# A Constraint Programming Model for the Super-Agile Earth Observation Satellite Imaging Scheduling Problem

arXiv: https://arxiv.org/abs/2601.11967 · HTML: https://mlsanity.com/paper/2601.11967/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Agile Earth observation satellite scheduling over 20 years: formulations, methods and future directions](https://mlsanity.com/paper/2003.06169/) (2020)
- [A Mixed Integer Linear Programming Model for Multi-Satellite Scheduling](https://mlsanity.com/paper/1811.12114/) (2018)

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

- [Optimizing Earth Observation Satellite Schedules under Unknown Operational Constraints: An Active Constraint Acquisition Approach](https://mlsanity.com/paper/2604.13283/) (2026)

## Abstract (arXiv)

As the dependence on satellite imaging continues to grow, modern satellites have become increasingly agile, with the new generation, namely super-agile Earth observation satellites (SAEOS), providing unprecedented imaging flexibility. The highly dynamic capabilities of these satellites introduce additional challenges to the scheduling of observation tasks, as existing approaches for conventional agile satellites do not account for variable observation durations and multiple imaging directions. Although some efforts have been made in this regard, the SAEOS imaging scheduling problem (SAEOS-ISP) remains largely unexplored, and no exact approaches have yet been proposed. In this context, this study presents the first exact Constraint Programming formulation for the SAEOS-ISP, considering flexible observation windows, multiple pointing directions and sequence-dependent transition times across multiple satellites. Computational experiments on a newly generated benchmark set demonstrate that the model can be solved efficiently and within very short computational times. Moreover, the results also show that the proposed approach has the potential to achieve higher computational performance compared to the non-exact approaches that are currently considered state-of-the-art.