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Building detection methods from remotely sensed images


Affiliations
1 Wadia Institute of Himalayan Geology, 33-GMS Road, Dehradun 248 001, India
2 Formerly Uttarakhand Technical University, Dehradun 248 007, India
 

With the availability of high-resolution satellite imagery, 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, including city modelling, map updating and urban monitoring. The manual processing of an image is a time-consuming and laborious task. Therefore, researchers have developed methods that involve less or no human effort. At present, building detection has improved through various 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) shadow detection-based methods, (v) cognition-based methods and (vi) deep learning models. We hope that this study will aid the researchers working in this domain.

Keywords

Building detection, classification, geospatial issues, remote sensing images, urban areas.
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Abstract Views: 269

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  • Building detection methods from remotely sensed images

Abstract Views: 269  |  PDF Views: 134

Authors

Naveen Chandra
Wadia Institute of Himalayan Geology, 33-GMS Road, Dehradun 248 001, India
Himadri Vaidya
Formerly Uttarakhand Technical University, Dehradun 248 007, India

Abstract


With the availability of high-resolution satellite imagery, 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, including city modelling, map updating and urban monitoring. The manual processing of an image is a time-consuming and laborious task. Therefore, researchers have developed methods that involve less or no human effort. At present, building detection has improved through various 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) shadow detection-based methods, (v) cognition-based methods and (vi) deep learning models. We hope that this study will aid the researchers working in this domain.

Keywords


Building detection, classification, geospatial issues, remote sensing images, urban areas.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi11%2F1252-1267