This document primarily lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent Machine learning (ML, e.g. random forests, stochastic gradient descent) are also discussed, as are classical image processing techniques.
Top links
https://github.com/chrieke/awesome-satellite-imagery-datasets
A modern geospatial workflow
geospatial-machine-learning
Long list of satellite missions with example imagery
AWS datasets
Datasets
Online computing resources
Interesting dl projects
Production
Image formats and catalogues
State of the art
Online platforms for Geo analysis
Techniques
Useful references
Warning satellite image files can be LARGE, even a small data set may comprise 50 GB of imagery
Various datasets listed here and at awesome-satellite-imagery-datasets
WorldView - SpaceNet
https://en.wikipedia.org/wiki/WorldView-3
0.3m PAN, 1.24 MS, 3.7m SWIR. Off-Nadir (stereo) available.
Owned by DigitalGlobe
Intro to SpaceNet
SpaceNet dataset on AWS -> see this getting started notebook and this notebook on the off-Nadir dataset. Note -> REQUESTER PAYS
cloud_optimized_geotif here used in the 3D modelling notebook here.
Package of utilities to assist working with the SpaceNet dataset.
For more Worldview imagery see Kaggle DSTL competition.
As part of the EU Copernicus program, multiple Sentinel satellites are capturing imagery
13 bands, Spatial resolution of 10 m, 20 m and 60 m, 290 km swath, the temporal resolution is 5 days
Open access data on GCP
Paid access via sentinel-hub and python-api.
Example loading sentinel data in a notebook
Long running US program
8 bands, 15 to 60 meters, 185km swath, the temporal resolution is 16 days
Imagery on GCP, see the GCP bucket here, with imagery analysed in this notebook on Pangeo
Shuttle Radar Topography Mission (digital elevation maps)
Data - open access
Kaggle
Kaggle hosts several large satellite image datasets (> 1 GB). A list if general image datasets is here. A list of land-use datasets is here. The kaggle blog is an interesting read.
Kaggle - Amazon from space - classification challenge
https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data
3-5 meter resolution GeoTIFF images from planet Dove satellite constellation
12 classes including - cloudy, primary + waterway etc
1st place winner interview - used 11 custom CNN
Kaggle - DSTL - segmentation challenge
https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
Rating - medium, many good examples (see the Discussion as well as kernels), but as this competition was run a couple of years ago many examples use python 2
WorldView 3 - 45 satellite images covering 1km x 1km in both 3 (i.e. RGB) and 16-band (400nm - SWIR) images
10 Labelled classes include Buildings, Road, Trees, Crops, Waterway, Vehicles
Interview with 1st place winner who used segmentation networks - 40+ models, each tweaked for particular target (e.g. roads, trees)
Deepsense 4th place solution
My analysis here
https://www.kaggle.com/c/airbus-ship-detection/overview
Rating - medium, most solutions using deep-learning, many kernels, good example kernel.
I believe there was a problem with this dataset, which led to many complaints that the competition was ruined.
Kaggle - Draper - place images in order of time
https://www.kaggle.com/c/draper-satellite-image-chronology/data
Rating - hard. Not many useful kernels.
Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned.
Kaggle interviews for entrants who used XGBOOST and a hybrid human/ML approach
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 Example notebook
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
Satellite + loan data -> https://www.kaggle.com/reubencpereira/spatial-data-repo
There are a variety of datasets suitable for land classification problems.
UC Merced
http://weegee.vision.ucmerced.edu/datasets/landuse.html
This is a 21 class land use image dataset meant for research purposes.
There are 100 RGB TIFF images for each class
Each image measures 256x256 pixels with a pixel resolution of 1 foot
AWS datasets
Landsat -> free viewer at remotepixel and libra
Optical, radar, segmented etc. https://aws.amazon.com/earth/
SpaceNet - WorldView-3 and article here. Also example semantic segmentation using Raster Vision
Several people have uploaded datasets to Quilt
https://developers.google.com/earth-engine/
Various imagery and climate datasets, including Landsat & Sentinel imagery
Python API but all compute happens on Googles servers
Weather Datasets
UK met-odffice
->https://www.metoffice.gov.uk/datapoint
NASA (make request and emailed when ready)
-> https://search.earthdata.nasa.gov
NOAA (requires BigQuery)
-> https://www.kaggle.com/noaa/goes16/home
Time series weather data for several US cities
->https://www.kaggle.com/selfishgene/historical-hourly-weather-data
Generally a GPU is required for DL, and this section lists Jupyter environments with GPU available. There is a good overview of online Jupyter envs on the fast.at site.
Google Colab
Collaboratory notebookswith 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.
Tensorflow available & pytorch can be installed, useful articles
Kaggle - also Google!
