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This document lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent classical Machine learning (ML, e.g. random forests) are also discussed, as are classical image processing techniques.

Table of contents

Top links


Warning satellite image files can be LARGE, even a small data set may comprise 50 GB of imagery






UC Merced



Kaggle hosts over 60 satellite image datasets, search results here. The kaggle blog is an interesting read.

Kaggle - Amazon from space - classification challenge

Kaggle - DSTL - segmentation challenge

Kaggle - Airbus Ship Detection Challenge

Kaggle - Draper - place images in order of time

Kaggle - Deepsat - classification challenge

Not satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in .mat Matlab format. JPEG?

  • Imagery source
  • Sat4 500,000 image patches covering four broad land cover classes - barren land, trees, grassland and a class that consists of all land cover classes other than the above three
  • Sat6 405,000 image patches each of size 28x28 and covering 6 landcover classes - barren land, trees, grassland, roads, buildings and water bodies.
  • Deep Gradient Boosted Learning article

Kaggle - Understanding Clouds from Satellite Images

In this challenge, you will build a model to classify cloud organization patterns from satellite images.

Kaggle - miscellaneous

Tensorflow datasets

  • There are a number of remote sensing datasets
  • resisc45 - RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
  • eurosat - EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
  • bigearthnet - The BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels.

AWS datasets


Google Earth Engine (GEE)

Radiant Earth

FAIR1M ‘world’s largest satellite image database’

DEM (digital elevation maps)

  • Shuttle Radar Topography Mission: data - open access
  • Copernicus Digital Elevation Model (DEM) on S3, represents the surface of the Earth including buildings, infrastructure and vegetation. Data is provided as Cloud Optimized GeoTIFFs. link

Weather Datasets

Time series datasets

  • BreizhCrops -> A Time Series Dataset for Crop Type Mapping

UAV & Drone datasets

Synthetic data

Interesting deep learning projects


  • TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch

Raster Vision by Azavea

DeepNetsForEO (no activity since 2019)

Skynet-data (no activity since 2018)

RoboSat (no longer maintained)

  • Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
  • robosat-jupyter-notebook -> walks through all of the steps in an excellent blog post on the Robosat feature extraction and machine learning pipeline.
  • Note there is/was fork of Robosat, originally named, and subsequently although this appears to be dead/archived

DeepOSM (no activity since 2017)


This section explores the different deep and machine learning techniques people are applying to common problems in satellite imagery analysis. Classification problems are the most simply addressed via DL, object detection is harder, and cloud detection harder still (niche interest). Note that almost all imagery data on the internet is in RGB format, and common techniques designed for working with this 3 band imagery may fail or need significant adaptation to work with multiband data (e.g. 13-band Sentinel 2).

Land classification - RGB data

Assign a label to an image, e.g. this is an image of a forest. With RGB imagery 'off the shelf' neural net architectures can be applied

Land classification - Hyperspectral data

Custom neural net architectures are required for this high dimensional imagery

Semantic segmentation

Whilst classification will assign a label to a whole image, semantic segmentation will assign a label to each pixel

Change detection

Monitor water levels, coast lines, size of urban areas, wildfire damage. Note, clouds change often too..!

Object detection

A good introduction to the challenge of performing object detection on aerial imagery is given in this paper. In summary, images are large and objects may comprise only a few pixels, easily confused with random features in background. An example task is detecting boats on the ocean, which should be simpler than land based detection owing to the relatively blank background in images, but is still challenging.

Object detection - buildings

Object detection - boats, planes & vehicles

Object detection - trees & green areas

Cloud detection

  • From this article on sentinelhub there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds. In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper here), but state that this requires too much compute resources.
  • This article compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method.
  • Segmentation of Clouds in Satellite Images Using Deep Learning -> a U-Net is employed to interpret and extract the information embedded in the satellite images in a multi-channel fashion, and finally output a pixel-wise mask indicating the existence of cloud.
  • Cloud Detection in Satellite Imagery compares FPN and CheapLab architectures on Sentinel-2 L1C and L2A imagery

Wealth and economic activity measurement

The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys


Super-resolution imaging is a class of techniques that enhance the resolution of an imaging system. For an introduction to this topic read this excellent article on the OpenCV dnn_superres module

Image-to-image translation using GANS

Generative Adversarial Networks, or GANS, can be used to translate images, e.g. from SAR to RGB.

