Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models

• URL: http://arxiv.org/abs/2605.08521v1
• Date: Fri, 08 May 2026 22:05:17 GMT
• Title: Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models
• Abstract summary: Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters.<n>This paper presents a geometric "Water Surface Elevation" approach for estimating flood depth from monocular aerial imagery.<n>Our pipeline utilizes Mask2Former, a state-of-the-art transformer-based segmentation model, to generate precise 2D flood masks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters. While 2D semantic segmentation provides accurate flood masking, it lacks the vertical dimension required to assess navigability and structural risk. This paper presents a geometric "Water Surface Elevation" approach for estimating flood depth from monocular aerial imagery. Our pipeline utilizes Mask2Former, a state-of-the-art transformer-based segmentation model, to generate precise 2D flood masks. These masks are fused with Digital Elevation Models (DEMs) to identify the water-land boundary, calculate a global water surface elevation ($Z_{water}$), and compute per-pixel depth based on the principle of local hydrostatic equilibrium. We evaluate this workflow using the FloodNet and CRASAR-U-DROIDS datasets, demonstrating how high-performance segmentation can be leveraged to extract 3D volumetric data from 2D imagery without the latency of hydrodynamic simulations.

Related papers

• Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
  SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
  arXiv  Detail & Related papers  (2026-08-12T11:25:31Z)
• Wat3R: Underwater 3D Geometry Learning without Annotations [58.91163632674025]
  Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations.<n>We propose Wat3R, a cross-domain semi-supervised learning framework designed to adapt feed-forward 3D reconstruction models from air to underwater scenes.
  arXiv  Detail & Related papers  (2026-07-09T17:59:58Z)
• Towards 3D karst underwater scene reconstruction from rotating sonar data [4.98552535993062]
  We present a pipeline for reconstructing underwater karst conduits from a sonar profiler.<n>We combine a continuous-time SLAM approach to correct trajectory drift with a novel two-stage deep learning method for surface reconstruction.
  arXiv  Detail & Related papers  (2026-06-18T15:02:00Z)
• Looking Into the Water by Unsupervised Learning of the Surface Shape [5.875225219574616]
  We address the problem of looking into the water from the air, where we seek to remove image distortions caused by refractions at the water surface.<n>Our approach is based on modeling the different water surface structures at various points in time, assuming the underlying image is constant.
  arXiv  Detail & Related papers  (2026-03-08T12:54:41Z)
• FreqPDE: Rethinking Positional Depth Embedding for Multi-View 3D Object Detection Transformers [91.59069344768858]
  We introduce Frequency-aware Positional Depth Embedding (FreqPDE) to equip 2D image features with spatial information for 3D detection transformer decoder.<n>FreqPDE combines the 2D image features and 3D position embeddings to generate 3D depth-aware features for query decoding.
  arXiv  Detail & Related papers  (2025-10-17T07:36:54Z)
• PFDepth: Heterogeneous Pinhole-Fisheye Joint Depth Estimation via Distortion-aware Gaussian-Splatted Volumetric Fusion [61.6340987158734]
  We present the first pinhole-fisheye framework for heterogeneous multi-view depth estimation, PFDepth.<n> PFDepth employs a unified architecture capable of processing arbitrary combinations of pinhole and fisheye cameras with varied intrinsics and extrinsics.<n>We show that PFDepth sets a state-of-the-art performance on KITTI-360 and RealHet datasets over current mainstream depth networks.
  arXiv  Detail & Related papers  (2025-09-30T09:38:59Z)
• HOTA: Hierarchical Overlap-Tiling Aggregation for Large-Area 3D Flood Mapping [4.567610334888734]
  This work presents HOTA: Hierarchical Overlap-Tiling Aggregation, a plug-and-play, multi-scale inference strategy.<n>When combined with SegFormer and a dual-constraint depth estimation module, this approach forms a complete 3D flood-mapping pipeline.<n>Results demonstrate that HOTA can produce accurate, large-area 3D flood maps suitable for rapid disaster response.
  arXiv  Detail & Related papers  (2025-07-10T09:40:20Z)
• Plenodium: UnderWater 3D Scene Reconstruction with Plenoptic Medium Representation [31.47797579690604]
  We present Plenodium, a 3D representation framework capable of jointly modeling both objects and participating media.<n>In contrast to existing medium representations that rely solely on view-dependent modeling, our novel plenoptic medium representation incorporates both directional and positional information.<n>Experiments on real-world underwater datasets demonstrate that our method achieves significant improvements in 3D reconstruction.
  arXiv  Detail & Related papers  (2025-05-27T14:37:58Z)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception.<n>Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation [44.99833362998488]
  We present DeepAqua, a self-supervised deep learning model that eliminates the need for manual annotations during the training phase. We exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%.
  arXiv  Detail & Related papers  (2023-05-02T18:06:21Z)
• Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models [0.0]
  Machine learning was used to extract a Water Surface Elevation (WSE) value from disturbed photogrammetric data. Data can be used to validate and calibrate hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate.
  arXiv  Detail & Related papers  (2021-12-22T11:25:23Z)
• Deep Two-View Structure-from-Motion Revisited [83.93809929963969]
  Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. We propose to revisit the problem of deep two-view SfM by leveraging the well-posedness of the classic pipeline. Our method consists of 1) an optical flow estimation network that predicts dense correspondences between two frames; 2) a normalized pose estimation module that computes relative camera poses from the 2D optical flow correspondences, and 3) a scale-invariant depth estimation network that leverages epipolar geometry to reduce the search space, refine the dense correspondences, and estimate relative depth maps.
  arXiv  Detail & Related papers  (2021-04-01T15:31:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.