ISSN 2278-9723
 

Research Article 


A New Deep-Learning-based Approach for Earthquake triggered Landslide

MOUNIKA VELAGANA, D SRINIVAS.

Abstract
Accurate landslide detection and mapping are essential for land use planning, management/ assessment, and geo disaster risk mitigation as well as post-disaster reconstructions. Till now, visual interpretation and field survey are still the most widely adopted techniques for landslide mapping, which are often criticized labour-intensive, time-consuming, and costly. With the rapid advancement of artificial intelligence, deep learning-based approach for landslide detection and mapping has drawn great attention for its significant advantages over the traditional techniques. However, lack of sufficient training samples has constrained the application of deep-learning-based approach in landslide detection from satellite images for a long time. The present study aimed to examine the feasibility of a new deep-learning-based approach to intelligently detect and map earthquake - triggered landslides from single-temporal Rapid Eye satellite images. Specifically, the proposed approach consists of three steps. First of all, a standard data pre-processing
workflow to automatically generate training samples was designed and some data augmentation strategies were implemented to alleviate the lack of training samples. Then, a cascaded end-to-end deep learning network, namely LandsNet, was constructed to learn various features of landslides. Finally, the identified landslide maps were further optimized with morphological processing. Experiments in two spatially independent earthquake-affected regions showed the proposed approach yielded the best F1 value of about 86.89%, which was about 7% and 8% higher than that obtained by ResUNet and DeepUNet, respectively. Comparative studies on the feasibility and robustness of the proposed approach with ResUNet and DeepUNet demonstrated its strong application potentials in the emergency response of natural disasters.

Key words: learning, constructed, images, landslide.


 
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Pubmed Style

MOUNIKA VELAGANA, D SRINIVAS. A New Deep-Learning-based Approach for Earthquake triggered Landslide. . 2023; 11(2): 127-134. doi:10.31838/ijccts/11.02.16


Web Style

MOUNIKA VELAGANA, D SRINIVAS. A New Deep-Learning-based Approach for Earthquake triggered Landslide. https://www.ijccts.org/?mno=144832 [Access: February 28, 2023]. doi:10.31838/ijccts/11.02.16


AMA (American Medical Association) Style

MOUNIKA VELAGANA, D SRINIVAS. A New Deep-Learning-based Approach for Earthquake triggered Landslide. . 2023; 11(2): 127-134. doi:10.31838/ijccts/11.02.16



Vancouver/ICMJE Style

MOUNIKA VELAGANA, D SRINIVAS. A New Deep-Learning-based Approach for Earthquake triggered Landslide. . (2023), [cited February 28, 2023]; 11(2): 127-134. doi:10.31838/ijccts/11.02.16



Harvard Style

MOUNIKA VELAGANA, D SRINIVAS (2023) A New Deep-Learning-based Approach for Earthquake triggered Landslide. , 11 (2), 127-134. doi:10.31838/ijccts/11.02.16



Turabian Style

MOUNIKA VELAGANA, D SRINIVAS. 2023. A New Deep-Learning-based Approach for Earthquake triggered Landslide. International Journal of Communication and Computer Technologies, 11 (2), 127-134. doi:10.31838/ijccts/11.02.16



Chicago Style

MOUNIKA VELAGANA, D SRINIVAS. "A New Deep-Learning-based Approach for Earthquake triggered Landslide." International Journal of Communication and Computer Technologies 11 (2023), 127-134. doi:10.31838/ijccts/11.02.16



MLA (The Modern Language Association) Style

MOUNIKA VELAGANA, D SRINIVAS. "A New Deep-Learning-based Approach for Earthquake triggered Landslide." International Journal of Communication and Computer Technologies 11.2 (2023), 127-134. Print. doi:10.31838/ijccts/11.02.16



APA (American Psychological Association) Style

MOUNIKA VELAGANA, D SRINIVAS (2023) A New Deep-Learning-based Approach for Earthquake triggered Landslide. International Journal of Communication and Computer Technologies, 11 (2), 127-134. doi:10.31838/ijccts/11.02.16