Free to use
GPU Kernels - may run for 1 hour
Tensorflow, pytorch & fast.ai available
Advantage that many datasets are already available
Read
Floydhub
https://www.floydhub.com/
Pricing -> https://www.floydhub.com/pricing
Free plan allows 1 job and 10GB storage, but pay for GPU.
Cloud GPUs (AWS backend)
Tensorboard
Version Control for DL
Deploy Models as REST APIs
Public Datasets
Clouderizer
https://clouderizer.com/
Clouderizer $5 month for 200 hours (Robbie plan)
Run projects locally, on cloud or both.
SSH terminal, Jupyter Notebooks and Tensorboard are securely accessible from Clouderizer Web Console.
Paperspace
https://www.paperspace.com/
1-Click Jupyter Notebooks, GPU on demand
Pay as you go -> https://www.paperspace.com/pricing
Python API
Crestle
https://www.crestle.com/
Pricing -> https://www.crestle.com/pricing
Min plan is $5 per month with 200 hours per month. Pay $0.59/hour for GPU and storage $0.014/GB/day
Cloud GPU & persistent file store
Fast.ai lessons pre-installed
https://www.azavea.com/projects/raster-vision/
An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets.
Accessible through the Raster Foundry
Example use cases on open data
https://github.com/mapbox/robosat
Generic ecosystem for feature extraction from aerial and satellite imagery.
RoboSat.Pink
A fork of robotsat
https://github.com/datapink/robosat.pink
https://github.com/trailbehind/DeepOSM
Train a deep learning net with OpenStreetMap features and satellite imagery.
DeepNetsForEO - segmentation
https://github.com/nshaud/DeepNetsForEO
Uses SegNET for working on remote sensing images using deep learning.
Skynet-data
https://github.com/developmentseed/skynet-data
Data pipeline for machine learning with OpenStreetMap
Basic https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html with code here
Advanced https://www.pyimagesearch.com/2018/01/29/scalable-keras-deep-learning-rest-api/
https://github.com/galiboo/olympus
https://www.tensorflow.org/serving/
Official version is python 2 but python 3 build here
Another approach is to use Docker
TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. Multiple models, or indeed multiple versions of the same model, can be served simultaneously. TensorFlow Serving comes with a scheduler that groups individual inference requests into batches for joint execution on a GPU
Floydhub
Allows exposing model via rest API
modeldepot
https://modeldepot.io
ML models hosted
Image formats & catalogues
We certainly want to consider cloud optimised GeoTiffs https://www.cogeo.org/
https://terria.io/ for pretty catalogues
Remote pixel
Sentinel-hub eo-browser
Large datasets may come in HDF5 format, can view with -> https://www.hdfgroup.org/downloads/hdfview/
Climate data is often in netcdf format, which can be opened using xarray
The xarray docs list a number of ways that data can be stored and loaded.
Specification describing the layout of a catalogue comprising of static files. 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.
An initiative of https://www.radiant.earth/ in particular https://github.com/cholmes
Spec at https://github.com/radiantearth/stac-spec
Browser at https://github.com/radiantearth/stac-browser
Talk at https://docs.google.com/presentation/d/1O6W0lMeXyUtPLl-k30WPJIyH1ecqrcWk29Np3bi6rl0/edit#slide=id.p
Example catalogue at
https://landsat-stac.s3.amazonaws.com/catalog.json
Chat https://gitter.im/SpatioTemporal-Asset-Catalog/Lobby
Several useful repos on https://github.com/sat-utils
What are companies doing?
Overall trend to using AWS S3 backend for image storage. There are a variety of tools for exploring and having teams collaborate on data on S3, e.g. T4.
Bucking the trend, Descartes & Airbus are using a google backend -> checkout gcsts for google cloud storage sile-system
Just speculating, but a serverless pipeline appears to be where companies are headed for routine compute tasks, whilst providing a Jupyter notebook approach for custom analysis.
Traditional data formats aren't designed for processing, so new standards are developing such as cloud optimised geotiffs and zarr
This article discusses some of the available platforms -> TLDR Pangeo rocks, but must BYO imagery
Pangeo - open source resources for parallel processing using Dask and Xarray http://pangeo.io/index.html
Airbus Sandbox -> will provide access to imagery
Descartes Labs-> access to EO imagery from a variety of providers via python API -> not clear which imagery is available (Airbus + others?) or pricing
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. Tutorial notebooks here. Only Sentinel-2 and Landsat data on free tier.
Planet have a Jupyter notebook platform which can be deployed locally and requires an API key (14 days free). They have a python wrapper (2.7..) to their rest API. No price after 14 day trial.
Techniques
This section explores the different techniques (DL, ML & classical) 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).
Very common problem, assign land classification to a pixel based on pixel value, can be addressed via simple sklearn cluster algorithm or deep learning.
