Deep Learning for Earth

Quality and reliability of object detectors Data-centric workflows for EO imagery Custom algorithms development Algorithms qualification Mentoring and consultancy Hackathons and challenges


  • Promote quality and reliability of object detectors for EO imagery

    The adoption of deep learning based algorithms on satellite imagery needs high quality and reliability. Metrics needs to be published and audited by various parties to build trust in the technology. Errors and edge-cases need to be analyzed and shared with potential users in order to find corrective actions. In turn, this will support explanability and ethical practices that are also needed for larger adoption.

  • Advance data-centric workflows for training and continuous improvement

    Building a object detector on satellite imagery cannot be achieved without placing data at the center of the development process. Tagging and data-mining are key elements of the final product. A data-centric workflow enables to label exactly the data which needs to be labelled in order to make the model better at every cycle. This means that a MLOps framework is used and that it includes the human annotator in the loop. This enables to continuously improve the model.

  • Foster adoption through open-source strategy and global training

    Some organizations can use pre-defined algorithms but some others will need to adapt the algorithms to their specific use-case. It could be a specific classification, a specific type of imagery (bands, resolution, geometry). One size does not fits all in Earth Observation. Consulting, mentoring and training are very important to increase knowledge and know-how in all industries using satellite imagery. That is also why open standards, open source, sharing knowledge and building community are key elements that DL4EO wants to foster.


  • Vessels detection on Sentinel-2

    Vessels detection on Sentinel-2 image
  • Infrastructure identification on Sentinel-2

    Infrastructure identification on Sentinel-2 image
  • Oil storage tanks detection

    Oil storage tanks detection image
  • Custom DL4EO model development

    Custom DL4EO model development image


  • Custom object detectors on satellite imagery

    This is typically a 3 to 6 months project which start by the definition of the objectives with the stakeholders and finishes with the delivery of a production-ready container (Docker) which detects objects in satellite imagery. All the anotations work and data-centric model building is created internally and provided to the client.

  • Innovation in Deep Learning for Earth Observation

    Applying Deep Learning to the specifics of satellite imagery is a real passion! From taking avantage of hyperspectral bands, to leveraging synthetic images for rare objects detection to the automatic extraction of height and other object characteristics, this is my core expertise at the convergence of physics, computer science and customer-oriented problem-solving skills.

  • Hackathons, Challenges and Mentoring

    Hackathons and challenges are sometimes a very good way of getting new idea for solving a specifically difficult problem; it also enables to build an ecosystem of partners and potential subcontractors. I can support such an initiative during the various stages of the project from inception, data preparation, mentoring, selection of the winning teams and integration.