With the advancement of satellite remote sensing technology, the accuracy of observation, as well as the breadth and frequency of data acquisition, have significantly increased. This has led to more diversified spatial information integration applications within the 3S (GIS, RS, GPS) and MMS (Mobile Mapping System) domains. To meet the high-demand needs of environmental monitoring applications, AI solutions have been introduced to develop automated spatial data fusion, remote sensing image recognition, and time-series forecasting models. These have already been successfully applied to issues such as real-time computation of hydrological parameters, agricultural crop interpretation and forest species classification, CCTV system image recognition, borehole and geophysical survey imagery interpretation for oil reservoir structures, road infrastructure object detection, and HD map spatial data extraction.

As countries accelerate the deployment of IoT monitoring equipment for smart cities, GIS and AI technologies are used to process IoT environmental big data and support spatial and temporal prediction and decision making.Geo-AIoT (GIS + AI + IoT) has clearly become a core and essential technology for smart cities. By integrating various social notification mechanisms and communication tools, it provides government agencies and the public with early-warning information and disaster alerts, enabling immediate understanding of disaster developments.These technologies have already been successfully applied to issues such as landslide disaster monitoring and early-warning mechanisms, hillside community safety monitoring, integrated smart watershed management, urban flood prevention and early warning, stormwater/wastewater sewer quality monitoring and simulation integration, natural disaster risk management, and insurance actuarial analysis.