Building Area Estimation in Drone Aerial Images Based on Mask R-CNN

In rural areas where disasters occur frequently, the calculation of building areas is crucial in property assessment. In the segmentation algorithm, Mask R-CNN can distinguish the adjacent objects and extract the outline of an object. Based on this observation, we propose a novel method to calculate the building areas based on Mask R-CNN and adopt the concept of transfer learning to train our model, which can achieve good results with a small number of drone aerial images as training samples. The proposed method involves three main steps: 1) pretraining using open-source satellite remote sensing images; 2) fine-tuning with a small number of drone aerial images; and 3) testing with new images and area calculation based on the number of building pixels. The experiments show that the proposed method can achieve good results in terms of F1 score and intersection over union.