geospatial Feature Server for PostGIS in Go\n* [osm-instance-segmentation](https://github.com/mnboos/osm-instance-segmentation) -\u003e QGIS plugin for finding changes in vector data from orthophotos (i.e. aerial imagery) using tensorflow\n* [Semi-Automatic Classification Plugin](https://github.com/semiautomaticgit/SemiAutomaticClassificationPlugin) -\u003e supervised classification of remote sensing images, providing tools for the download, the preprocessing and postprocessing of images\n* [chippy-checker-editor](https://github.com/devglobalpartners/chippy-checker-editor) -\u003e QGIS plugin for viewing and editing labeled remote sensing images\n* [qgis-plugin-deepness](https://github.com/PUTvision/qgis-plugin-deepness) -\u003e Plugin for neural network inference in QGIS: segmentation, regression and detection\n* [QGPTAgent](https://github.com/momaabna/QGPTAgent) -\u003e plugin for QGIS that utilizes the advanced natural language processing capabilities of the OpenAI GPT model to automate various processes in QGIS\n* [EO Time Series Viewer](https://eo-time-series-viewer.readthedocs.io/en/latest/) -\u003e QGIS Plugin to visualize and label raster-based earth observation time series data\n* [Deepness](https://github.com/PUTvision/qgis-plugin-deepness) -\u003e a remote sensing plugin that enables deep learning inference in QGIS\n* [jupyter-remote-qgis-proxy](https://github.com/sunu/jupyter-remote-qgis-proxy) -\u003e Run QGIS inside a remote Desktop on Jupyter and open remote data sources\n* [LLMFileDescribe](https://github.com/r-wenger/LLMFileDescribe) -\u003e QGIS plugin for AI-powered geospatial data description using local LLMs (Ollama).\n\n# Jupyter\nThe [Jupyter](https://jupyter.org/) Notebook is a web-based interactive computing platform. There are many extensions which make it a powerful environment for analysing satellite imagery\n* [jupyterlite](https://jupyterlite.readthedocs.io/en/latest/) -\u003e JupyterLite is a JupyterLab distribution that runs entirely in the browser\n* [jupyter_compare_view](https://github.com/Octoframes/jupyter_compare_view) -\u003e Blend Between Multiple Images\n* [folium](https://python-visualization.github.io/folium/quickstart.html) -\u003e display interactive maps in Jupyter notebooks\n* [ipyannotations](https://github.com/janfreyberg/ipyannotations) -\u003e Image annotations in python using jupyter notebooks\n* [pigeonXT](https://github.com/dennisbakhuis/pigeonXT) -\u003e create custom image classification annotators within Jupyter notebooks\n* [jupyter-innotater](https://github.com/ideonate/jupyter-innotater) -\u003e Inline data annotator for Jupyter notebooks\n* [jupyter-bbox-widget](https://github.com/gereleth/jupyter-bbox-widget) -\u003e A Jupyter widget for annotating images with bounding boxes\n* [mapboxgl-jupyter](https://github.com/mapbox/mapboxgl-jupyter) -\u003e Use Mapbox GL JS to visualize data in a Python Jupyter notebook\n* [pylabel](https://github.com/pylabel-project/pylabel) -\u003e includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model\n* [jupyterlab-s3-browser](https://github.com/IBM/jupyterlab-s3-browser) -\u003e extension for browsing S3-compatible object storage\n* [papermill](https://github.com/nteract/papermill) -\u003e Parameterize, execute, and analyze notebooks\n* [pretty-jupyter](https://github.com/JanPalasek/pretty-jupyter) -\u003e Creates dynamic html report from jupyter notebook\n* [Fast Dash is an innovative way to deploy your Python code as interactive web apps with minimal changes](https://github.com/dkedar7/fast_dash)\n\n## Streamlit\n[Streamlit](https://streamlit.io/) is an awesome python framework for creating apps with python. These apps can be used to present ML models, and here I list resources which are EO related. Note that a component is an addon which extends Streamlits basic functionality\n* [cogviewer](https://github.com/mykolakozyr/cogviewer) -\u003e Simple Cloud Optimized GeoTIFF viewer\n* [cogcreator](https://github.com/mykolakozyr/cogcreator) -\u003e Simple Cloud Optimized GeoTIFF Creator. Generates COG from GeoTIFF files.