Near--Real-Time Conflict-Related Fire Detection in Sudan Using Unsupervised Deep Learning

TL;DR

This paper introduces a lightweight, unsupervised deep learning method using Variational Auto-Encoders for rapid detection of conflict-related fires in Sudan from high-resolution satellite imagery within 24-30 hours, outperforming traditional methods.

Contribution

The study adapts a VAE-based model for 4-band high-resolution imagery, enabling near-real-time conflict fire detection with improved accuracy over existing techniques.

Findings

The approach detects fire-affected regions within 24-30 hours.

It outperforms cosine distance, CVA, and IR-MAD in precision, recall, and F1-score.

Adding more spectral bands yields marginal performance improvements.

Abstract

Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas. Recent advances in deep learning and high-frequency satellite imagery enable near--real-time assessment of active fires and burn scars in war zones. This study presents a near--real-time monitoring approach using a lightweight Variational Auto-Encoder (VAE)--based model integrated with 4-band Planet Labs imagery at 3 m spatial resolution. We demonstrate that these impacted regions can be detected within approximately 24 to 30 hours under favorable observational conditions using accessible, commercially available satellite data. To achieve this, we adapt a VAE--based model, originally designed for 10-band imagery, to operate effectively on high-resolution 4-band inputs. The model is trained in an unsupervised manner to learn compact latent representations of nominal land-surface conditions and identify burn signatures by quantifying changes between temporally paired latent embeddings. Performance is evaluated across five case studies in Sudan and compared against cosine distance, CVA, and IR-MAD using precision, recall, F1-score, and the area under the precision-recall curve (AUPRC) computed between temporally paired image tiles. Results show that the proposed approach consistently outperforms the other methods, achieving higher recall and F1-scores while maintaining operationally viable precision in highly imbalanced fire-detection scenarios. Experiments with 8-band imagery and temporal image sequences yield only marginal performance gains over single 4-band inputs, underscoring the effectiveness of the proposed lightweight approach for scalable, near--real-time conflict monitoring.