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

• URL: http://arxiv.org/abs/2010.14014v2
• Date: Wed, 18 Nov 2020 19:07:27 GMT
• Title: Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
• Authors: Yu Shen, Sijie Zhu, Taojiannan Yang, Chen Chen
• Abstract summary: Building damage assessment from satellite imagery is critical before an effective response is conducted. We propose a novel cross-directional fusion strategy to better explore the correlations between pre- and post-disaster images. The proposed method achieves state-of-the-art performance on a large-scale building damage assessment dataset.
• Score: 26.767229623170497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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