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Computer Vision framework for GeoSpatial Imagery
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Computer Vision framework for GeoSpatial imagery buildings segmentation from Imagery


  • DataSet Quality Analysis
  • Change Detection highlighter
  • Features extraction and completion

Main Features:

  • Provides several command line tools, you can combine together to build your own workflow
  • Follows geospatial standards to ease interoperability and data preparation
  • Build-in cutting edge Computer Vision model, Data Augmentation and Loss implementations (and allows to replace by your owns)
  • Support either RGB and multibands imagery, and allows Data Fusion
  • Web-UI tools to easily display, hilight or select results (and allow to use your own templates)
  • High performances
  • Eeasily extensible by design

Draw me



Config file:


  • rsp cover Generate a tiles covering, in csv format: X,Y,Z
  • rsp download Downloads tiles from a remote server (XYZ, WMS, or TMS)
  • rsp extract Extracts GeoJSON features from OpenStreetMap .pbf
  • rsp rasterize Rasterize vector features (GeoJSON or PostGIS), to raster tiles
  • rsp subset Filter images in a slippy map dir using a csv tiles cover
  • rsp tile Tile raster coverage
  • rsp train Trains a model on a dataset
  • rsp export Export a model to ONNX or Torch JIT
  • rsp predict Predict masks, from given inputs and an already trained model
  • rsp compare Compute composite images and/or metrics to compare several XYZ dirs
  • rsp vectorize Extract simplified GeoJSON features from segmentation masks
  • rsp info Print version informations

Presentations slides:


With PIP:

pip3 install

With Ubuntu 19.10, from scratch:

# [mandatory]
sudo sh -c "apt update && apt install -y build-essential python3-pip
pip3 install && export PATH=$PATH:~/.local/bin

# NVIDIA GPU Drivers [needed for rsp train]
sudo sh -a -q --ui=none

# Extra CLI tools [used in tutorials and integration tests]
sudo apt install -y gdal-bin osmium-tool

# HTTP Server [for WebUI rendering]
sudo apt install -y apache2 && sudo ln -s ~ /var/www/html/rsp


  • Requires: Python 3.6 or 3.7
  • GPU is mandatory, for rsp train
  • To test install, launch in a new terminal: rsp info
  • If needed, to remove pre-existing Nouveau driver: sudo sh -c "echo blacklist nouveau > /etc/modprobe.d/blacklist-nvidia-nouveau.conf && update-initramfs -u && reboot"

Architecture: use cherry-picked Open Source libs among Deep Learning, Computer Vision and GIS stacks.


GeoSpatial OpenDataSets:


Contributions and Services:

  • Pull Requests are welcome ! Feel free to send code... Don't hesitate either to initiate a prior discussion via gitter or ticket on any implementation question. And give also a look at Makefile rules.

  • 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, assistance and training on 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.

Requests for funding:

We've already identified several new features and research papers able to improve again, your funding would make a difference to implement them on a coming release:

  • Increase (again) prediction accuracy :

    • on low resolution imagery
    • even with few labels (aka Weakly Supervised)
    • feature extraction when they are (really) close (aka Instance Segmentation)
  • Add support for :

    • Linear features extraction
    • Time Series Imagery
    • StreetView Imagery
  • Improve (again) performances



    title = {{} Computer Vision framework for GeoSpatial Imagery},
    author = {Olivier Courtin, Daniel J. Hofmann},
    organization = {DataPink},
    year = {2019},
    url = {},
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