Smart City Gnosys
Smart city article details
| Title | Development Of Detailed Building Distribution Map To Support Smart City Promotion -An Approach Using Satellite Image And Deep Learning- |
|---|---|
| ID_Doc | 19678 |
| Authors | Okada K.; Nishiyama N.; Akiyama Y.; Miyazaki H.; Miyazawa S. |
| Year | 2022 |
| Published | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 4/W3-2022 |
| DOI | http://dx.doi.org/10.5194/isprs-annals-X-4-W3-2022-189-2022 |
| Abstract | Detailed demographics play an important role in the development of smart cities. However, especially in developing countries, the maintenance and management of this data is incomplete, which hinders the promotion of smart cities. The objective of this study is to develop a method to create detailed building distribution maps from satellite images, which will serve as a basis for developing detailed demographic data to support the promotion of smart cities around the world. The target area is several areas of Tokyo where validation data is available. We first developed a method for extracting buildings from satellite images and then estimating the building use to determine the buildings where residents are distributed. Both methods use deep learning. As a result, it was possible to extract buildings with an extraction rate (the number of buildings in the automatically extracted building data divided by the number of buildings in the data for verification) of up to 60.3% for the entire target area. In addition, in the estimation of building use, our method was able to classify detached and non-detached buildings with an average accuracy of 78.7% for the entire target area. © Author(s) 2022. |
| Author Keywords | Building Detection; Building Use; Deep Learning; Planning Support; Population; Satellite Image |
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