Deep-Learning-Based Edge Detection for Improving Building Footprint Extraction from Satellite Images

Abstract

Buildings are objects of great importance that need to be observed continuously. Satellite and aerial images provide valuable resources nowadays for building footprint extraction. Since these images cover large areas, manually detecting buildings will be a time-consuming task. Recent studies have proven the capability of deep learning algorithms in building footprint extraction automatically. But these algorithms need vast amounts of data for training and they may not perform well under the low-data conditions. Digital surface models provide height information, which helps discriminate buildings from their surrounding objects. However, they may suffer from noises, especially on the edges of buildings, which may result in low boundary resolution. In this research, we aim to address this problem by using edge bands detected by a deep learning model alongside the digital surface models to improve the building footprint extraction when training data are low. Since satellite images have complex backgrounds, using conventional edge detection methods like Canny or Sobel filter will produce a lot of noisy edges, which can deteriorate the model performance. For this purpose, first, we train a U-Net model for building edge detection with the WHU dataset and fine-tune the model with our target training dataset, which contains a low quantity of satellite images. Then, the building edges of the target test images are predicted using this fine-tuned U-Net and concatenated with our RGB-DSM test images to form 5-band RGB-DSM-Edge images. Finally, we train a U-Net with 5-band training images of our target dataset, which contain precise building edges in their fifth band. Then, we use this model for building footprint extraction from 5-band test images, which contain building edges in their fifth band that are predicted by a deep learning model in the first stage. We compared the results of our proposed method with 4-band RGB-DSM and 3-band RGB images. Our method obtained 82.88% in IoU and 90.45% in F1-score metrics, which indicates that, by using edge bands alongside the digital surface models, the performance of the model improved 2.57% and 1.59% in IoU and F1-score metrics, respectively. Also, the predictions made by 5-band images have sharper building boundaries than RGB-DSM images.

Keywords

• deep learning
• building footprint extraction
• edge detection
• U-Net
• digital surface model