A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES
Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-da...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/1101/2022/isprs-archives-XLIII-B2-2022-1101-2022.pdf |
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| author | I. Yalcin I. Yalcin R. Can S. Kocaman S. Kocaman C. Gokceoglu |
| author_facet | I. Yalcin I. Yalcin R. Can S. Kocaman S. Kocaman C. Gokceoglu |
| author_sort | I. Yalcin |
| collection | DOAJ |
| description | Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed. |
| first_indexed | 2024-04-12T11:03:57Z |
| format | Article |
| id | doaj.art-d2ff0a83ee484b0bb97a2ef5e8d9289a |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-12T11:03:57Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d2ff0a83ee484b0bb97a2ef5e8d9289a2022-12-22T03:35:52ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-05-01XLIII-B2-20221101110810.5194/isprs-archives-XLIII-B2-2022-1101-2022A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGESI. Yalcin0I. Yalcin1R. Can2S. Kocaman3S. Kocaman4C. Gokceoglu5Hacettepe University, Graduate School of Science and Engineering, Beytepe, Ankara, TurkeyHacettepe University, Başkent OSB Technical Sciences Vocational School, 06909 Sincan Ankara, TurkeyHacettepe University, Graduate School of Science and Engineering, Beytepe, Ankara, TurkeyHacettepe University, Dept. of Geomatics Engineering, 06800 Beytepe Ankara, TurkeyETH Zurich, Institute of Geodesy and Photogrammetry, 8093 Zurich, SwitzerlandHacettepe University, Dept. of Geological Engineering, 06800 Beytepe Ankara, TurkeyDetermination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/1101/2022/isprs-archives-XLIII-B2-2022-1101-2022.pdf |
| spellingShingle | I. Yalcin I. Yalcin R. Can S. Kocaman S. Kocaman C. Gokceoglu A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES |
| title_full | A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES |
| title_fullStr | A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES |
| title_full_unstemmed | A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES |
| title_short | A CNN ARCHITECTURE FOR DISCONTINUITY DETERMINATION OF ROCK MASSES WITH CLOSE RANGE IMAGES |
| title_sort | cnn architecture for discontinuity determination of rock masses with close range images |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2022/1101/2022/isprs-archives-XLIII-B2-2022-1101-2022.pdf |
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