mlsanityData to 14 Oct 2026

Effective Damage Data Generation by Fusing Imagery with Human Knowledge Using Vision-Language Models

arXiv 2508.01380 ↗First posted 2 Aug 2025cs.CV cs.AIcited by 3

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

It is of crucial importance to assess damages promptly and accurately in humanitarian assistance and disaster response (HADR). Current deep learning approaches struggle to generalize effectively due to the imbalance of data classes, scarcity of moderate damage examples, and human inaccuracy in pixel labeling during HADR situations. To accommodate for these limitations and exploit state-of-the-art techniques in vision-language models (VLMs) to fuse imagery with human knowledge understanding, there is an opportunity to generate a diversified set of image-based damage data effectively. Our initial experimental results suggest encouraging data generation quality, which demonstrates an improvement in classifying scenes with different levels of structural damage to buildings, roads, and infrastructures.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 16 of its 40 references that are in the map. First posted on arXiv 2 Aug 2025.

Builds on

16 of 40 references.

References · 16

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
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4 more
  1. 2023A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAScited by 151
  2. 2025Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Surveycited by 30
  3. 2021NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networkscited by 4
  4. 2025Investigating Vision-Language Model for Point Cloud-based Vehicle Classificationcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recoverycited by 2
  2. 2026Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysiscited by 0
  3. 2026Damage Classification for 3D Point Cloud Data via 3D Data Analysis and Vision Foundation Model-based 2D Projectionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery
  2. 2026From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
  3. 2026DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery
  4. 2026DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response
  5. 2026Satellite-to-Street: Synthesizing Post-Disaster Views from Satellite Imagery via Generative Vision Models
  6. 2025Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models
  7. 2025VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessment
  8. 2025DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response