Forest Inspection Dataset: A Synthetic UAV Dataset for Semantic Segmentation of Forest Environments

Abstract

This work describes the Forest Inspection dataset, a synthetic aerial image collection designed for semantic segmentation of forest environments with an emphasis on UAV-based forest inspection. The dataset consists of high-resolution RGB images paired with dense pixel-level semantic labels covering 11 classes, including deciduous trees, coniferous trees, fallen trees, ground vegetation, dirt ground, rocks, sky, buildings, fences, and vehicles. Images were generated in AirSim using a photorealistic virtual forest environment and captured with simulated UAV flights at three altitudes (30 m, 50 m, 80 m) and three camera pitch angles (0°, 60°, 90°) to reproduce diverse observation conditions, under two weather settings: sunny and overcast. Each data sample includes the corresponding UAV pose metadata for spatial context. The dataset is provided in standard image and annotation formats, accompanied by a description of the scene configuration and acquisition parameters. This resource is intended to support the development and evaluation of semantic segmentation models and other computer vision methods for UAV-based forest scene understanding and inspection.