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Jiang Wandong, Xi Jiangbo, Li Zhenhong, Ding Mingtao, Yang Ligong, Xie Dashuai. Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200692
Citation: Jiang Wandong, Xi Jiangbo, Li Zhenhong, Ding Mingtao, Yang Ligong, Xie Dashuai. Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200692

Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

doi: 10.13203/j.whugis20200692
Funds:

Major Program of the National Natural Science Foundation of China (41941019), The National Key Research and Development Program of China (2018YFC1504805), The National Natural Science Foundation of China(61806022, 41874005), The Fundamental Research Funds for the Central Universities (300102260301/087, 300102260404/087, 300102269103, 300102269304, and 300102269205), the State Key Laboratory of Geographic Information (SKLGIE2018-M-3-4).

  • Received Date: 2020-12-24
  • With the advance in artificial intelligence, using high-resolution images to detect geological hazards has gradually become a research hotspot. Visual interpretation of landslides heavily relies on expert experience, and conventional automatic landslide detection approaches are sensitive to the presence of bare land, roads and other ground objects. To address these, a Mask R-CNN with simulated hard samples is presented in this paper for landslide detection and segmentation. Based on existing landslide samples, hard landslide samples are simulated by utilizing the shapes, colors, textures, and other characteristics of landslides to make each of the samples with a more complicated background. The original imagery and simulated hard samples are then fed into the Mask R-CNN for landslide detection and segmentation. Since the number of landslides is often limited in reality, small sample learning in the frequency domain is also presented in this paper to reduce the number of input samples while ensuring the accuracy of detection and segmentation. The experimental results in Bijie, Guizhou Province, showed that the detection and the average pixel segmentation accuracies of the proposed Mask R-CNN method with simulated hard samples are 94.0% and 90.3%, respectively. It is seen that the proposed method has high performance on landslide detection and segmentation with low false alarm rates. In addition, the performance of the proposed small-sample-based learning method in frequency domain can be improved even with a half of the data input. The effectiveness of the proposed Mask R-CNN method is further proved by the successful detection of Tianshui landslides in Gansu Province.
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Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples

doi: 10.13203/j.whugis20200692
Funds:

Major Program of the National Natural Science Foundation of China (41941019), The National Key Research and Development Program of China (2018YFC1504805), The National Natural Science Foundation of China(61806022, 41874005), The Fundamental Research Funds for the Central Universities (300102260301/087, 300102260404/087, 300102269103, 300102269304, and 300102269205), the State Key Laboratory of Geographic Information (SKLGIE2018-M-3-4).

Abstract: With the advance in artificial intelligence, using high-resolution images to detect geological hazards has gradually become a research hotspot. Visual interpretation of landslides heavily relies on expert experience, and conventional automatic landslide detection approaches are sensitive to the presence of bare land, roads and other ground objects. To address these, a Mask R-CNN with simulated hard samples is presented in this paper for landslide detection and segmentation. Based on existing landslide samples, hard landslide samples are simulated by utilizing the shapes, colors, textures, and other characteristics of landslides to make each of the samples with a more complicated background. The original imagery and simulated hard samples are then fed into the Mask R-CNN for landslide detection and segmentation. Since the number of landslides is often limited in reality, small sample learning in the frequency domain is also presented in this paper to reduce the number of input samples while ensuring the accuracy of detection and segmentation. The experimental results in Bijie, Guizhou Province, showed that the detection and the average pixel segmentation accuracies of the proposed Mask R-CNN method with simulated hard samples are 94.0% and 90.3%, respectively. It is seen that the proposed method has high performance on landslide detection and segmentation with low false alarm rates. In addition, the performance of the proposed small-sample-based learning method in frequency domain can be improved even with a half of the data input. The effectiveness of the proposed Mask R-CNN method is further proved by the successful detection of Tianshui landslides in Gansu Province.

Jiang Wandong, Xi Jiangbo, Li Zhenhong, Ding Mingtao, Yang Ligong, Xie Dashuai. Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200692
Citation: Jiang Wandong, Xi Jiangbo, Li Zhenhong, Ding Mingtao, Yang Ligong, Xie Dashuai. Landslide Detection and Segmentation Using Mask R-CNN with Simulated Hard Samples[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200692
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