Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples
Deep learning has been widely used in landslides detection. However, in practical application, the sample quality often cannot meet the requirements of training models. Some smaller landslides are easy to be omitted if there are multiple landslide objects in one sample. Furthermore, there are some o...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2023.2300823 |
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| _version_ | 1846126441267200000 |
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| author | Xiaohui Liu Ling Xu Jinyu Zhang |
| author_facet | Xiaohui Liu Ling Xu Jinyu Zhang |
| author_sort | Xiaohui Liu |
| collection | DOAJ |
| description | Deep learning has been widely used in landslides detection. However, in practical application, the sample quality often cannot meet the requirements of training models. Some smaller landslides are easy to be omitted if there are multiple landslide objects in one sample. Furthermore, there are some objects with similar shape, texture and colour to landslides (complex backgrounds), such as bare land, roads, water surfaces and artificial buildings. The traditional landslides detection method is easy to confuse landslides and complex backgrounds, which leads to false and omissive detections. To solve the above two problems, a complex background enhancement method with multi-scale samples (MSSCBE) was proposed to improve sample quality. Using the background enhanced samples, the deep learning model can not only learn differences between landslides and complex backgrounds, but also learn the multi-scale features of landslides better. The proposed method was applied to detect landslides that occurred in Jiuzhaigou County, Sichuan Province. Comparative experiments were conducted using Mask R-CNN model. And the model trained with both MSSCBE background enhanced samples and original samples has the best performance. Compared with the model trained with only original samples, Precision, Recall, F1 Score and mIoU is improved by 29.76%, 5.59%, 17.82% and 25.80%, respectively. |
| format | Article |
| id | doaj-art-a5eed17109a340248d49b13ef2a81e8f |
| institution | Kabale University |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-a5eed17109a340248d49b13ef2a81e8f2024-12-12T18:11:17ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132024-12-0115110.1080/19475705.2023.2300823Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samplesXiaohui Liu0Ling Xu1Jinyu Zhang2School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, Shandong, ChinaDeep learning has been widely used in landslides detection. However, in practical application, the sample quality often cannot meet the requirements of training models. Some smaller landslides are easy to be omitted if there are multiple landslide objects in one sample. Furthermore, there are some objects with similar shape, texture and colour to landslides (complex backgrounds), such as bare land, roads, water surfaces and artificial buildings. The traditional landslides detection method is easy to confuse landslides and complex backgrounds, which leads to false and omissive detections. To solve the above two problems, a complex background enhancement method with multi-scale samples (MSSCBE) was proposed to improve sample quality. Using the background enhanced samples, the deep learning model can not only learn differences between landslides and complex backgrounds, but also learn the multi-scale features of landslides better. The proposed method was applied to detect landslides that occurred in Jiuzhaigou County, Sichuan Province. Comparative experiments were conducted using Mask R-CNN model. And the model trained with both MSSCBE background enhanced samples and original samples has the best performance. Compared with the model trained with only original samples, Precision, Recall, F1 Score and mIoU is improved by 29.76%, 5.59%, 17.82% and 25.80%, respectively.https://www.tandfonline.com/doi/10.1080/19475705.2023.2300823Landslide detectioncomplex backgroundsbackground enhancementdeep learningmulti-scale samples |
| spellingShingle | Xiaohui Liu Ling Xu Jinyu Zhang Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples Geomatics, Natural Hazards & Risk Landslide detection complex backgrounds background enhancement deep learning multi-scale samples |
| title | Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples |
| title_full | Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples |
| title_fullStr | Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples |
| title_full_unstemmed | Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples |
| title_short | Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples |
| title_sort | landslide detection with mask r cnn using complex background enhancement based on multi scale samples |
| topic | Landslide detection complex backgrounds background enhancement deep learning multi-scale samples |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2023.2300823 |
| work_keys_str_mv | AT xiaohuiliu landslidedetectionwithmaskrcnnusingcomplexbackgroundenhancementbasedonmultiscalesamples AT lingxu landslidedetectionwithmaskrcnnusingcomplexbackgroundenhancementbasedonmultiscalesamples AT jinyuzhang landslidedetectionwithmaskrcnnusingcomplexbackgroundenhancementbasedonmultiscalesamples |