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ABSTRACT
During disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often suffer from the contradiction between the accuracy and efficiency of building damage extraction. This paper proposed a simple and effective framework to rapid recognize collapsed building objects using pre-disaster building distribution maps and post-disaster quasi-panchromatic remote sensing images. The proposed method is validated using several historical disasters in the xBD dataset and tested using three cases of earthquakes in terms of both effectiveness and efficiency. In addition, we have verified that the texture information of optical remote sensing images can be used as the main basis to judge whether a building is collapsed or not, so the panchromatic images are sufficient to enable the deep learning model to correctly recognize collapsed buildings. The experimental results indicate that using quasi-panchromatic images can alleviate the influence of style variations and diverse roof colors present in multi-spectral images on the model’s generalization performance, resulting in an average overall accuracy improvement of 2.4%. Additionally, the reduced data volume leads to an improvement in inference efficiency.
Acknowledgments
This research is carried out under programming in Python and deep learning framework Pytorch.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The xBD dataset is available at https://xview2.org/dataset.