Low-data & low-code geo-spatial Deep-Learning

Towards a Low-data, Low-code Geospatial Deep Learning Platform for Domain Experts: A first application to wildfire impact severity assessment.


  • Lead school School of Engineering and Computer Science
  • Institute Institute for Data Applications and Security (IDAS)
  • Research unit IDAS / Applied Machine Intelligence
  • Funding organisation Others
  • Duration (planned) 01.03.2022 - 28.02.2023
  • Project management Dr. Souhir Ben Souissi
  • Head of project Dr. Souhir Ben Souissi
  • Project staff Céline Hüttenmoser
  • Partner Hasler Stiftung
  • Keywords Geo-Spatial, Wildfire Burn Severity, Deep Learning, Computer Science


The latest theoretical and practical advancements in data engineering and deep-learning have allowed domain-experts (such as environmental scientists & emergency management agencies) to perform prediction, classification, regression and segmentation tasks for their areas of interest (e.g. geo-spatial imaging) at a scale and accuracy that was previously impossible. Unfortunately, in these applied settings the benefits of AI & Deep-Learning are now showing diminishing returns, due to improper or noisy deployment environments as well as lack of data for domain-specific usage.

Course of action

In this project we aim to: (a) Construct more specialized data-sets that better mimic the real-world problems we have identified. (b) Experiment with DL (Deep-Learning) solutions that try to mitigate these problems in a specific context (such as Geo-spatial Deep Learning). Finally, (c) prototype a low-code solution for easily deploying and fine-tuning these type of systems in practice. We expect that the resulting work for what we describe as low-data & low-code Deep-Learning will be more amenable to specialized configuration by domain-experts who do not have an advanced AI or data engineering background. As a first application, the researchers are testing their deep-learning solutions to identify the severity of wildfire burns by using satellite images.

This project contributes to the following SDGs

  • 9: Industry, innovation and infrastructure