Building detection methods from remotely sensed images

Abstract

With the availability of high-resolution satellite ima-gery, new applications have been developed for solving geospatial issues in urban regions. Building detection from remote sensing images has been an active area of research due to its broad range of applications, includ-ing city modelling, map updating and urban monitoring. The manual processing of an image is a time-consuming and laborious task. Therefore, researchers have deve-loped methods that involve less or no human effort. At present, building detection has improved through vari-ous automated and semi-automated methods/algorithms/ techniques suggested in various studies. The objective of the present study is to review the efforts of such studies. Here, the building detection methods are categorized into six groups: (i) low-level feature-based methods, (ii) snake models, (iii) graph-based methods, (iv) shad-ow detection-based methods, (v) cognition-based meth-ods and (vi) deep learning models. We hope that this study will aid the researchers working in this domain.