# Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning

arXiv: https://arxiv.org/abs/2603.06719 · HTML: https://mlsanity.com/paper/2603.06719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [Demonstrating Onboard Inference for Earth Science Applications with Spectral Analysis Algorithms and Deep Learning](https://mlsanity.com/paper/2508.15053/) (2025)
- [Real-Time Instrument Planning and Perception for Novel Measurements of Dynamic Phenomena](https://mlsanity.com/paper/2509.03500/) (2025)
- [Learning-Based Planning for Improving Science Return of Earth Observation Satellites](https://mlsanity.com/paper/2509.07997/) (2025)
- [Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations](https://mlsanity.com/paper/2601.06188/) (2026)

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

- [Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling](https://mlsanity.com/paper/2607.25835/) (2026)

## Abstract (arXiv)

The Dynamic Targeting (DT) mission concept is an approach to satellite observation in which a lookahead sensor gathers information about the upcoming environment and uses this information to intelligently plan observations. Previous work has shown that DT has the potential to increase the science return across applications. However, DT mission concepts must address challenges, such as the limited spatial extent of onboard lookahead data and instrument mobility, data throughput, and onboard computation constraints. In this work, we show how the performance of DT systems can be improved by using supplementary data streamed from geostationary satellites that provide lookahead information up to 35 minutes ahead of time rather than the 1 minute latency from an onboard lookahead sensor. While there is a greater volume of geostationary data, the search space for observation planning explodes exponentially with the size of the horizon. To address this, we introduce a hierarchical planning approach in which the geostationary data is used to plan a long-term observation blueprint in polynomial time, then the onboard lookahead data is leveraged to refine that plan over short-term horizons. We compare the performance of our approach to that of traditional DT planners relying on onboard lookahead data across four different problem instances: three cloud avoidance variations and a storm hunting scenario. We show that our hierarchical planner outperforms the traditional DT planners by up to 41% and examine the features of the scenarios that affect the performance of our approach. We demonstrate that incorporating geostationary satellite data is most effective for dynamic problem instances in which the targets of interest are sparsely distributed throughout the overflight.