# Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery

arXiv: https://arxiv.org/abs/2010.14014 · HTML: https://mlsanity.com/paper/2010.14014/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Learning Efficient Unsupervised Satellite Image-based Building Damage Detection](https://mlsanity.com/paper/2312.01576/) (2023)
- [Building Damage Mapping with Self-PositiveUnlabeled Learning](https://mlsanity.com/paper/2111.02586/) (2021)
- [Disaster mapping from satellites: damage detection with crowdsourced point labels](https://mlsanity.com/paper/2111.03693/) (2021)
- [GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief](https://mlsanity.com/paper/2206.09242/) (2022)

## Abstract (arXiv)

Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply use pre- and post-disaster images as input without considering their correlations. In this paper, we propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. Moreover, the data augmentation method CutMix is exploited to tackle the challenge of hard classes. The proposed method achieves state-of-the-art performance on a large-scale building damage assessment dataset -- xBD.