Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data

TL;DR

This paper presents an open-source, end-to-end system that combines UAV and satellite data with deep learning to improve tree inventory and vitality assessment, aiding urban arborists and foresters in task planning.

Contribution

It introduces a novel multi-source data integration approach with deep learning for urban tree health monitoring and task planning support.

Findings

Successful integration of UAV, satellite, and sensor data for tree analysis.

Deep learning models effectively assess tree vitality.

Web application enhances task planning for arborists and foresters.

Abstract

Climate crisis and correlating prolonged, more intense periods of drought threaten tree health in cities and forests. In consequence, arborists and foresters suffer from increasing workloads and, in the best case, a consistent but often declining workforce. To optimise workflows and increase productivity, we propose a novel open-source end-to-end approach that generates helpful information and improves task planning of those who care for trees in and around cities. Our approach is based on RGB and multispectral UAV data, which is used to create tree inventories of city parks and forests and to deduce tree vitality assessments through statistical indices and Deep Learning. Due to EU restrictions regarding flying drones in urban areas, we will also use multispectral satellite data and fifteen soil moisture sensors to extend our tree vitality-related basis of data. Furthermore, Bamberg already has a georeferenced tree cadastre of around 15,000 solitary trees in the city area, which is also used to generate helpful information. All mentioned data is then joined and visualised in an interactive web application allowing arborists and foresters to generate individual and flexible evaluations, thereby improving daily task planning.