# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 1 Introduction  
Road collapse is defined as a phenomenon in which the instability of underground spatial structures leads to the damage of the overlying pavement layers, resulting in the subsidence or collapse of the road surface into the cavity below. This process is usually accompanied by the formation of collapse pits of varying scales and morphologies on the ground surface, directly threatening road safety and the normal operation of urban functions [1].  
On 7 October 2018, a road collapse occurred in Dazhou, Sichuan, resulting in four fatalities and one injury. On 13 January 2021, another road collapse incident took place in Xining, Qinghai, causing 10 deaths and 17 injuries. In addition to these incidents, according to incomplete statistics, from 2014 to 2018, a total of 2322 large-scale road collapse accidents occurred in China [2]. The total length of roads affected by these disasters reached hundreds of kilometers, with direct economic losses amounting to tens of billions of yuan.  
Studies have revealed that collapse accidents damage the structure and function of roads, leading to traffic disruptions and posing severe threats to the safety of pedestrians and vehicles. From a long-term perspective, road collapses can also influence the dynamic processes of urban traffic, such as altering traffic flow distribution, shortening the service life of roads, and increasing maintenance costs. These impacts have further profoundeffects on the operational efficiency and sustainability of urban road systems. Moreover, collapses may cause a series of problems, including the rupture of underground pipelines and groundwater contamination [3].  
In recent years, with the continuous development of artificial intelligence technology, machine learning and deep learning methods have shown significant advantages in spatial modeling and risk assessment. [PERSON] and [PERSON] [4] constructed an urban road collapse risk assessment model based on the support vector machine algorithm and applied it to a specific area in Guilin, Guangxi, analyzing the influence of various environmental variables on road collapse. [PERSON] et al. [5] employed a Bayesian network model to evaluate the susceptibility and hazard of comprehensive geological disasters. [PERSON] et al. [6] proposed a method that couples an information quantity model with a logistic regression model, using hundreds of disaster occurrence points as research objects to verify the predictive ability of the coupled model. [PERSON] et al. [7] proposed a hybrid collapse prediction model that integrates factor analysis, the genetic algorithm, and the extreme learning machine. With the same number of hidden layer neuron nodes, this model exhibited a higher prediction accuracy. Additionally, various deep learning methods, such as BP neural networks [8] and CNN [9], have gradually begun to be applied in road collapse assessment work in specific areas, demonstrating high accuracy and applicability.  
In this study, we introduce a novel approach that harnesses the temporal dynamics of collapse incidents using InSAR data. By employing a Long Short-Term Memory (LSTM) network, we effectively capture and model the subsidence trends over time. This temporal analysis allows us to establish a strong correlation between the temporal variations in the InSAR data and the risk of road collapse. Our approach not only highlights the temporal dimension of collapse events but also integrates spatial coordinates to provide a comprehensive risk assessment. This method addresses the limitations of traditional risk assessment models by focusing on the temporal evolution of subsidence, thus offering a more dynamic and predictive tool for urban planners and safety officials.