# awesome-satellite-imagery-competitions **Repository Path**: mirrors_qubvel/awesome-satellite-imagery-competitions ## Basic Information - **Project Name**: awesome-satellite-imagery-competitions - **Description**: List of machine learning competitions for satellite imagery and remote sensing. - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2020-08-18 - **Last Updated**: 2026-05-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Awesome Satellite Imagery Competitions [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re) List of machine learning competitions for satellite imagery and remote sensing. Sorted by submission deadline. ![](header_img.jpg) - [**Agricultural Crop Cover Classification Challenge**](https://crowdanalytix.com/contests/agricultural-crop-cover-classification-challenge) *(CrowdANALYTIX, Jul 2018)* Semantic Segmentation (2 main categories: Corn, Soybeans), Open data, [Landsat 8](https://earthexplorer.usgs.gov) imagery (30m) and [Cropland Data Layer](https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php) as ground truth. - [**DOTA: Large-scale Dataset for Object Detection in Aerial Images**](https://captain-whu.github.io/DOTA/index.html) *(Wuhan University et al.)* Object Detection (15 categories), 188k instances, Google Earth image chips, Faster-RCNN baseline model (MXNet), DOTA development kit, Academic use only - [**xView 2018 Detection Challenge**](http://xviewdataset.org) *(DIUx, Jul 2018)* Object Detection (60 categories), 1 million instances, Worldview-3 imagery (0.3m), COCO data format, pre-trained Tensorflow and Pytorch baseline models - [**CrowdAI Mapping Challenge**](https://www.crowdai.org/challenges/mapping-challenge) *(Humanity & Inclusion NGO, May 2018)* Semantic/Instance Segmentation (buildings), RGB sat. imagery, COCO data format - [**Open AI Challenge: Aerial Imagery of South Pacific Islands**](https://werobotics.org/blog/2018/01/10/open-ai-challenge/) *(Worldbank, May 2018)* Object Detection (4 tree species), Semantic Segmentation (2 road types), RGB UAV imagery (0.4/0.8m), multiple AOIs in Tonga - [**DEEPGLOBE - 2018 Satellite Challange**](http://deepglobe.org/index.html) *(CVPR, Apr 2018)* 3 challenge tracks: Road Extraction, Building Detection, Land cover classification - [**IEEE Data Fusion Contest 2018**](http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/) *(IEEE, -Mar 2018)* Land cover classification (20 categories) by fusing data three sources: Multispectral LiDAR, Hyperspectral (1m), RGB imagery (0.05m) - [**Spacenet challenge - Round 3**](https://spacenetchallenge.github.io/Competitions/Competition3.html) *(CosmiQ Works, Radiant Solutions, NVIDIA, Feb 2018)* Road Extraction, multiple city aois, 3(RGB)/8band Worldview-3 imagery (0.3m), SpaceNet Challenge Asset Library - [**Statoil/C-CORE Iceberg Classifier Challenge**](https://www.kaggle.com/c/statoil-iceberg-classifier-challenge) *(Statoil/C-CORE, Jan 2018)* Image Recognition (Predict if image chip contains ship or iceberg), 2-band HH/HV polarization SAR imagery, Kaggle kernels - [**Functional Map of the World Challenge**](https://www.iarpa.gov/challenges/fmow.html) *(IARPA, Dec 2017)* Object Detection (63 categories), 1 million instances, 4/8 band sat. imagery, COCO data format, baseline models - [**Urban 3D Challenge**](https://www.topcoder.com/urban3d) *(USSOCOM, Dec 2017)* Building footprint detection, RGB orthophotos (0.5m), 3 cities, SpaceNet Challenge Asset Library - [**NIST DSE Plant Identification with NEON Remote Sensing Data**](https://www.ecodse.org) *(inria.fr, Oct 2017)* Extraction of tree position, species and crown parameters, hyperspectral (1m) & RGB imagery (0.25m), LiDAR point cloud and canopy height model - [**Planet: Understanding the Amazon from Space**](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space) *(Planet, Jul 2017)* Image recognition (Predict 1 of 13 land cover and 1 of 4 cloud condition labels per image chip), Amazonian rainforest, 4 band sat. imagery (RGB-NIR, 3-5m), Kaggle kernels - [**TiSeLaC : Time Series Land Cover Classification Challenge**](https://sites.google.com/site/dinoienco/tiselc) *(UMR TETIS, Jul 2017)* Land cover time series classification (9 categories), Landsat-8 (30m, 23 images time series, 10 band features), Reunion island - [**NOAA Fisheries Steller Sea Lion Population Count**](https://www.kaggle.com/c/noaa-fisheries-steller-sea-lion-population-count) *(NOAA, Jun 2017)* Object Detection (5 sea lion categories), ~ 80k instances, ~ 1k aerial images, Kaggle kernels - [**Spacenet challenge - Round 2**](https://spacenetchallenge.github.io/Competitions/Competition2.html) *(CosmiQ Works, Radiant Solutions, NVIDIA, May 2017)* Building extraction, multiple city aois, 3/8band Worldview-3 imagery (0.3m), SpaceNet Challenge Asset Library - [**DSTL Satellite Imagery Feature Detection challenge**](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection) *(Dstl, Feb 2017)* Object Detection & Classification (10 categories). 3/16 band Worldview 3 imagery (0.3m - 7.5m), Kaggle kernels - [**Spacenet challenge - Round 1**](https://spacenetchallenge.github.io/Competitions/Competition1.html) *(CosmiQ Works, Radiant Solutions, NVIDIA, Jan 2017)* Building extraction, Rio de Janeiro, 3/8band Worldview-3 imagery (0.5m mosaic), SpaceNet Challenge Asset Library - [**Multi-View Stereo 3D Mapping Challenge**](https://www.iarpa.gov/challenges/3dchallenge.html) *(IARPA, Nov 2016)* Develop of a Multi-View Stereo (MVS) 3D mapping algorithm that can convert high-resolution satellite images to 3D point clouds - [**Draper Satellite Image Chronology**](https://www.kaggle.com/c/draper-satellite-image-chronology) *(Draper, Jun 2016)* Predict the chronological order of images taken at the same locations over 5 days, Kaggle kernels - [**Inria Aerial Image Labeling**](https://project.inria.fr/aerialimagelabeling/contest/) *(inria.fr)* Semantic Segmentation (buildings), RGB aerial imagery (0.3m), 5 cities