Deep Learning
for Earth Observation

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.

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.

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.

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 efficiently and provided to the client.

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.

The speed of innovation in the Deep Learning field is very high. It is always a good idea to test ideas and do prototyping before engaging into a new challenge. I can help you with that and also benchmark your algorithms against the state-of-the-art. I can also help you with the evaluation of your algorithms and the definition of the metrics that are relevant for your use-case.