SAR & Denoising

I group these together since I most often see denoising in the context of SAR imagery.

ML best practice & general techniques

Miscellaneous (generally) non ML Techniques


Image fusion of low res multispectral with high res pan band.

NVDI - vegetation index

Image registration

Image registration is the process of transforming different sets of data into one coordinate system. Typical use is overlapping images taken at different times or with different cameras.

  • Wikipedia article on registration -> register for change detection or image stitching
  • Traditional approach -> define control points, employ RANSAC algorithm
  • Phase correlation is used to estimate the translation between two images with sub-pixel accuracy. Can be used for accurate registration of low resolution imagery onto high resolution imagery, or to register a sub-image on a full image -> Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects.
  • cnn-registration -> A image registration method using convolutional neural network features written in Python2, Tensorflow 1.5

Terrain mapping, Lidar & DEMs

Measure surface contours.

Image formats, data management and catalogues

Cloud Optimised GeoTiff (COG)

A Cloud Optimized GeoTIFF (COG) is a regular GeoTIFF file with an internal organization that enables more efficient workflows on the cloud. In particular they support HTTP range requests, enabling downloading of specific tiles rather than the full file. COGs work normally in GIS software such as QGIS, but are larger than regular GeoTIFFs

STAC - SpatioTemporal Asset Catalog specification

The STAC specification provides a common metadata specification, API, and catalog format to describe geospatial assets, so they can more easily indexed and discovered. The aim is that the catalogue is crawlable so it can be indexed by a search engine and make imagery discoverable, without requiring yet another API interface. A good place to start is to view the Planet Disaster Data catalogue which has the catalogue source on Github and uses the stac-browser

State of the art

What are companies doing?

Online platforms for Geo analysis

  • This article discusses some of the available platforms
  • Pangeo -> There is no single software package called “pangeo”; rather, the Pangeo project serves as a coordination point between scientists, software, and computing infrastructure. Includes open source resources for parallel processing using Dask and Xarray. Pangeo recently announced their 2.0 goals: pivoting away from directly operating cloud-based JupyterHubs, and towards eductaion and research
  • Airbus Sandbox -> will provide access to imagery
  • Descartes Labs -> access to EO imagery from a variety of providers via python API
  • DigitalGlobe have a cloud hosted Jupyter notebook platform called GBDX. Cloud hosting means they can guarantee the infrastructure supports their algorithms, and they appear to be close/closer to deploying DL.
  • Planet have a Jupyter notebook platform which can be deployed locally.
  • -> hosted Jupyter environment with many features for working with EO data
  • -> data & platform for EO analytics in Jupyter env, paid
  • Unfolded Studio -> next generation geospatial analytics and visualization platform building on open source geospatial technologies including, and H3. Processing is down browser side enabling excellent performance. Rasters support added April 2021
  • up42 is a developer platform and marketplace, offering all the building blocks for powerful, scalable geospatial products
  • Microsoft Planetary Computer -> direct Google Earth Engine competitor in the making?

Free online computing resources

Generally a GPU is required for DL, and this section lists a couple of free Jupyter environments with GPU available. There is a good overview of online Jupyter development environments on the site. I personally use Colab with data hosted on Google Drive

Google Colab

  • Collaboratory notebooks with GPU as a backend for free for 12 hours at a time. Note that the GPU may be shared with other users, so if you aren't getting good performance try reloading.
  • Also a pro tier for $10 a month ->
  • Tensorflow pytorch can be installed

Kaggle - also Google!

  • Free to use
  • GPU Kernels - may run for 1 hour
  • Tensorflow, pytorch & available
  • Advantage that many datasets are already available


For an overview on serving deepl learning models checkout Practical-Deep-Learning-on-the-Cloud.

