Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya)
Due to their individual shape, form, texture and colour variations, the automatic extraction of a building from high-resolution aerial photographs continues to be complicated. The Mask Region-based Convolutional neural network (Mask R-CNN) has shown recent improvements in object detection and extrac...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/130/e3sconf_igeos2024_06003.pdf |
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| _version_ | 1841557454464221184 |
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| author | Ramadhani Anisa Alya Nurul Fitri |
| author_facet | Ramadhani Anisa Alya Nurul Fitri |
| author_sort | Ramadhani Anisa |
| collection | DOAJ |
| description | Due to their individual shape, form, texture and colour variations, the automatic extraction of a building from high-resolution aerial photographs continues to be complicated. The Mask Region-based Convolutional neural network (Mask R-CNN) has shown recent improvements in object detection and extraction for updating data, which are superior to other methods. In this paper, a dataset consisting of aerial photography images acquired by aircraft in the urban and educational area of Institut Teknologi Sepuluh Nopember Surabaya to explore the potential of using Mask R-CNN, the art model, for instance, segmentation to automatically detect building footprints, which are essential attributes that define the urban fabric (which is critical to accelerating land cover updates with high highly accurate in terms of area and spatial assessment). The objective of this study was to implement Artificial Intelligence, especially with the Mask-RCNN method to perform building footprint detection. To enable this, aerial imagery was clipped into chip-sized images as training data for the model to learn. The model appeared to result in 73% precision. The model also shows the loss value graph, which represents the data well. Further study could focus on improving the precision of the model, which could also improve the result better. |
| format | Article |
| id | doaj-art-ee34d65c5a16434f925c9d8305bebe80 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-ee34d65c5a16434f925c9d8305bebe802025-01-06T11:30:22ZengEDP SciencesE3S Web of Conferences2267-12422024-01-016000600310.1051/e3sconf/202460006003e3sconf_igeos2024_06003Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya)Ramadhani Anisa0Alya Nurul Fitri1Geomatics Engineering Department, Faculty of Civil, Planning, and Geo-EngineeringMapping Survey and Geographic Information Department, Faculty of Social Sciences EducationDue to their individual shape, form, texture and colour variations, the automatic extraction of a building from high-resolution aerial photographs continues to be complicated. The Mask Region-based Convolutional neural network (Mask R-CNN) has shown recent improvements in object detection and extraction for updating data, which are superior to other methods. In this paper, a dataset consisting of aerial photography images acquired by aircraft in the urban and educational area of Institut Teknologi Sepuluh Nopember Surabaya to explore the potential of using Mask R-CNN, the art model, for instance, segmentation to automatically detect building footprints, which are essential attributes that define the urban fabric (which is critical to accelerating land cover updates with high highly accurate in terms of area and spatial assessment). The objective of this study was to implement Artificial Intelligence, especially with the Mask-RCNN method to perform building footprint detection. To enable this, aerial imagery was clipped into chip-sized images as training data for the model to learn. The model appeared to result in 73% precision. The model also shows the loss value graph, which represents the data well. Further study could focus on improving the precision of the model, which could also improve the result better.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/130/e3sconf_igeos2024_06003.pdf |
| spellingShingle | Ramadhani Anisa Alya Nurul Fitri Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) E3S Web of Conferences |
| title | Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) |
| title_full | Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) |
| title_fullStr | Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) |
| title_full_unstemmed | Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) |
| title_short | Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) |
| title_sort | building extraction from unmanned aerial vehicle imagery using mask rcnn case study institut teknologi sepuluh nopember surabaya |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/130/e3sconf_igeos2024_06003.pdf |
| work_keys_str_mv | AT ramadhanianisa buildingextractionfromunmannedaerialvehicleimageryusingmaskrcnncasestudyinstitutteknologisepuluhnopembersurabaya AT alyanurulfitri buildingextractionfromunmannedaerialvehicleimageryusingmaskrcnncasestudyinstitutteknologisepuluhnopembersurabaya |