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 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.

翻译：持续不断的武装冲突凸显了在苏丹快速监测冲突相关火灾影响区域的迫切需求。深度学习与高频次卫星影像的最新进展使得实时评估战区活跃火灾与燃烧疤痕成为可能。本研究提出一种近实时监测方法，采用基于轻量级变分自编码器（VAE）的模型，融合空间分辨率为3米的4波段Planet Labs影像。研究表明，在有利观测条件下，利用可获取的商业卫星数据，可在大约24至30小时内检测到这些受影响的区域。为此，我们将原本针对10波段影像设计的VAE模型进行适配，使其高效处理高分辨率4波段输入数据。该模型以无监督方式进行训练，学习名义地表状态的紧凑潜在表征，并通过量化时间配对的潜在嵌入之间的变化来识别燃烧特征。通过苏丹境内五个案例研究评估模型性能，并与余弦距离、CVA及IR-MAD方法进行比较，比较指标包括精确率、召回率、F1分数以及基于时间配对影像瓦片计算的精确率-召回率曲线下面积（AUPRC）。结果表明，所提出的方法持续优于其他方法，在高度不平衡的火灾检测场景中，在保持可行精确率的同时实现了更高的召回率和F1分数。使用8波段影像及时间序列影像的实验相较于仅用4波段输入仅带来边际性能提升，这突显了所提出的轻量级方法在实现可扩展的近实时冲突监测中的有效性。