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

27 Oct 2020  ·  Yu Shen, Sijie Zhu, Taojiannan Yang, Chen Chen ·

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.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
2D Semantic Segmentation xBD Double branch U-Net Weighted Average F1-score 0.804 # 2

Methods