# Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring

arXiv: https://arxiv.org/abs/1703.02854 · HTML: https://mlsanity.com/paper/1703.02854/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

## Built on by (most cited first, 8 of 8)

- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration](https://mlsanity.com/paper/2208.03496/) (2022)
- [Optimizing Terrain Mapping and Landing Site Detection for Autonomous UAVs](https://mlsanity.com/paper/2205.03522/) (2022)
- [Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2108.01884/) (2021)
- [Cost Aware Asynchronous Multi-Agent Active Search](https://mlsanity.com/paper/2210.02259/) (2022)
- [Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization](https://mlsanity.com/paper/2605.15806/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) can offer timely and cost-effective delivery of high-quality sensing data. How- ever, deciding when and where to take measurements in complex environments remains an open challenge. To address this issue, we introduce a new multiresolution mapping approach for informative path planning in terrain monitoring using UAVs. Our strategy exploits the spatial correlation encoded in a Gaussian Process model as a prior for Bayesian data fusion with probabilistic sensors. This allows us to incorporate altitude-dependent sensor models for aerial imaging and perform constant-time measurement updates. The resulting maps are used to plan information-rich trajectories in continuous 3-D space through a combination of grid search and evolutionary optimization. We evaluate our framework on the application of agricultural biomass monitoring. Extensive simulations show that our planner performs better than existing methods, with mean error reductions of up to 45% compared to traditional "lawnmower" coverage. We demonstrate proof of concept using a multirotor to map color in different environments.