Poster Abstract: Generative Modeling of Post-Disaster POI Visits Recovery

The development of Internet of Things (IoT) systems has enabled disaster perception and prediction to be highly accurate. On this basis, high-quality post-disaster Point of Interest (POI) visit data can help city decision-makers develop more sophisticated recovery plans to minimize the cost of recovery. This work focuses on the problem of POI visits generation in post-disaster recovery scenarios, utilizing diffusion model to generate visit recovery curves base on the data from sensor networks. We take the disaster severity as condition and propose a disaster mapping method to map the sensor data to each POI.