| Abstract |
The exponential growth of human activities and the climate change put cities around the world in face of multiple risks and threats that led eventually to the emergence of a new urban model, which is the smart city resilience. Although being equipped with a myriad of connected smart devices and sensors, the smart city is still physically made up of buildings, roads, parks, industrial sites, shopping centers, etc. Therefore, location-based crisis management endorses a geospatial modeling strategy approach for major hazard data management in a smart city. Hence, spatial data remains always at the center of risk management processes. However, smart and resilient cities still strive to solve the imparity between the huge amounts of geospatial data generated mostly in real time in particular geographic user content contributions also known as Volunteered Geographic Information (VGI) and the delayed decision-making. In this paper, we reviewed major studies using VGI in big spatiotemporal data analytics in supporting smart city resilience. Then, we propose a vision of big spatiotemporal data architecture perquisites leveraging big data technologies, VGI and deep learning techniques for smart hazard management. |
