# HOTA: Hierarchical Overlap-Tiling Aggregation for Large-Area 3D Flood Mapping

arXiv: https://arxiv.org/abs/2507.07585 · HTML: https://mlsanity.com/paper/2507.07585/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping](https://mlsanity.com/paper/2506.13201/) (2025)

## Built on by (most cited first, 1 of 1)

- [Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models](https://mlsanity.com/paper/2603.27504/) (2026)

## Abstract (arXiv)

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.