Near-real time fires detection using satellite imagery in Sudan conflict
This provides near-real-time monitoring for conflict zones, but it is incremental as it builds on existing deep learning and satellite methods.
The paper tackled the problem of rapid fire damage monitoring in the Sudan conflict using satellite imagery, achieving more accurate detection of active fires and charred areas compared to a baseline with minimal delay.
The challenges of ongoing war in Sudan highlight the need for rapid monitoring and analysis of such conflicts. Advances in deep learning and readily available satellite remote sensing imagery allow for near real-time monitoring. This paper uses 4-band imagery from Planet Labs with a deep learning model to show that fire damage in armed conflicts can be monitored with minimal delay. We demonstrate the effectiveness of our approach using five case studies in Sudan. We show that, compared to a baseline, the automated method captures the active fires and charred areas more accurately. Our results indicate that using 8-band imagery or time series of such imagery only result in marginal gains.