This project combines deep learning with open-source satellite imagery to support the ICRC in addressing violence in armed conflicts. Satellite images offer a non-intrusive form of observing surface changes related to conflict events, such as the impact of air strikes, burning villages, or the movement of heavy weaponry. The use of this remote sensing technology complements on-the-ground activities and is particularly valuable to gather information about conflict events in remote areas or areas with high security risks.

The ICRC already uses satellite images on an on-demand basis, manually assessing high resolution images to verify and document violations of humanitarian law. However, assessing satellite images manually is very labour-intensive and requires high-resolution images that are expensive to purchase, especially if entire countries need to be monitored. This makes it impossible to screen vast conflict areas on a regular basis with the current approach. To address this challenge, our project will develop an automated monitoring tool that combines deep learning with open-source satellite images of ESA’s Sentinel-1 and Sentinel-2 satellite constellations, allowing the ICRC to monitor entire conflict areas in near-real time. The project team combines experts from multiple disciplines to come up with an innovative and practical solution that addresses a technical challenge in a context appropriate and conflict sensitive manner. By enabling the ICRC to monitor large areas in a systematic and continuous manner, the project will strengthen the ICRC’s early warning capacities and facilitate near-real time assessment of impact of humanitarian action in the aftermath of major conflict events, and support the existing remote documentation process.

ETH PIs: Prof. Konrad Schindler, Prof. Jan Dirk Wegner (Ecovision Lab & University of Zurich), Dr. Valerie Sticher (Center for Security Studies)

Partner: Dr. Thao Ton-That Whelan (ICRC), Jonathan Drake (American Association for the Advancement of Science), Martin Wählisch (UNDPPA Innovation Cell)

Photo: ICRC

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