Rest API on dedicated server

A conceptually simple approach to serving up deep learning model inference code is to wrap it in a rest API. That can be implemented in python (flask or FastAPI), and hosted on a dedicated server e.g. EC2 instance

Framework specific serving options


chip-n-scale-queue-arranger by developmentseed

Useful paid software

Useful open source software

A note on licensing: The two general types of licenses for open source are copyleft and permissive. Copyleft requires that subsequent derived software products also carry the license forward, e.g. the GNU Public License (GNU GPLv3). For permissive, options to modify and use the code as one please are more open, e.g. MIT & Apache 2. Checkout

GDAL & Rasterio

  • So improtant this pair gets their own section. GDAL is THE command line tool for reading and writing raster and vector geospatial data formats. If you are using python you will probably want to use Rasterio which provides a pythonic wrapper for GDAL
  • GDAL and on twitter
  • GDAL is a dependency of Rasterio and can be difficult to build and install. I recommend using conda, brew (on OSX) or docker in these situations
  • GDAL docker quickstart: docker pull osgeo/gdal then docker run --rm -v $(pwd):/data/ osgeo/gdal gdalinfo /data/cog.tiff
  • Even Rouault maintains GDAL, please consider sponsoring him
  • Rasterio -> reads and writes GeoTIFF and other raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON. There are a variety of plugins that extend Rasterio functionality.
  • rio-cogeo -> Cloud Optimized GeoTIFF (COG) creation and validation plugin for Rasterio.
  • rioxarray -> geospatial xarray extension powered by rasterio
  • aws-lambda-docker-rasterio -> AWS Lambda Container Image with Python Rasterio for querying Cloud Optimised GeoTiffs. See this presentation
  • godal -> golang wrapper for GDAL
  • Write rasterio to xarray
  • Loam: A Client-Side GDAL Wrapper for Javascript

Python general utilities

  • PyShp -> The Python Shapefile Library (PyShp) reads and writes ESRI Shapefiles in pure Python
  • s2p -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos
  • EarthPy -> A set of helper functions to make working with spatial data in open source tools easier. readExploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy
  • pygeometa -> provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files
  • pesto -> PESTO is designed to ease the process of packaging a Python algorithm as a processing web service into a docker image. It contains shell tools to generate all the boiler plate to build an OpenAPI processing web service compliant with the Geoprocessing-API. By Airbus Defence And Space
  • GEOS -> Google Earth Overlay Server (GEOS) is a python-based server for creating Google Earth overlays of tiled maps. Your can also display maps in the web browser, measure distances and print maps as high-quality PDF’s.
  • GeoDjango intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data. Some features of GDAL are supported.
  • rasterstats -> summarize geospatial raster datasets based on vector geometries
  • turfpy -> a Python library for performing geospatial data analysis which reimplements turf.js

Python low level numerical & data formats

  • xarray -> N-D labeled arrays and datasets. Read Handling multi-temporal satellite images with Xarray. Checkout xarray_leaflet for tiled map plotting
  • xarray-spatial -> Fast, Accurate Python library for Raster Operations. Implements algorithms using Numba and Dask, free of GDAL
  • Geowombat -> geo-utilities applied to air- and space-borne imagery, uses Rasterio, Xarray and Dask for I/O and distributed computing with named coordinates
  • NumpyTiles -> a specification for providing multiband full-bit depth raster data in the browser
  • Zarr -> Zarr is a format for the storage of chunked, compressed, N-dimensional arrays. Zarr depends on NumPy

Python image handling and manipulation

  • Pillow is the Python Imaging Library -> this will be your go-to package for image manipulation in python
  • opencv-python is pre-built CPU-only OpenCV packages for Python
  • 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.
  • tifffile -> Read and write TIFF files
  • xtiff -> A small Python 3 library for writing multi-channel TIFF stacks
  • geotiff -> A noGDAL tool for reading and writing geotiff files
  • image_slicer -> Split images into tiles. Join the tiles back together.
  • tiler -> split images into tiles and merge tiles into a large image
  • felicette -> Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data.
  • imagehash -> Image hashes tell whether two images look nearly identical.
  • xbatcher -> 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.

Python deep learning toolsets

  • torchvision-enhance -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file
  • DeepHyperX -> A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets.
  • image-super-resolution -> Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Python data discover and ingestion

  • landsat_ingestor -> Scripts and other artifacts for landsat data ingestion into Amazon public hosting
  • satpy - a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats

Python graphing and visualisation

  • hvplot -> A high-level plotting API for the PyData ecosystem built on HoloViews. Allows overlaying data on map tiles, see Exploring USGS Terrain Data in COG format using hvPlot
  • Pyviz examples include several interesting geospatial visualisations
  • napari -> napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools. Note that to view a 3GB COG I had to install the napari-tifffile-reader plugin.
  • pixel-adjust -> Interactively select and adjust specific pixels or regions within a single-band raster. Built with rasterio, matplotlib, and panel.
  • Plotly Dash can be used for making interactive dashboards
  • folium -> a python wrapper to the excellent leaflet.js which makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. Also checkout the streamlit-folium component for adding folium maps to your streamlit apps
  • ipyearth -> An IPython Widget for Earth Maps
  • geopandas-view -> Interactive exploration of GeoPandas GeoDataFrames
  • geogif -> Turn xarray timestacks into GIFs

