Urban planning, climatology, or hydrology require continuous and spatially explicit information about impervious surfaces. Semantic segmentation using very high-resolution remote sensing data increased the performance of their detection. However, semantic segmentation models (SSMs) suffer from domains shifts when applied across cities or seasons. While domain adaptation (DA) techniques exist, the current literature provides little information on the level of sensitivity expected for baseline SSMs in mapping impervious surfaces in such scenarios. This study evaluates how data modality (e. g. spectral or height information) and adaptive batch normalization (AdaBN) affect the robustness of SSMs in cross-city and cross-season scenarios. Potsdam and Vaihingen benchmark datasets were used and merged into classes of impervious surfaces, buildings, and background. The impervious surface class was found to be the most sensitive to cross-domain shifts. Multimodal datasets and AdaBN increased model robustness, while in comparison, the impact of AdaBN was 3.46 percentage points lower regarding the mean intersection over union (mIoU). The combination of multimodal datasets and AdaBN exhibited the best results throughout the experiments, increasing mIoU by an additional 10.06 percentage points compared to the multimodal model versions. When DA techniques are unavailable, using multimodal datasets in combination with AdaBN holds a practical approach for cross-domain scenarios in impervious surface mapping.
Langenkamp et al. (Mon,) studied this question.
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