# From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery

arXiv: https://arxiv.org/abs/2603.22768 · HTML: https://mlsanity.com/paper/2603.22768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response](https://mlsanity.com/paper/2505.21089/) (2025)
- [VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis](https://mlsanity.com/paper/2106.10548/) (2021)
- [ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2506.00238/) (2025)
- [Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces](https://mlsanity.com/paper/2505.03974/) (2025)
- [Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery](https://mlsanity.com/paper/2510.27224/) (2025)

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

- [GeBDA: Building Damage Assessment as Text-Based Sequence Prediction](https://mlsanity.com/paper/2608.28567/) (2026)

## Abstract (arXiv)

Rapid and accurate structural damage assessment following natural disasters is critical for effective emergency response and recovery. However, remote sensing imagery often suffers from low spatial resolution, contextual ambiguity, and limited semantic interpretability, reducing the reliability of traditional detection pipelines. In this work, we propose a novel hybrid framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment. First, we enhance pre- and post-disaster satellite imagery using a Video Restoration Transformer (VRT) to upscale images from 1024x1024 to 4096x4096 resolution, improving structural detail visibility. Next, a YOLOv11-based detector localizes buildings in pre-disaster imagery, and cropped building regions are analyzed using VLMs to semantically assess structural damage across four severity levels. To ensure robust evaluation in the absence of ground-truth captions, 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. Experiments on subsets of the xBD dataset, including the Moore Tornado and Hurricane Matthew events, demonstrate that the proposed framework enhances the semantic interpretation of damaged buildings. In addition, our framework provides helpful recommendations to first responders for recovery based on damage analysis.