AI-driven fence identification and mapping for large-landscape conservation

Fence is a globally ubiquitous form of linear infrastructure that interrupts animal movement, compromises population fitness, alters ecological processes, and diminishes landscape connectivity. However, fences are also widely used as conservation and land-management tools to mitigate human–wildlife conflict. Despite their significant relevance to biodiversity conservation, they remain largely absent from regional to global biodiversity assessments, in contrast to other linear infrastructures such as roads and railways. This omission stems primarily from the absence of large-scale spatial datasets and scalable, consistent mapping methods. Existing efforts rely heavily on field surveys or manual interpretation of remote sensing imagery, both of which are time-consuming, labor-intensive, and difficult to scale, thereby limiting their broader applicability and long-term utility.

Here, we introduce FenceMapper, an AI-driven framework for accurate and scalable fence detection using multi-scale segmentation of high-resolution remote sensing imagery. A local model extracts fine-scale fence features from small image patches, while a global refinement model improves structural continuity by incorporating broader contextual information and reducing fragmentation. Using 972 sampling sites containing 24,632 km of fences and more than 170,000 image patches across western U.S. rangelands, we trained and evaluated the framework and demonstrated robust performance: FenceMapper achieved 77% correctness, 75% completeness, and 76% quality under a 10 m tolerance, with highly consistent fence-length estimates (R² = 0.83). We then scaled the workflow to map 740,283 km of fences across the rangelands of the western United States, producing the most comprehensive sub-continental fence dataset to date.

FenceMapper and the resulting open-access dataset provide a critical missing layer for biodiversity monitoring. For example, ecologists can pair spatial fence location with animal tracking or occurrence data to assess how fences alter animal behavior and influence community dynamics. Large-scale spatial fence density complements current landscape connectivity assessment, hence provides critical information for landscape-level conservation prioritization across diverse ecological settings. This work demonstrates how remote sensing and AI can address key gaps in global biodiversity observation systems and strengthen the integration of structural landscape information into conservation planning and decision support.