Journal Article

On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net

Doulamis, Anastasios, Temenos, Anastasios, Temenos, Nikos, Doulamis, Nikolaos

2022

Overview

Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks. In this work, we explore the effectiveness of the CNN-based architecture U-Net and its variations, namely, the Residual U-Net, the Attention U-Net, and the Attention Residual U-Net, in automatic building extraction. We showcase their robustness in feature extraction and information processing using exclusively RGB images, as they are a low-cost alternative to multi-spectral and LiDAR ones, selected from the SpaceNet 1 dataset. The experimental results show that U-Net achieves a 91.9% accuracy, whereas introducing residual blocks, attention gates, or a combination of both improves the accuracy of the vanilla U-Net to 93.6%, 94.0%, and 93.7%, respectively. Finally, the comparison between U-Net architectures and typical deep learning approaches from the literature highlights their increased performance in accurate building localization around corners and edges.

• Publisher: MDPI AG
• Subject: Artificial neural networks / attention residual U-Net / attention U-Net / automatic building extraction / Buildings / Classification / Color imagery / Data processing / Datasets / Deep learning / Digitization / Feature extraction / Image classification / Linear programming / Localization / Machine learning / Neural networks / Remote sensing / residual U-Net / Satellite imagery / semantic segmentation / Semantics / Sensors / U-Net / Unmanned aerial vehicles / Urban planning