Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events
地表覆盖与洪水类型决定了全球多样化洪水事件中卫星洪水测绘的检测极限
Abstract: Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response. Geospatial foundation models pretrained on satellite archives offer geographic transferability, but their operational reliability across diverse, unseen events remains uncharacterized.
摘要： 洪水是最具破坏性的自然灾害之一，气候变化下洪水频率的增加使得基于卫星的淹没测绘对于灾害响应至关重要。在卫星档案上预训练的地理空间基础模型提供了地理迁移能力，但其在多样化、未见过的事件中的运行可靠性尚未得到充分表征。
Here we deploy Prithvi-EO-2.0 across 19 out-of-distribution flood events (2017-2025) spanning six continents, eight climate zones, and six flood mechanisms, validating against two independent reference products. Detection accuracy depended jointly on land cover and flood type, with cropland yielding the highest agreement (IoU=52%) and riverine events the strongest detection (F1=0.69), while tree cover and built-up areas showed near-zero detection (IoU=4%) regardless of flood mechanism.
在此，我们部署了 Prithvi-EO-2.0 模型，针对跨越六大洲、八个气候带和六种洪水机制的 19 个分布外（out-of-distribution）洪水事件（2017-2025 年）进行了测试，并与两个独立的参考产品进行了验证。检测准确度取决于地表覆盖和洪水类型的共同影响，其中农田的吻合度最高（IoU=52%），河流洪水事件的检测效果最强（F1=0.69）；而无论洪水机制如何，树木覆盖区和建筑区的检测率均接近于零（IoU=4%）。
Dual-reference validation revealed that apparent model error partly reflects definitional inconsistency between reference products rather than detection failure. Iterative pipeline testing identified 23 failure modes, with pipeline engineering dominating initial error over model capacity. These findings establish environment-dependent detection boundaries for operational satellite flood mapping.
双重参考验证表明，明显的模型误差部分反映了参考产品之间定义的不一致，而非检测失败。迭代流水线测试确定了 23 种故障模式，其中流水线工程导致的初始误差超过了模型本身的能力限制。这些发现为业务化卫星洪水测绘建立了依赖于环境的检测边界。
Paper Details:
- Authors: Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane, Caden Helbling, Deepak Shah, Othneil Drew, Iksha Gurung, Manil Maskey, Rahul Ramachandran
- arXiv ID: 2606.07780
- Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
论文详情：
- 作者： Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane, Caden Helbling, Deepak Shah, Othneil Drew, Iksha Gurung, Manil Maskey, Rahul Ramachandran
- arXiv ID: 2606.07780
- 学科分类： 人工智能 (cs.AI)；计算机视觉与模式识别 (cs.CV)；机器学习 (cs.LG)