DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING–
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-10-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/189/2022/isprs-annals-X-4-W3-2022-189-2022.pdf |
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| author | K. Okada N. Nishiyama Y. Akiyama H. Miyazaki S. Miyazawa |
| author_facet | K. Okada N. Nishiyama Y. Akiyama H. Miyazaki S. Miyazawa |
| author_sort | K. Okada |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T13:33:50Z |
| format | Article |
| id | doaj.art-5ccb8e48d60d4d38a81afeafe5df103d |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-12T13:33:50Z |
| publishDate | 2022-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-5ccb8e48d60d4d38a81afeafe5df103d2022-12-22T03:31:05ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502022-10-01X-4-W3-202218919610.5194/isprs-annals-X-4-W3-2022-189-2022DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING–K. Okada0N. Nishiyama1Y. Akiyama2H. Miyazaki3S. Miyazawa4Tokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku,Tokyo, JapanYokosuka City Office, 11 Ogawa-cho, Yokosuka-shi, Kanagawa-ken, JapanTokyo City University, 1-28-1, Tamazutsumi, Setagaya-ku,Tokyo, JapanCenter for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, JapanLocationMind Inc.,701, 3-5-2, Iwamoto-cho, Chiyoda-ku, Tokyo, 101-0032, JapanDetailed 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.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/189/2022/isprs-annals-X-4-W3-2022-189-2022.pdf |
| spellingShingle | K. Okada N. Nishiyama Y. Akiyama H. Miyazaki S. Miyazawa DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– |
| title_full | DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– |
| title_fullStr | DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– |
| title_full_unstemmed | DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– |
| title_short | DEVELOPMENT OF DETAILED BUILDING DISTRIBUTION MAP TO SUPPORT SMART CITY PROMOTION -AN APPROACH USING SATELLITE IMAGE AND DEEP LEARNING– |
| title_sort | development of detailed building distribution map to support smart city promotion an approach using satellite image and deep learning |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/X-4-W3-2022/189/2022/isprs-annals-X-4-W3-2022-189-2022.pdf |
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