# Building-Guided Pseudo-Label Learning for Cross-Modal Building Damage Mapping

arXiv: https://arxiv.org/abs/2505.04941 · HTML: https://mlsanity.com/paper/2505.04941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 7 of 7)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2404.09179/) (2024)
- [Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network](https://mlsanity.com/paper/2307.12309/) (2023)

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

- [Advancing All-Weather Building Damage Mapping to the Instance Level: Outcomes and Insights from the 2026 Bright Challenge](https://mlsanity.com/paper/2607.22746/) (2026)

## Abstract (arXiv)

Accurate building damage assessment using bi-temporal multi-modal remote sensing images is essential for effective disaster response and recovery planning. This study proposes a novel Building-Guided Pseudo-Label Learning Framework to address the challenges of mapping building damage from pre-disaster optical and post-disaster SAR images. First, we train a series of building extraction models using pre-disaster optical images and building labels. To enhance building segmentation, we employ multi-model fusion and test-time augmentation strategies to generate pseudo-probabilities, followed by a low-uncertainty pseudo-label training method for further refinement. Next, a change detection model is trained on bi-temporal cross-modal images and damaged building labels. To improve damage classification accuracy, we introduce a building-guided low-uncertainty pseudo-label refinement strategy, which leverages building priors from the previous step to guide pseudo-label generation for damaged buildings, reducing uncertainty and enhancing reliability. Experimental results on the 2025 IEEE GRSS Data Fusion Contest dataset demonstrate the effectiveness of our approach, which achieved the highest mIoU score (54.28%) and secured first place in the competition.