Urban infrastructural health monitoring and geo-hazard early-warning using InSAR technologies

Keywords: infrastructural health, sustainable development, InSAR
Abstract Type: Paper Abstract

Abstract

Global urbanization has created a huge demand for civil infrastructures, such as buildings, subways, bridges, dams, highways, airports, and railroads. Unfortunately, land subsidence and structural deformations under the combined effects of natural and anthropogenic activities are threatening infrastructural health and public safety. Like human’s health conditions, infrastructural health should also be monitored regularly to build a safe and resilient city. In this work, we develop innovative InSAR methods with deep learning analytics for monitoring large-scale land deformations and studying their interactions with environmental factors. Two important scientific issues will be specifically addressed. For one thing, we discuss how to accurately monitor land deformations and intelligently mine their spatiotemporal patterns in various geological, hydrological, and built environments through multi-source big data analytics. For another, we focus on exploring the interactions between land deformations and environmental factors and identifying risk indicators for early warning. This research advances fundamental scientific knowledge of large-scale infrastructural dynamics and their relationship with specific environmental factors and it strongly supports the United Nations Sustainable Development Goals.