KIND: Knowledge-Driven Identification and Precise Warning of Geological Disasters

Geological disasters have become one of the most significant environmental threats, creating risks to human life and economic development.

It is therefore imperative to conduct fine-grained precise disaster monitoring and early warning to guide disaster prevention and emergency rescue strategies, which can greatly contribute to saving lives, reducing economic losses, and safeguarding communities.

However, several critical challenges, including inefficient multi-dimensional geological environment remote sensing (GERS) data fusion, low generalization of data-driven AI models, and inconclusive disaster evolution patterns, remarkably limit the precise identification and warning of geological disasters from GERS data.

To address these challenges, this project will comprehensively integrate multi-modal GERS data and cutting-edge AI technologies to create an innovative knowledge-driven disaster identification and evolution associative analysis methodology with high accuracy, interpretability and reliability.

Specifically, 1) a novel multi-modal GERS data fusion method will be developed to effectively ensemble remote sensing images and monitoring data to obtain high-precision fused data; 2) An original knowledge-driven interpretation scheme will be designed to combine the spatial relationships and domain expert knowledge of geological environment in order to improve the precision and generalization of disaster and geological context identification; 3) A disaster evolution analysis mechanism will be developed to recognize the evolution process of typical geological disasters and warn early disaster signs; 4) A creative knowledge-driven method with high accuracy, interpretability and reliability will be developed for disaster identification and evolution analysis.

This project will revolutionize the way disasters are predicted and monitored, which empowers more swift responses to the geological threats, significantly reducing the devastation caused by geological disasters.