新洪水检测数据集，应对卫星图像季节变化挑战。
A Novel Dataset for Flood Detection Robust to Seasonal Changes in Satellite Imagery
- 从2019年美国中西部洪水影像构建10个州的100张图像数据集。
- 模型在该数据集上表现一般，提示需改进时空联合学习。
- 适合遥感、计算机视觉与灾害应急研究者使用。
本研究提出一个用于卫星图像洪水区域分割的新数据集。通过审查77个现有基准，发现该任务缺乏合适的数据集。为此，我们从Planet Labs的Planet Explorer平台收集了2019年美国中西部洪水的卫星影像（图像 © 2024 Planet Labs PBC）。数据集包含5个州（爱荷华州、堪萨斯州、蒙大拿州、内布拉斯加州、南达科他州）各10个地点，每地点10张图像，每张图像均含洪水与非洪水区域。数据处理中保证统一分辨率与重采样。为评估语义分割性能，我们在该数据集上测试了计算机视觉与遥感领域的先进模型，并进行消融实验，改变窗口大小以捕捉时间特征。总体结果较为有限，表明未来需发展多模态与时间建模策略。数据集将公开于 <https://github.com/youngsunjang/SDSU_MidWest_Flood_2019>。
原文摘要 · Abstract (English)
This study introduces a novel dataset for segmenting flooded areas in satellite images. After reviewing 77 existing benchmarks utilizing satellite imagery, we identified a shortage of suitable datasets for this specific task. To fill this gap, we collected satellite imagery of the 2019 Midwestern USA floods from Planet Explorer by Planet Labs (Image \c{opyright} 2024 Planet Labs PBC). The dataset consists of 10 satellite images per location, each containing both flooded and non-flooded areas. We selected ten locations from each of the five states: Iowa, Kansas, Montana, Nebraska, and South Dakota. The dataset ensures uniform resolution and resizing during data processing. For evaluating semantic segmentation performance, we tested state-of-the-art models in computer vision and remote sensing on our dataset. Additionally, we conducted an ablation study varying window sizes to capture temporal characteristics. Overall, the models demonstrated modest results, suggesting a requirement for future multimodal and temporal learning strategies. The dataset will be publicly available on <https://github.com/youngsunjang/SDSU_MidWest_Flood_2019>.
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