DeepAI AI Chat
Log In Sign Up

BuildSeg: A General Framework for the Segmentation of Buildings

by   Lei Li, et al.

Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality. While current research using different types of convolutional and transformer networks has considerably improved the performance on this task, even more accurate segmentation methods for buildings are desirable for applications such as automatic mapping. In this study, we propose a general framework termed BuildSeg employing a generic approach that can be quickly applied to segment buildings. Different data sources were combined to increase generalization performance. The approach yields good results for different data sources as shown by experiments on high-resolution multi-spectral and LiDAR imagery of cities in Norway, Denmark and France. We applied ConvNeXt and SegFormer based models on the high resolution aerial image dataset from the MapAI-competition. The methods achieved an IOU of 0.7902 and a boundary IOU of 0.6185. We used post-processing to account for the rectangular shape of the objects. This increased the boundary IOU from 0.6185 to 0.6189.


page 1

page 2


Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings

Automatic mapping of buildings from remote sensing imagery is currently ...

CNNs Fusion for Building Detection in Aerial Images for the Building Detection Challenge

This paper presents our contribution to the DeepGlobe Building Detection...

Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR

This paper presents an autonomous approach to tree detection and segment...

Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets

Automation of objects labeling in aerial imagery is a computer vision ta...

Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks

Building damage detection after natural disasters like earthquakes is cr...