Abstract
National reports on the total economic loss of geologic hazards demonstrate that landslides have serious negative economic impacts, especially in southwest of China. Most of geologic hazards are widely distributed, highly covert, abrupt, and devastating, which imposes more challenges on landslide detection in the early stage. Detection of slow landslides movement and prediction of the tendency of landslides in mountain terrains have great potential for crisis area targeting, hazard preventing, and people protection. Accordingly, in this study, we first propose a novel framework of artificial intelligent landslides detection over comprehensive remote sensing technique, from InSAR and high-resolution remote sensing images. The paper then discusses in detail how to construct background database with typical elements by categorizing sample sub-databases by versatile data features. In addition, a series of relevant key techniques of importance including heterogeneous data fusion, object-oriented surface coverage change detection, and intelligent comprehensive identification of hidden geohazards have been thoroughly investigated. The new framework has been successfully applied and verified in Jinsha River, China.
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Acknowledgements
This work was supported by Henan Key Laboratory of Spatial Information Application on Eco-environmental Protection. The authors also appreciate the efforts and suggestions from Liqiang Tong, Yi Wang and Jiancun Li, experts in Remote Sensing Geology from AGRS, China.
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Zhang, Y., Ran, H., Peng, Y., Zheng, Y. (2020). A Novel Framework of Artificial Intelligent Geologic Hazards Detection Over Comprehensive Remote Sensing. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019). CHREOC 2019. Lecture Notes in Electrical Engineering, vol 657. Springer, Singapore. https://doi.org/10.1007/978-981-15-3947-3_35
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DOI: https://doi.org/10.1007/978-981-15-3947-3_35
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