Deep Learning simplification for on-board processing


The main objective of the project is to define a workflow easing the integration and reduction of complex Deep Neural Networks (DNN) models on SoC (Sytem-on-Chip) FPGA platforms.


Image processing on-board (HR & VHR & Hyperspectral)

This workflow will be demonstrated on a Deep Learning (DL) image processing pipeline devoted to feature extraction in Earth Observation images on-board of small satellites.

This pipeline will be as generic as possible to be able to update, in flight, the performed recognition tasks (clouds, floods, planes, ships or more generic objects identification) with new models required by new applications or users (replace ships detection by oil spills detection, or even fires).

The main challenge of this activity is to define the most suitable combination of methods for DL networks simplification (pruning, compression, …) allowing to execute efficient but complex networks in on-board hardware resources (spatialized or not, including FPGA). These methods are generic enough to be applied to networks or aggregate of networks with different types of architectures. For example:

As a summary, the main objectives of the project are:
Reduce DNN free parameters to fit in existing devices suitable for cubesat platforms, with minimal performance loss & high throughput
Propose and benchmark generic methodologies for DNN simplification
some applications
to identify ships, planes or vehicules for sites monitoring
in agriculture, to analyse cultures and soil evolution
forest fire
to identify and fight forest fires
to identify evolution of glaciers and movement of drifting icebergs
for any other application
This work is funded by a contract in the framework of the EO SCIENCE FOR SOCIETY PERMANENTLY OPEN CALL FOR PROPOSALS EOEP-5 BLOCK 4 issued by the European Space Agency.


ESA EO Φ-WEEK 2020 Virtual Event This event will feature invited talks only. You have the opportunity to contribute with an e-poster or a side event. The deadline for submissions is 27 July 2020.
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  • Contractor:
  • Agenium Space
  • 1 avenue de l'Europe, Bâtiment 1
  • 31400 Toulouse