The research paper summarizing the corresponding benchmark and associated solutions can be found here : Deep Learning for Understanding Satellite Imagery: An Experimental Survey
This repository contains the details of implementation of the Baseline submission using Mask RCNN which obtains a score of [AP(IoU=0.5)=0.697 ; AR(IoU=0.5)=0.479] for the crowdAI Mapping Challenge.
git clone https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn
cd crowdai-mapping-challenge-mask-rcnn
# Please ensure that you use python3.6
pip install -r requirements.txt
python setup.py install
Please follow the instructions on the relevant notebooks for the training, prediction and submissions.
- Training
- Prediction and Submission (pre-trained weights for baseline submission included)
@article{mohanty2020deep,
title={Deep Learning for Understanding Satellite Imagery: An Experimental Survey},
author={Mohanty, Sharada Prasanna and Czakon, Jakub and Kaczmarek, Kamil A and Pyskir, Andrzej and Tarasiewicz, Piotr and Kunwar, Saket and Rohrbach, Janick and Luo, Dave and Prasad, Manjunath and Fleer, Sascha and others},
journal={Frontiers in Artificial Intelligence},
volume={3},
year={2020},
publisher={Frontiers Media SA}
}
@misc{crowdAIMappingChallengeBaseline2018,
author = {Mohanty, Sharada Prasanna},
title = {CrowdAI Mapping Challenge 2018 : Baseline with Mask RCNN},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn}},
commit = {bac1cf19adbc9d078122c6933da6f808c4ee590d}
}
This repository heavily reuses code from the amazing tensorflow Mask RCNN implementation by @waleedka. Many thanks to all the contributors of that project. You are encouraged to checkout https://github.com/matterport/Mask_RCNN for documentation on many other aspects of this code.
Sharada Mohanty [email protected]