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Contribute.md

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+If you know other challenges that provide datasets and ressources in the context of machine learning, satellite imagery and remote sensing? Please open an issue or a pull request, thank you for your contribution!
+
+You can also reach me via [Twitter](https://twitter.com/christoph_rieke).
+

README.md

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-# awesome-satellite-imagery-competitions
-List of machine learning competitions for satellite imagery and remote sensing.
+# Awesome Satellite Imagery Competitions [![Awesome](https://awesome.re/badge-flat.svg)](https://awesome.re)  
+
+List of machine learning competitions in the context of satellite imagery and remote sensing. Sorted by submission deadline.
+
+![](header_img.jpg)  
+
+- [**xView 2018 Detection Challenge**](http://xviewdataset.org) *(DIUx, Jul 2018)*  
+Object Detection (60 categories), 1 million instances, 0.3m resolution Worldview-3 imagery, 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 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), 0.4m/0.8m UAV imagery, 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, 3/8band 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, COCO data format, baseline algorithms
+
+- [**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 (RGB-NIR, 3-5m), 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 band (RGB) and 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)*  
+Development 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), multiple city aois, aerial imagery (0.3m)