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This paper proposes a method for automatically monitoring and analyzing theevolution of complex geographic objects. The objects are modeled as aspatiotemporal graph, which separates filiation relations, spatial relations,and spatiotemporal relations, and is analyzed by detecting frequent sub-graphsusing constraint satisfaction problems (CSP). The process is divided into foursteps: first, the identification of complex objects in each satellite image;second, the construction of a spatiotemporal graph to model the spatiotemporalchanges of the complex objects; third, the creation of sub-graphs to bedetected in the base spatiotemporal graph; and fourth, the analysis of thespatiotemporal graph by detecting the sub-graphs and solving a constraintnetwork to determine relevant sub-graphs. The final step is further broken downinto two sub-steps: (i) the modeling of the constraint network with definedvariables and constraints, and (ii) the solving of the constraint network tofind relevant sub-graphs in the spatiotemporal graph. Experiments wereconducted using real-world satellite images representing several cities inSaudi Arabia, and the results demonstrate the effectiveness of the proposedapproach.

Modeling Complex Object Changes in Satellite Image Time-Series: Approach based on CSP and Spatiotemporal Graph