Abstract
Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model.
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Acknowledgements
This study was financially supported by the National Natural Science Foundation of China [Grant Number 52078493]; the Natural Science Foundation of Hunan Province [Grant Number 2022JJ30700]; the Natural Science Foundation for Excellent Young Scholars of Hunan [Grant Number 2021JJ20057]; the Innovation Provincial Program of Hunan Province [Grant Number 2020RC3002]; the Science and Technology Plan Project of Changsha [Grant Number kq2206014]; and the Innovation Driven Program of Central South University [Grant Number 2023CXQD033]. These financial supports are gratefully acknowledged.
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The research was funded by the National Key R&D Program of China (Grant No. 2018YFD1100401), the National Natural Science Foundation of China (Grant No. 52078493), the Natural Science Foundation for Excellent Young Scholars of Hunan (Grant No. 2021JJ20057), the Innovation Provincial Program of Hunan Province (Grant No. 2020RC3002).
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Fu, B., Li, Y., Han, Z. et al. RIPF-Unet for regional landslides detection: a novel deep learning model boosted by reversed image pyramid features. Nat Hazards 119, 701–719 (2023). https://doi.org/10.1007/s11069-023-06145-0
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DOI: https://doi.org/10.1007/s11069-023-06145-0