ATTransUNet: Semantic Segmentation Model for Building Segmentation from Aerial Image and Laser Data

Abstract

The segmentation of buildings using aerial images and laser data (LIDAR) is a key area of study in computer vision and artificial intelligence. In this paper, we proposed a new deep learning-based framework architecture based on U-Net for the MapAI competition, which required participants to perform two tasks for segmenting buildings. On segmentation task 1, our model achieved an Intersection-over-Union (IoU) score of 0.7551 and a Boundary Intersection-over-Union (BIoU) score of 0.5613. On segmentation task 2, our model achieved an IoU score of 0.8555 and a BIoU score of 0.7127. These results demonstrate that our proposed method achieves competitive IoU and BIoU accuracies in building segmentation.