  This paper proposes a method for automatically monitoring and analyzing the
evolution of complex geographic objects. The objects are modeled as a
spatiotemporal graph, which separates filiation relations, spatial relations,
and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs
using constraint satisfaction problems (CSP). The process is divided into four
steps: first, the identification of complex objects in each satellite image;
second, the construction of a spatiotemporal graph to model the spatiotemporal
changes of the complex objects; third, the creation of sub-graphs to be
detected in the base spatiotemporal graph; and fourth, the analysis of the
spatiotemporal graph by detecting the sub-graphs and solving a constraint
network to determine relevant sub-graphs. The final step is further broken down
into two sub-steps: (i) the modeling of the constraint network with defined
variables and constraints, and (ii) the solving of the constraint network to
find relevant sub-graphs in the spatiotemporal graph. Experiments were
conducted using real-world satellite images representing several cities in
Saudi Arabia, and the results demonstrate the effectiveness of the proposed
approach.
