Landslide detection based on deep learning and Google Earth imagery

Jun Xia; Hongjiang Liu; Linfu Zhu

doi:10.1117/12.2637088

15 June 2022 Landslide detection based on deep learning and Google Earth imagery

Jun Xia, Hongjiang Liu, Linfu Zhu

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 122850J (2022) https://doi.org/10.1117/12.2637088
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

Landslide geological disasters have seriously threatened human life and property safety. This paper takes the Wenchuan earthquake landslide as the research object, and uses five deep learning models (LeNet5, AlexNet, VGG16, ResNet152V2 and DenseNet201) to explore the landslide detection method based on high-resolution Google Earth images, select the optimal model, and then use Google Earth to detect landslides. The images are cropped into image samples of 4 different sizes (60×60, 120×120, 180×180 and 240×240 Pixel), and the dataset is then trained, validated and tested using the optimal model. The results show that: (1) Among the five deep learning models, the DenseNet201 model is the best, the F1-Score is the largest, reaching 0.8878, and the RMSE is the smallest 0.2524; (2) In the Google Earth image sample datasets of four different sizes, the DenseNet201 deep learning model is used to analyze the landslide images. For detection, the F1-Score can reach 0.8995, the RMSE can reach 0.2486, and the Accuracy can reach 0.9308. It can be seen that based on high-resolution Google Earth images, the deep learning method can quickly and accurately detect landslide information, providing a method reference for the prevention and control of landslide geological hazards.

Citation Download Citation

Jun Xia, Hongjiang Liu, and Linfu Zhu "Landslide detection based on deep learning and Google Earth imagery", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 122850J (15 June 2022); https://doi.org/10.1117/12.2637088

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KEYWORDS

Landslide (networking)

Data modeling

Statistical modeling

Convolutional neural networks

Earthquakes

Image classification

Remote sensing

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