Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines or quadratic discriminant analysis. By performing a grid-search-based hyperparameter optimization on 23 feature spaces we can show that all considered classifiers are capable of outperforming the current state-of-the-art neural network-based approaches in terms of total IoU on their best-performing feature spaces. With total and mean IoU values of 0.8751 and 0.7031 compared to 0.70 and 0.5873 as the previous best-reported results, we show that a simple gradient boosting classifier can significantly improve over deep neural network based approaches, despite using less training data. Furthermore, an analysis of the regional distribution of the Sen1Floods11 dataset reveals a problem of spatial imbalance. We show that traditional machine learning models can learn this bias and argue that modified metric evaluations are required to counter artifacts due to spatial imbalance. Lastly, a qualitative analysis shows that this pixel-wise classifier provides highly-precise surface water classifications indicating that a good choice of a feature space and pixel-wise classification can generate high-quality flood maps using optical and SAR data. We make our code publicly available at: https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance

翻译：气候变化加剧了全球范围内天气灾害的严重程度与发生频率。基于对地观测数据的洪水淹没制图能在此背景下提供帮助，通过生成反映洪水事件影响区域的高精度低成本地图，并近乎实时地传递给应急响应单位。本文依托近期发展的Sen1Floods11数据集（该数据集提供有限的手工标注高质量训练数据），评估了五种传统机器学习方法的潜力，包括梯度提升决策树、支持向量机和二次判别分析。通过对23个特征空间进行基于网格搜索的超参数优化，我们证明所有被考察的分类器在其最优特征空间上，均能在总交并比指标上超越当前最先进的基于神经网络的洪水制图方法。本文取得的总IoU为0.8751、平均IoU为0.7031，相较此前最优报告结果（0.70和0.5873），表明即使使用更少的训练数据，简单的梯度提升分类器也能显著优于深度神经网络方法。此外，对Sen1Floods11数据集区域分布的分析揭示了空间不平衡问题。我们证明传统机器学习模型能学习这种偏差，并主张需采用修正后的度量评估来消除空间不平衡导致的伪影。最后，定性分析显示这种逐像素分类器能提供高精度的地表水体分类结果，表明合理选择特征空间与逐像素分类可基于光学和SAR数据生成高质量洪水制图。我们已将代码公开于：https://github.com/DFKI-Earth-And-Space-Applications/Flood_Mapping_Feature_Space_Importance