A review of building detection from very high resolution optical remote sensing images

Building detection from very high resolution (VHR) optical remote sensing images, which is an essential but challenging task in remote sensing, has attracted increased attention in recent years. However, despite the many methods that have been developed, an in-depth review of the recent literature on building extraction from VHR optical images is still lacking. In this article, we present a comprehensive review of the recent advances (since 2000) in this field. In total, we survey and summarize 417 articles in terms of the building detection method, post-processing, and accuracy assessment. The building detection methods are categorized into physical rule based methods, image segmentation based methods, and traditional and advanced machine learning (i.e. deep learning) methods. Furthermore, four promising related research directions of building polygon delineation, building change detection, building type classification, and height retrieval from monocular optical images are also discussed. Overall, building detection from VHR optical images is a popular research topic that has received extensive attention, due to its great significance. It is hoped that this review will help researchers to have a better understanding of this topic, and thus assist them to conduct related work.

Automated summary

This article provides a comprehensive review of the recent advancements in building extraction from very high resolution (VHR) optical remote sensing images. It categorizes the building detection methods into physical rule based methods, image segmentation based methods, and traditional and advanced machine learning methods. The article also discusses four promising research directions in building extraction and provides a better understanding of this topic for researchers.