Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images
Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remot...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/9/606 |
| _version_ | 1797518990283636736 |
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| author | Samitha Daranagama Apichon Witayangkurn |
| author_facet | Samitha Daranagama Apichon Witayangkurn |
| author_sort | Samitha Daranagama |
| collection | DOAJ |
| description | Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes. |
| first_indexed | 2024-03-10T07:37:04Z |
| format | Article |
| id | doaj.art-bf2254ec21f54a7e8745bcbc9a4980f6 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T07:37:04Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-bf2254ec21f54a7e8745bcbc9a4980f62023-11-22T13:25:05ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-09-0110960610.3390/ijgi10090606Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution ImagesSamitha Daranagama0Apichon Witayangkurn1Department of Information and Communication Technologies, School of Engineering and Technology, Asian Institute of Technology, Pathumthani 12120, ThailandSchool of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, ThailandBuildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes.https://www.mdpi.com/2220-9964/10/9/606deep learningbuilding extractionUAV imagesaerial imagessemantic segmentationtransfer learning |
| spellingShingle | Samitha Daranagama Apichon Witayangkurn Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images ISPRS International Journal of Geo-Information deep learning building extraction UAV images aerial images semantic segmentation transfer learning |
| title | Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images |
| title_full | Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images |
| title_fullStr | Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images |
| title_full_unstemmed | Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images |
| title_short | Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images |
| title_sort | automatic building detection with polygonizing and attribute extraction from high resolution images |
| topic | deep learning building extraction UAV images aerial images semantic segmentation transfer learning |
| url | https://www.mdpi.com/2220-9964/10/9/606 |
| work_keys_str_mv | AT samithadaranagama automaticbuildingdetectionwithpolygonizingandattributeextractionfromhighresolutionimages AT apichonwitayangkurn automaticbuildingdetectionwithpolygonizingandattributeextractionfromhighresolutionimages |