# Online Informative Path Planning for Active Classification Using UAVs

arXiv: https://arxiv.org/abs/1609.08446 · HTML: https://mlsanity.com/paper/1609.08446/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2016.

## Built on by (most cited first, 11 of 11)

- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs](https://mlsanity.com/paper/2201.04125/) (2022)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2108.01884/) (2021)
- [Active Classification of Moving Targets with Learned Control Policies](https://mlsanity.com/paper/2212.03068/) (2022)
- [Measurement Simplification in ρ-POMDP with Performance Guarantees](https://mlsanity.com/paper/2309.10701/) (2023)
- [Adaptive path planning for efficient object search by UAVs in agricultural fields](https://mlsanity.com/paper/2504.02473/) (2025)
- [Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles](https://mlsanity.com/paper/2310.17977/) (2023)
- [Coverage Path Planning for Thermal Interface Materials](https://mlsanity.com/paper/2405.13512/) (2024)
- [Attention-based Learning for 3D Informative Path Planning](https://mlsanity.com/paper/2506.08434/) (2025)
- [Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization](https://mlsanity.com/paper/2606.09188/) (2026)

## Abstract (arXiv)

In this paper, we introduce an informative path planning (IPP) framework for active classification using unmanned aerial vehicles (UAVs). Our algorithm uses a combination of global viewpoint selection and evolutionary optimization to refine the planned trajectory in continuous 3D space while satisfying dynamic constraints. Our approach is evaluated on the application of weed detection for precision agriculture. We model the presence of weeds on farmland using an occupancy grid and generate adaptive plans according to information-theoretic objectives, enabling the UAV to gather data efficiently. We validate our approach in simulation by comparing against existing methods, and study the effects of different planning strategies. Our results show that the proposed algorithm builds maps with over 50% lower entropy compared to traditional "lawnmower" coverage in the same amount of time. We demonstrate the planning scheme on a multirotor platform with different artificial farmland set-ups.