HOTA: Hierarchical Overlap-Tiling Aggregation for Large-Area 3D Flood Mapping
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Floods are among the most frequent natural hazards and cause significant
social and economic damage. Timely, large-scale information on flood extent and
depth is essential for disaster response; however, existing products often
trade spatial detail for coverage or ignore flood depth altogether. To bridge
this gap, this work presents HOTA: Hierarchical Overlap-Tiling Aggregation, a
plug-and-play, multi-scale inference strategy. When combined with SegFormer and
a dual-constraint depth estimation module, this approach forms a complete 3D
flood-mapping pipeline. HOTA applies overlapping tiles of different sizes to
multispectral Sentinel-2 images only during inference, enabling the SegFormer
model to capture both local features and kilometre-scale inundation without
changing the network weights or retraining. The subsequent depth module is
based on a digital elevation model (DEM) differencing method, which refines the
2D mask and estimates flood depth by enforcing (i) zero depth along the flood
boundary and (ii) near-constant flood volume with respect to the DEM. A case
study on the March 2021 Kempsey (Australia) flood shows that HOTA, when coupled
with SegFormer, improves IoU from 73\% (U-Net baseline) to 84\%. The resulting
3D surface achieves a mean absolute boundary error of less than 0.5 m. These
results demonstrate that HOTA can produce accurate, large-area 3D flood maps
suitable for rapid disaster response.
