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For various urban spatial elements, the method development and practical applications are in the center of the intelligent monitoring and spatial deduction simulation using multi-source remote sensing and GIS. The monitoring and simulation are of great significance to territorial and spatial planning and management, urban planning and comprehensive control, and regional decision-making and management. The coverage and driving elements in urban areas are complex and nonlinear, thus we have developed a few intelligent identification methods (e.g. the intelligent adaptive decision tree classifier) that use multi-source remote sensing data and can derive highly accurate and reliable coverage element results. By integrating multi-source remote sensing, POI, and spatiotemporal big data, we have developed new methods that can effectively detect and identify the driving forces of urban element changes. Urban simulation and deduction are advanced modeling based on the spatial monitoring of remote sensing for urban management and decision-making. We systematically have studied the urban deduction and prediction method based on urban spatial evolution mechanisms, spatial statistical modeling, and heuristic intelligent modeling, and applied these methods to simulate complex land use, urban expansion, ecological evolution, and carbon storage. Among the platforms available, we have developed two state-of-art software packages (i.e. UrbanCA and Futureland) in which the former focuses on urban growth and the latter focuses on multiple types of land-use change, and both integrate a variety of advanced methods, which have been successfully verified in the Yangtze River Delta.
