Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery

• URL: http://arxiv.org/abs/2608.01906v1
• Date: Mon, 03 Aug 2026 08:39:59 GMT
• Title: Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery
• Abstract summary: 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.
• Score: 3.016488960368601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid and accurate post-disaster building damage assessment is essential, yet remains a challenging task. Unmanned Aerial Vehicle (UAV) imagery offers a timely and high-resolution view of affected areas, but existing Computer Vision (CV) models often demand large annotated datasets, generalize poorly across geographic regions and their assessment policies, and are confined to the specific tasks they were trained for. Large Vision-Language Models (LVLMs) offer a promising alternative through their strong reasoning and generalization capabilities, but fall short on precise, low-level perception tasks such as object detection and accurate bounding box generation. Furthermore, they often require a substantial amount of data for effective fine-tuning on domain-specific tasks. In this paper, we propose a hybrid framework that decouples detection from damage assessment, combining the precision of CV models with the reasoning power of LVLMs. 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. We evaluated our framework on two real-world benchmarks: RescueNet and FloodNet. In particular, the best combination under this framework accurately counts intact, partially damaged and completely destroyed buildings, surpassing isolated baselines by up to 2.1 R^2 points, while requiring only limited annotated data for the detection stage. Beyond reporting aggregate gains, we provide a detailed analysis of failure scenarios and edge cases, offering practical insights for practitioners and concrete directions for future work. Our source code and data are publicly available to the research community via the following repository: https://github.com/ungquanghuy-kddi/VLM_GDINO.git

Related papers

• DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery [6.514592086157328]
  DamageScope is a retrieval-augmented framework that combines satellite imagery with Vision-Language Models (VLMs) and Large Language Models (LLMs)<n>Built on a Retrieval-Augmented Generation (RAG) framework, DamageScope extracts structured visual representations from satellite imagery to support interactive natural language queries for damage assessment.
  arXiv  Detail & Related papers  (2026-08-21T18:09:21Z)
• Seeing the Unseen: Towards Zero-Shot Inspection for Wind Turbine Blades using Knowledge-Augmented Vision Language Models [10.230967860299504]
  We propose a zero-shot-oriented inspection framework that integrates Retrieval-Augmented Generation with Vision-Language Models.<n>A multimodal knowledge base is constructed, comprising technical documentation, representative reference images, and domain-specific guidelines.<n>We evaluate the framework on 30 labeled blade images covering diverse damage categories.
  arXiv  Detail & Related papers  (2025-10-26T23:19:28Z)
• Rethinking Evaluation of Infrared Small Target Detection [105.59753496831739]
  This paper introduces a hybrid-level metric incorporating pixel- and target-level performance, proposing a systematic error analysis method, and emphasizing the importance of cross-dataset evaluation.<n>An open-source toolkit has be released to facilitate standardized benchmarking.
  arXiv  Detail & Related papers  (2025-09-21T02:45:07Z)
• More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV [58.89234732689013]
  CODrone is a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions.<n>It also serves as a new benchmark designed to align with downstream task requirements.<n>We conduct a series of experiments based on 22 classical or SOTA methods to rigorously evaluate CODrone.
  arXiv  Detail & Related papers  (2025-04-28T17:56:02Z)
• Effective and Efficient Adversarial Detection for Vision-Language Models via A Single Vector [97.92369017531038]
  We build a new laRge-scale Adervsarial images dataset with Diverse hArmful Responses (RADAR) We then develop a novel iN-time Embedding-based AdveRSarial Image DEtection (NEARSIDE) method, which exploits a single vector that distilled from the hidden states of Visual Language Models (VLMs) to achieve the detection of adversarial images against benign ones in the input.
  arXiv  Detail & Related papers  (2024-10-30T10:33:10Z)
• CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset [14.246172794156987]
  $textitCableInspect-AD$ is a high-quality dataset created and annotated by domain experts from Hydro-Qu'ebec, a Canadian public utility. This dataset includes high-resolution images with challenging real-world anomalies, covering defects with varying severity levels. We present a comprehensive evaluation protocol based on cross-validation to assess models' performances.
  arXiv  Detail & Related papers  (2024-09-30T14:50:13Z)
• Long Range Object-Level Monocular Depth Estimation for UAVs [0.0]
  We propose several novel extensions to state-of-the-art methods for monocular object detection from images at long range. Firstly, we propose Sigmoid and ReLU-like encodings when modeling depth estimation as a regression task. Secondly, we frame the depth estimation as a classification problem and introduce a Soft-Argmax function in the calculation of the training loss.
  arXiv  Detail & Related papers  (2023-02-17T15:26:04Z)
• Multi-view deep learning for reliable post-disaster damage classification [0.0]
  This study aims to enable more reliable automated post-disaster building damage classification using artificial intelligence (AI) and multi-view imagery. The proposed model is trained and validated on reconnaissance visual dataset containing expert-labeled, geotagged images of the inspected buildings following hurricane Harvey.
  arXiv  Detail & Related papers  (2022-08-06T01:04:13Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Domain Adaptive Monocular Depth Estimation With Semantic Information [13.387521845596149]
  We propose an adversarial training model that leverages semantic information to narrow the domain gap. The proposed compact model achieves state-of-the-art performance comparable to complex latest models.
  arXiv  Detail & Related papers  (2021-04-12T18:50:41Z)
• 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.