\n* [cogvalidator](https://github.com/mykolakozyr/cogvalidator) -\u003e Simple Cloud Optimized GeoTIFF validator\n* [streamlit-image-comparison](https://github.com/fcakyon/streamlit-image-comparison) -\u003e compare images with a slider. Used in [example-app-image-comparison](https://github.com/streamlit/example-app-image-comparison)\n* [streamlit-folium](https://github.com/randyzwitch/streamlit-folium) -\u003e Streamlit Component for rendering Folium maps\n* [streamlit-keplergl](https://github.com/chrieke/streamlit-keplergl) -\u003e Streamlit component for rendering kepler.gl maps\n* [streamlit-light-leaflet](https://github.com/andfanilo/streamlit-light-leaflet) -\u003e Streamlit quick \u0026 dirty Leaflet component that sends back coordinates on map click\n* [leafmap-streamlit](https://github.com/giswqs/leafmap-streamlit) -\u003e various examples showing how to use streamlit to: create a 3D map using Kepler.gl, create a heat map, display a GeoJSON file on a map, and add a colorbar or change the basemap on a map\n* [geemap-apps](https://github.com/giswqs/geemap-apps) -\u003e build a multi-page Earth Engine App using streamlit and geemap\n* [streamlit-geospatial](https://github.com/giswqs/streamlit-geospatial) -\u003e A multi-page streamlit app for geospatial\n* [geospatial-apps](https://github.com/giswqs/geospatial-apps) -\u003e A collection of streamlit web apps for geospatial applications\n* [BirdsPyView](https://github.com/rjtavares/BirdsPyView) -\u003e convert images to top-down view and get coordinates of objects\n* [Build a useful web application in Python: Geolocating Photos](https://medium.com/spatial-data-science/build-a-useful-web-application-in-python-geolocating-photos-186122de1968) -\u003e Step by Step tutorial using Streamlit, Exif, and Pandas\n* [Wild fire detection app](https://github.com/yueureka/WildFireDetection)\n* [dvc-streamlit-example](https://github.com/sicara/dvc-streamlit-example) -\u003e how dvc and streamlit can help track model performance during R\u0026D exploration\n* [stacdiscovery](https://github.com/mykolakozyr/stacdiscovery) -\u003e Simple STAC Catalogs discovery tool\n* [SARveillance](https://github.com/MJCruickshank/SARveillance) -\u003e Sentinel-1 SAR time series analysis for OSINT use\n* [streamlit-template](https://github.com/giswqs/streamlit-template) -\u003e A streamlit app template for geospatial applications\n* [streamlit-labelstudio](https://github.com/deneland/streamlit-labelstudio) -\u003e A Streamlit component that provides an annotation interface using the LabelStudio Frontend\n* [streamlit-img-label](https://github.com/lit26/streamlit-img-label) -\u003e a graphical image annotation tool using streamlit. Annotations are saved as XML files in PASCAL VOC format\n* [Streamlit-Authenticator](https://github.com/mkhorasani/Streamlit-Authenticator) -\u003e A secure authentication module to validate user credentials in a Streamlit application\n* [prettymapp](https://github.com/chrieke/prettymapp) -\u003e Create beautiful maps from OpenStreetMap data in a webapp\n* [mapa-streamlit](https://github.com/fgebhart/mapa-streamlit) -\u003e creating 3D-printable models of the earth surface based on mapa\n* [BoulderAreaDetector](https://github.com/pszemraj/BoulderAreaDetector) -\u003e CNN to classify whether a satellite image shows an area would be a good rock climbing spot or not, deployed to streamlit app\n* [streamlit-remotetileserver](https://github.com/banesullivan/streamlit-remotetileserver) -\u003e Easily visualize a remote raster given a URL and check if it is a valid Cloud Optimized GeoTiff (COG)\n* [Streamlit_Image_Sorter](https://github.com/2320sharon/Streamlit_Image_Sorter) -\u003e Generic Image Sorter Interface for Streamlit\n* [Streamlit-Folium + Snowflake + OpenStreetMap](https://github.com/cuulee/streamlit-folium-snowflake-openstreetmap) -\u003e demonstrates the power of Snowflake Geospatial data types and queries combined with Streamlit\n* [observing-earth-from-space-with-streamlit](https://blog.streamlit.io/observing-earth-from-space-with-streamlit/) -\u003e blog post on the [SatSchool](https://github.com/Spiruel/SatSchool) app\n\n## Gradio\n* [gradio_folium](https://github.com/freddyaboulton/gradio_folium) -\u003e Display Interactive Maps Created with Folium, [example](https://huggingface.co/spaces/freddyaboulton/gradio_folium)\n* [Developing interactive web apps with gradio and leafmap](https://leafmap.org/notebooks/66_gradio/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatellite-image-deep-learning%2Fsoftware","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsatellite-image-deep-learning%2Fsoftware","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatellite-image-deep-learning%2Fsoftware/lists"}