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

arXiv: https://arxiv.org/abs/2305.15091 · HTML: https://mlsanity.com/paper/2305.15091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 32 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 1 of 1)

- [A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving: Application to Remote Sensing](https://mlsanity.com/paper/2106.05193/) (2021)

## Built on by (most cited first, 1 of 1)

- [On the use of Graphs for Satellite Image Time Series](https://mlsanity.com/paper/2505.16685/) (2025)

## Abstract (arXiv)

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.