Identification of Vegetation Communities Using Segmentation of Aerial Images

ABSTRACT Aims Vegetation mapping remains a slow and costly process, as traditional approaches rely heavily on expert fieldwork. Therefore, the objective of this study was to develop an efficient and scalable methodology for generating vegetation maps by leveraging computer vision techniques on aerial imagery. Location The study focused on the island of Gran Canaria, an ecologically rich territory with heterogeneous environments where vegetation mapping is essential for environmental conservation, ecosystem monitoring, and biodiversity assessment. Methods Deep semantic segmentation techniques were employed to delineate vegetation communities from high‐resolution aerial imagery. A comparative analysis was conducted between widely used segmentation architectures. The methodology incorporated transfer learning with various backbones and evaluated performance across two versions of the dataset: One focused exclusively on vegetation communities and another that additionally included some non‐ecological classes such as shadows, roads, and water bodies. Finally, an aggregation of the vegetation communities was performed based on biological similarity. Results The results obtained revealed clear performance differences between models, with Feature Pyramid Network (FPN) consistently achieving the highest Dice and IoU scores across all dataset configurations, reaching approximately 70% Dice and 59% IoU in both aggregated versions of the dataset. The analysis also highlighted the benefits of class aggregation for improving segmentation quality in highly fragmented vegetation types. A final discussion examined these findings and outlined the methodological limitations and practical implications for ecological mapping. Conclusions The findings confirmed that deep learning‐based semantic segmentation enables the efficient generation of vegetation maps, even in ecologically complex territories. Although performance remained constrained by data availability and class complexity, the results demonstrated that these models can provide accurate and biologically meaningful representations of plant communities. The proposed framework therefore offers a solid foundation for supporting large‐scale ecological monitoring and for guiding future developments toward more detailed, scalable, and data‐rich vegetation mapping strategies.