GES: A New Building Damage Data Augmentation and Detection Method Based on Extremely Imbalanced Data and Unique Spatial Distribution of Satellite Images

Abstract

The statistics of damaged buildings after natural disasters are crucial for rescue operations, especially for damaged buildings that are extremely challenging for object detection. There are unique spatial distribution problems in the existing damaged building datasets, and different categories of building targets show obvious imbalance, especially when the proportion of damaged buildings is less than 0.15%. To address the issues of extreme class distribution imbalance and spatial distribution uniqueness, this article proposes a new data augmentation method called the geospatial enhancement sampling (GES) algorithm. The GES algorithm performs precise data enhancement work by positioning the spatial information of the data. To enhance the robustness of the dataset for object-level detection tasks, the xFBD dataset is reconstructed into the xFBDTWC dataset in this article. The xFBDTWC dataset, featuring balanced samples and cloud occlusion, has demonstrated its excellence through experimental results. The experimental research on the proposed algorithm is conducted using the mainstream object detection models. The experimental results show that, at the object level, the detection accuracy of severely damaged buildings is 0.56, and the detection accuracy of damaged buildings is 0.65. Compared with the original detection accuracy, this method improves it by 39% and 22%, respectively. The outstanding experimental results demonstrate the effectiveness of the GES algorithm, which is crucial for the accuracy and reliability of postdisaster assessments, thereby promoting more efficient and effective disaster response and resource allocation.

Keywords

• Building damage detection
• data augmentation
• deep learning
• object detection