GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

• URL: http://arxiv.org/abs/2608.28567v1
• Date: Fri, 28 Aug 2026 17:44:55 GMT
• Title: GeBDA: Building Damage Assessment as Text-Based Sequence Prediction
• Abstract summary: Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation models.<n>In this work, we ask whether a general-purpose Vision-Language Model (VLM) can localize buildings and grade their damage through autoregressive sequence generation alone.
• Score: 20.804718264064512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation models. In this work, we ask whether a general-purpose Vision-Language Model (VLM) can localize buildings and grade their damage through autoregressive sequence generation alone. We cast BDA as predicting a variable-length set of bounding boxes, each specified by its coordinates and a damage label. Our preliminary implementation, based on the open Gemma model, achieves promising damage mapping results from only bi-temporal satellite images and a suitable text prompt.

Related papers

• Orthogonal JEPA: Factorized Predictive States for Latent World Models [95.20158869896393]
  We introduce method, a latent world-modeling framework based on predictive factorization.<n>We show that method can allocate redundant capacity to dominant signals while providing weak or conflicting gradients to less dominant predictive structure.<n>Experiments on controlled vision, single-cell transcriptomics, longitudinal health records, continuous control, and molecular dynamics evaluate representation quality, forecasting, planning, and long-horizon stability.
  arXiv  Detail & Related papers  (2026-08-20T13:59:57Z)
• Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery [3.016488960368601]
  We propose a hybrid framework that decouples detection from damage assessment.<n>A CV model first detects buildings and generates bounding boxes on the image that are then passed to an LVLM for damage classification and contextual interpretation.<n>We evaluate our framework on two real-world benchmarks: RescueNet and FloodNet.
  arXiv  Detail & Related papers  (2026-08-03T08:39:59Z)
• Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback [51.08692072066352]
  Text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures.<n>Structured Defect Grounding casts T2I diagnosis as structured set prediction by modeling each defect as a location, type, reason, importance.<n>Our detector outperforms leading proprietary VLMs on structured defect grounding.
  arXiv  Detail & Related papers  (2026-06-04T13:03:16Z)
• From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery [0.0]
  We propose a novel framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment.<n>First, we enhance pre- and post-disaster satellite imagery using a Video Restoration Transformer (VRT) to upscale images from 1024x1024 to 4096x4096 resolution.<n>We employ CLIPScore for reference-free semantic alignment and introduce a multi-model VLM-as-a-Jury strategy to reduce individual model bias in safety-critical decision making.
  arXiv  Detail & Related papers  (2026-03-24T03:50:41Z)
• Learning Efficient Unsupervised Satellite Image-based Building Damage Detection [43.06758527206676]
  Building Damage Detection (BDD) methods always require labour-intensive pixel-level annotations of buildings and their conditions. In this paper, we investigate a challenging yet practical scenario of U-BDD, where only unlabelled pre- and post-disaster satellite image pairs are provided. We present a novel self-supervised framework, U-BDD++, which improves upon the U-BDD baseline by addressing domain-specific issues associated with satellite imagery.
  arXiv  Detail & Related papers  (2023-12-04T02:20:35Z)
• BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images [24.989412626461213]
  Building damage assessment from satellite imagery is critical before relief effort is deployed. Deep neural networks have been successfully applied to building damage assessment. We propose a novel two-stage convolutional neural network for Building Damage Assessment, called BDANet.
  arXiv  Detail & Related papers  (2021-05-16T06:13:28Z)
• Assessing out-of-domain generalization for robust building damage detection [78.6363825307044]
  Building damage detection can be automated by applying computer vision techniques to satellite imagery. Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event. We argue that future work should focus on the OOD regime instead.
  arXiv  Detail & Related papers  (2020-11-20T10:30:43Z)
• MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos [74.22132693931145]
  We study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks. The second contribution is a new model, namely MSNet, which contains novel region proposal network designs.
  arXiv  Detail & Related papers  (2020-06-30T02:23:05Z)
• RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery [83.49145695899388]
  RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
  arXiv  Detail & Related papers  (2020-04-15T19:52:09Z)

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.