Early identification method for landslide disaster based on Beidou unmanned aerial vehicle and remote sensing

By fusing multi-source data from BeiDou UAVs and remote sensing technology with the ConvNeXt-ViT model, the limitations of coverage, accuracy, and timeliness in landslide monitoring have been overcome, enabling efficient and accurate early warning for landslide identification.

CN121705856AActive Publication Date: 2026-03-20NANJING TECH UNIV
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Patent Information

Application Number
CN202610216209.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-14
Publication Date
2026-03-20
Estimated Expiration
2046-02-14

AI Technical Summary

Technical Problem

Existing technologies for landslide monitoring have limitations in coverage, accuracy, timeliness, and cost, making it difficult to achieve comprehensive, high-precision, real-time, and dynamic early identification and warning.

Method used

A multi-source monitoring data fusion method based on BeiDou UAV and remote sensing was adopted. Through collaborative preprocessing, spatiotemporal benchmark unification and multi-frequency time series coupling, the ConvNeXt-ViT landslide early warning model was used to extract multi-source features and make collaborative predictions. A spatiotemporally aligned fusion dataset was constructed to predict the landslide risk level and future deformation rate.

Benefits of technology

It has achieved comprehensive monitoring of landslides from "macro-trend to meso-level location to micro-level details", which has improved the comprehensiveness and accuracy of landslide deformation identification, enabled timely detection of potential risks, and provided a scientific basis for disaster prevention and mitigation.

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Abstract

The invention discloses an early landslide disaster recognition method based on a Beidou unmanned aerial vehicle and remote sensing, and belongs to the technical field of geological disaster monitoring. The method comprises the following steps: firstly, cooperatively acquiring and preprocessing SAR images, Beidou GNSS and unmanned aerial vehicle multi-source data; performing time sequence coupling fusion; then, SAR deformation intensity, Beidou displacement dynamic trend, unmanned aerial vehicle surface fracture and three-dimensional terrain multi-source features are extracted by using a CNN network and fused; inputting the fusion features into a pre-constructed ConvNeXt-ViT double-path landslide early warning model, and outputting a landslide risk grade and a deformation rate in the next 15 days; and finally, closed-loop early warning within 10 minutes is executed through an integrated software and hardware early warning system. According to the landslide early warning method, macroscopic-mesoscopic-microscopic all-directional monitoring of the landslide is achieved, the cross-regional adaptability of the model is high, the early warning precision and timeliness are remarkably improved, the false alarm rate is smaller than 10%, and the missing report rate is smaller than 5%.
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