Semantic Segmentation ecosystem for GeoSpatial Imagery
This repository is a DataPink flavor of RoboSat, including our latests developments.
- Cutting edge Computer Vision research papers implementation
- Industrial code robustness
- Several tools, you can combine together (as Lego)
- Extensible, by design
- High performances
- Minimalism as a code aesthetic
- GeoSpatial standards compliancy
- OSM and MapBox ecosystems friendly
- PyTorch based
- DataSet Quality Analysis
- Change Detection highlighter
- Features extraction and completion
- Python >= 3.6 and PyTorch >= 1.0 installed, with related Nvidia GPU drivers, CUDA and CUDNN libs.
- At least one GPU, with RAM GPU >= 6Go (default batch_size settings is targeted to 11Go).
- Libs with headers: libjpeg, libwebp, libbz2, zlib, libboost. And Qt dependancies: libsm and libxrender. On a recent Ubuntu-server, could be done with:
apt-get install build-essential libboost-python-dev zlib1g-dev libbz2-dev libjpeg-turbo8-dev libwebp-dev libsm6 libxrender1
2) Python libs Install:
python3 -m pip install -r requirements.txt
NOTA: if you want to significantly increase performances switch from Pillow to Pillow-simd.
- Move the
rspcommand to a bin directory covered by your
PATH(or update your
- Move the robosat_pink dir to somewhere covered by your
PYTHONPATH(or update your
- RoboSat.pink tutorial: from OpenData to OpenDataSet
- RoboSat.pink documentation: Extensibility by Design
- RoboSat.pink slides @PyParis 2018
- RoboSat.pink slides @FOSDEM 2019
- Historical MapBox RoboSat github directory
- Christoph Rieke's Awesome Satellite Imagery Datasets
- Mr Gloom's Awesome Semantic Segmentation
- The Lovász-Softmax loss: A tractable surrogate for the optimization of the IoU measure in neural networks
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- Deep Residual Learning for Image Recognition
- Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks
- TernausNetV2: Fully Convolutional Network for Instance Segmentation
- Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
Contributions and Services:
Pull Requests are welcome ! Feel free to send code... Don't hesitate either to initiate a prior discussion throught tickets on any implementation question.
If you want to collaborate through code production and maintenance on a long term basis, please get in touch, co-edition with an ad hoc governance can be considered.
If you want a new feature, but don't want to implement it, DataPink provide core-dev services.
Expertise and training on RoboSat.pink are also provided by DataPink.
And if you want to support the whole project, because it means for your own business, funding is also welcome.