Python cluster computing with Dask

Algorithms in python

  • WaterDetect -> an end-to-end algorithm to generate open water cover mask, specially conceived for L2A Sentinel 2 imagery. It can also be used for Landsat 8 images and for other multispectral clustering/segmentation tasks.
  • GatorSense Hyperspectral Image Analysis Toolkit -> This repo contains algorithms for Anomaly Detectors, Classifiers, Dimensionality Reduction, Endmember Extraction, Signature Detectors, Spectral Indices
  • arosics -> Perform automatic subpixel co-registration of two satellite image datasets based on an image matching approach
  • detectree -> Tree detection from aerial imagery
  • pylandstats -> compute landscape metrics

Tools for image annotation

If you are performing object detection you will need to annotate images with bounding boxes. Check that your annotation tool of choice supports large image (likely geotiff) files, as not all will. Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework. There are both closed and open source tools for creating and converting annotation formats.

  • A long list of tools is here
  • GroundWork is designed for annotating and labeling geospatial data like satellite imagery, from Azavea
  • Labelme Image Annotation for Geotiffs -> uses Labelme
  • Label Maker -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an .npz file for use in machine learning training.
  • CVAT is worth investigating, and have an open issue to support large TIFF files. This article on Roboflow gives a good intro to CVAT.
  • Deep Block is a general purpose AI platform that includes a tool for COCOJSON export for aerial imagery. Checkout this video
  • AWS supports image annotation via the Rekognition Custom Labels console
  • Roboflow can be used to convert between annotation formats
  • Other annotation tools include (web UI), rectlabel (OSX desktop app) and VoTT
  • Label Studio is a multi-type data labeling and annotation tool with standardized output format, webpage at
  • Deeplabel is a cross-platform tool for annotating images with labelled bounding boxes. Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model.
  • Alturos.ImageAnnotation is a collaborative tool for labeling image data on S3 for yolo
  • rectlabel is a desktop app for MacOS to label images for bounding box object detection and segmentation
  • pigeonXT can be used to create custom image classification annotators within Jupyter notebooks
  • ipyannotations -> Image annotations in python using jupyter notebooks
  • diffgram supports cloud backends

Movers and shakers on Github

  • Adam Van Etten is doing interesting things in object detection and segmentation
  • Andrew Cutts cohosts the Scene From Above podcast and has many interesting repos
  • Ankit Kariryaa published a recent nature paper on tree detection
  • Chris Holmes is doing great things at Planet
  • Christoph Rieke maintains a very popular imagery repo and has published his thesis on segmentation
  • Even Rouault maintains several of the most critical tools in this domain such as GDAL, please consider sponsoring him
  • Jake Shermeyer many interesting repos
  • Mort Canty is an expert in change detection
  • Nicholas Murray is an Australia-based scientist with a focus on delivering the science necessary to inform large scale environmental management and conservation
  • Qiusheng Wu is an Assistant Professor in the Department of Geography at the University of Tennessee
  • Robin Wilson is a former academic who is very active in the satellite imagery space

Companies on Github

For a full list of companies, on and off Github, checkout awesome-geospatial-companies. The following lists companies with interesting Github profiles.



Online communities


Neural nets in space

Processing on satellite allows less data to be downlinked. E.g. super-resolution image might take 4-8 images to generate, then a single image is downlinked.

About the author

My background is in optical physics, and I hold a PhD from Cambridge on the topic of localised surface Plasmons. Since academia I have held a variety of roles, including doing research at Sharp Labs Europe, developing optical systems at Surrey Satellites (SSTL), and working at an IOT startup. It was whilst at SSTL that I started this repository as a personal resource. Over time I have steadily gravitated towards data analytics and software engineering with python, and I now work as a senior data scientist at Satellite Vu. Please feel free to connect with me on Twitter & LinkedIn, and please do let me know if this repository is useful to your work.

Linkedin: robmarkcole Twitter Follow