# Informative Path Planning for Active Learning in Aerial Semantic Mapping

arXiv: https://arxiv.org/abs/2203.01652 · HTML: https://mlsanity.com/paper/2203.01652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2022.

## Built on by (most cited first, 2 of 2)

- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning](https://mlsanity.com/paper/2312.04402/) (2023)

## Abstract (arXiv)

Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new approach for using unmanned aerial vehicles (UAVs) to autonomously collect useful data for model training. We exploit a Bayesian approach to estimate model uncertainty in semantic segmentation. During a mission, the semantic predictions and model uncertainty are used as input for terrain mapping. A key aspect of our pipeline is to link the mapped model uncertainty to a robotic planning objective based on active learning. This enables us to adaptively guide a UAV to gather the most informative terrain images to be labelled by a human for model training. Our experimental evaluation on real-world data shows the benefit of using our informative planning approach in comparison to static coverage paths in terms of maximising model performance and reducing labelling efforts.