# Constraint Model for the Satellite Image Mosaic Selection Problem

arXiv: https://arxiv.org/abs/2312.04210 · HTML: https://mlsanity.com/paper/2312.04210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Geometric Dominating Set and Set Cover via Local Search](https://mlsanity.com/paper/1605.02499/) (2016)
- [Solution Dominance over Constraint Satisfaction Problems](https://mlsanity.com/paper/1812.09207/) (2018)
- [Faster Approximation Algorithms for Geometric Set Cover](https://mlsanity.com/paper/2003.13420/) (2020)

## Abstract (arXiv)

Satellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of interest is studied, several images must be stitched together to create a single larger image, called a mosaic, that can cover the area. Today, with the increasing number of satellite images available for commercial use, selecting the images to build the mosaic is challenging, especially when the user wants to optimize one or more parameters, such as the total cost and the cloud coverage percentage in the mosaic. More precisely, for this problem the input is an area of interest, several satellite images intersecting the area, a list of requirements relative to the image and the mosaic, such as cloud coverage percentage, image resolution, and a list of objectives to optimize. We contribute to the constraint and mixed integer lineal programming formulation of this new problem, which we call the textitsatellite image mosaic selection problem, which is a multi-objective extension of the polygon cover problem. We propose a dataset of realistic and challenging instances, where the images were captured by the satellite constellations SPOT, Pl'eiades and Pl'eiades Neo. We evaluate and compare the two proposed models and show their efficiency for large instances, up to 200 images.