Land use is related to classification, but we are trying to detect a scene, e.g. housing, forestry. I have tried CNN -> See my notebooks
Land Use Classification using Convolutional Neural Network in Keras
Sea-Land segmentation using DL
Pixel level segmentation on Azure
Deep Learning-Based Classification of Hyperspectral Data
A U-net based on Tensorflow for objection detection (or segmentation) of satellite images - DSTL dataset but python 2.7
Pixel-wise classification
Instance segmentation with keras - links to satellite examples
Monitor water levels, coast lines, size of urban areas, wildfire damage. Note, clouds change often too..!
Using PCA (python 2, requires updating) -> https://appliedmachinelearning.blog/2017/11/25/unsupervised-changed-detection-in-multi-temporal-satellite-images-using-pca-k-means-python-code/
Using CNN -> https://github.com/vbhavank/Unstructured-change-detection-using-CNN
Siamese neural network to detect changes in aerial images
https://www.spaceknow.com/
LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Random Forest
Change Detection in 3D: Generating Digital Elevation Models from Dove Imagery
Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks
PySAR - InSAR (Interferometric Synthetic Aperture Radar) timeseries analysis in python
Wikipedia article on registration -> register for change detection or image stitching
Traditional approach -> define control points, employ RANSAC algorithm
Phase correlation used to estimate the translation between two images with sub-pixel accuracy, useful for allows accurate registration of low resolution imagery onto high resolution imagery, or 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. Applied to Landsat images here.
A typical task is detecting boats on the ocean, which should be simpler than land based challenges owing to blank background in images, but is still challenging and no convincing robust solutions available.
Intro articles here and here.
DigitalGlobe article - they use a combination classical techniques (masks, erodes) to reduce the search space (identifying water via NDWI which requires SWIR) then apply a binary DL classifier on candidate regions of interest. They deploy the final algo as a task on their GBDX platform. They propose that in the future an R-CNN may be suitable for the whole process.
Planet use non DL felzenszwalb algorithm to detect ships
Segmentation of buildings on kaggle
Identifying Buildings in Satellite Images with Machine Learning and Quilt -> NDVI & 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”.
Deep learning for satellite imagery via image segmentation
Building Extraction with YOLT2 and SpaceNet Data
Find sports fields using Mask R-CNN and overlay on open-street-map
A subset of the object detection problem, but surprisingly challenging
From this article on sentinelhubthere 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.
DL..
https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-1-ec5c5cd3cd2
https://modeldepot.io/joe/vdsr-for-super-resolution
Image fusion of low res multispectral with high res pan band. Several algorithms described in the ArcGIS docs, with the simplest being taking the mean of the pan and RGB pixel value.
Does not require DL, classical algos suffice, see this notebook and this kaggle kernel
https://github.com/mapbox/rio-pansharpen
Wikipedia DEM article and phase correlation article
Intro to depth from stereo
Map terrain from stereo images to produce a digital elevation model (DEM) -> high resolution & paired images required, typically 0.3 m, e.g. Worldview or GeoEye.
Process of creating a DEM here and here.
ArcGIS can generate DEMs from stereo images
https://github.com/MISS3D/s2p -> produces elevation models from images taken by high resolution optical satellites -> demo code on https://gfacciol.github.io/IS18/
Automatic 3D Reconstruction from Multi-Date Satellite Images
Semi-global matching with neural networks
Predict the fate of glaciers
monodepth - Unsupervised single image depth prediction with CNNs
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Terrain and hydrological analysis based on LiDAR-derived digital elevation models (DEM) - Python package
Phase correlation in scikit-image
Reconstructing 3D buildings from aerial LiDAR with Mask R-CNN)
Simple band math ndvi = np.true_divide((ir - r), (ir + r)) but challenging due to the size of the imagery.
Example notebook local
Landsat data in cloud optimised format analysed for NVDI with medium article here.
Removing speckle noise from Sentinel-1 SAR using a CNN
A dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset, which contains around 250K corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data. Data download, Paper
Style transfer - see the world in a new way
QGIS- Create, edit, visualise, analyse and publish geospatial information. Python scripting and plugins.
Orfeo toolbox - remote sensing toolbox with python API (just a wrapper to the C code). Do activites such as pansharpening, ortho-rectification, image registration, image segmentation & classification. Not much documentation.
QUICK TERRAIN READER - view DEMS, Windows
torchvision-enhance -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file,...
dl-satellite-docker -> docker files for geospatial analysis, including tensorflow, pytorch, gdal, xgboost...
Useful References
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0
https://github.com/taspinar/sidl/blob/master/notebooks/2_Detecting_road_and_roadtypes_in_sattelite_images.ipynb
Geonotebooks with Docker container
Sentinel NetCDF data
Open Data Cube - serve up cubes of data https://www.opendatacube.org/
Process Satellite data using AWS Lambda functions
OpenDroneMap- generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.
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