EGU21-13974
https://doi.org/10.5194/egusphere-egu21-13974
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA

Te Pei1, Savinay Nagendra2, Srikanth Banagere Manjunatha2, Guanlin He2, Daniel Kifer2, Tong Qiu1, and Chaopeng Shen1
Te Pei et al.
  • 1Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, United States of America
  • 2Department of Computer Science, Pennsylvania State University, University Park, United States of America

Landslides are common natural disasters around the globe. Understanding the accurate spatial distribution of landslides is essential for landslide analysis, prediction, and hazard mitigation. So far, many techniques have been used for landslide mapping to establish landslide inventories. However, these techniques either have a low automation level (e.g., visual interpretation-based methods) or a low generalization ability (e.g., pixel-based or object-based approaches); and improvements are required for landslide mapping. Therefore, we have developed an interactive, user-friendly web portal for landslide labeling. The web portal takes multi-temporal satellite images as inputs. A deep learning model will first detect landslide-suspicious areas in the image and present results to users for validation. Users can then review and annotate these machine-labeled landslides through a user-friendly interface. Users’ editions on landslide annotation will further improve the accuracy of the deep learning model. Two landslide-affected regions in Washington were selected to test the capability of our web portal for landslide mapping. The detected landslides were validated by expert labelers. The results indicated that our annotation tool was able to produce landslide maps with high precision, a high rate of annotation, and reduced human efforts.

How to cite: Pei, T., Nagendra, S., Banagere Manjunatha, S., He, G., Kifer, D., Qiu, T., and Shen, C.: Utilizing an interactive AI-empowered web portal for landslide labeling for establishing a landslide database in Washington state, USA, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13974, https://doi.org/10.5194/egusphere-egu21-13974, 2021.

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