Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. While recent deep learning approaches achieve high accuracy when high-resolution multispectral data are available, their applicability in operational settings, where a quick delineation of the burn scar shortly after a wildfire incident is required, is limited by the trade-off between spatial resolution and temporal revisit frequency of current satellite systems. To address this limitation, we propose a novel deep learning model, namely BAM-MRCD, which employs multi-resolution, multi-source satellite imagery (MODIS and Sentinel-2) for the timely production of detailed burnt area maps with high spatial and temporal resolution. Our model manages to detect even small scale wildfires with high accuracy, surpassing similar change detection models as well as solid baselines. All data and code are available in the GitHub repository: https://github.com/Orion-AI-Lab/BAM-MRCD.

翻译：利用卫星影像划定野火影响区域仍然面临挑战，原因在于电磁波谱中不规则且空间异质的光谱变化。尽管当高分辨率多光谱数据可用时，近期的深度学习方法能够实现高精度制图，但其在业务化场景中的适用性受到限制——这类场景要求在野火事件发生后快速划定火烧迹地，而当前卫星系统的空间分辨率与时间重访频率之间存在固有矛盾。为应对这一局限，我们提出了一种新型深度学习模型BAM-MRCD，该模型利用多分辨率多源卫星影像（MODIS与Sentinel-2）及时生成兼具高时空分辨率的精细化火烧迹地分布图。我们的模型能够以高精度检测小规模野火事件，其性能超越同类变化检测模型及现有稳健基线方法。所有数据与代码均发布于GitHub仓库：https://github.com/Orion-AI-Lab/BAM-MRCD。