Advancing urban tree mapping with airborne data and deep learning

Urban trees are essential for sustaining biodiversity, mitigating climate impacts, and promoting public health. Among them, oak trees (Quercus spp.) hold particular ecological and cultural importance but are also associated with risks posed by the oak processionary caterpillar, an invasive pest with direct health implications. Effective monitoring and management of urban oaks require precise, scalable mapping tools that go beyond the limitations of traditional field surveys. This thesis develops a novel framework integrating airborne LiDAR, aerial photographs, and deep learning to detect and map individual oak trees across the Netherlands.
The research first addresses the persistent challenge of spatial misalignment between multimodal datasets. A new tree-oriented matching approach was designed to align aerial photographs and LiDAR point clouds at the individual tree level, significantly improving registration compared to conventional image-based methods. Building on this foundation, a decision-level fusion strategy was introduced to combine detection outputs from aerial photographs and LiDAR data, leveraging their complementary strengths. This approach achieved higher detection accuracy than pixel-level fusion by mitigating residual misregistration errors.
The thesis then advances species classification using side-view projections of LiDAR point clouds. By optimizing the number of projections and integrating intensity information, deep learning models demonstrated clear advantages over traditional classifiers, achieving robust performance even with low-density national LiDAR data. An EfficientNetV2-S architecture proved particularly effective for distinguishing English oak from morphologically similar species, with fine-tuning enabling strong model transferability across diverse urban environments.
These methodological innovations were combined into a nationwide case study, identifying more than two million oak trees across 1,045 Dutch urban areas. Spatial analyses revealed distinct patterns of oak distribution in relation to land use and population density, highlighting areas of heightened exposure risk to oak processionary caterpillars.
Overall, the thesis contributes a transferable and scalable deep learning framework for urban tree mapping that advances both ecological research and public health planning. By integrating multimodal airborne data with state-of-the-art machine learning, this work provides actionable tools for municipalities, urban planners, and biodiversity managers to design healthier, more resilient cities.