Gran canaria vegetation segmentation dataset from multi-year aerial imagery for environmental monitoring and conservation

Abstract

Vegetation maps are an essential tool for territorial planning, enabling the identification of areas requiring protection and facilitating the study of key ecosystem dynamics such as their evolution over time and the threats they face. These aspects are especially critical in island territories, where their fragmented nature and isolation from the mainland pose significant challenges to the development of such documentation. Traditionally, these maps have relied on local experts, requiring extensive fieldwork, significant time and financial resources. To address these challenges, a novel dataset focused on Gran Canaria (Canary Islands, Spain) is presented, designed to allow researchers to develop and test deep learning models that automatically generate vegetation maps using computer vision techniques.

This dataset is unique in the field of aerial image-based semantic segmentation, as it provides detailed annotations for 20 well-defined vegetation communities, going beyond the broad classifications commonly found in existing datasets (e.g., forests or grasslands). Additionally, an alternative version of the dataset includes five non-vegetal classes, such as water bodies, roads, or buildings to support more visually comprehensive segmentation tasks.