Mapping the Spatial Characteristics of Agricultural Heritage Landscapes: A Knowledge-Guided GeoAI Approach

Agricultural heritage landscapes (AHLs) are multidimensional cultural landscapes shaped by interconnected productive, ecological, hydraulic, infrastructural, and sociocultural relationships. However, these dimensions are often assessed separately, limiting understanding of their spatial organization and internal differentiation. This study develops a knowledge-guided GeoAI approach for the multidimensional spatial characterization and assessment of AHLs. The approach integrates multisource geospatial data, high-resolution remote sensing, GIS-based spatial measurement, graph representation learning, and spatially constrained regionalization. It was applied to the Sangyuanwei Dike–Pond landscape in the Pearl River Delta, China. Four dimensions were examined through spatial indicators: Production and Livelihood, Water Management, Transportation Connectivity, and Ecological Networks. The results reveal strong associations between productive and ecological characteristics, while hydraulic organization and transportation connectivity show distinct relationships with traditional spatial patterns of land and water. Graph-learned configurations describe recurrent multidimensional combinations and their neighbourhood organization. Spatially constrained regionalization synthesizes these patterns into five geographically coherent and interpretable landscape types. By moving beyond separate element mapping, the approach provides integrated spatial evidence for landscape characterization and preliminary discussion of differentiated management. The integrated workflow offers a methodological reference for agricultural heritage landscape research, with potential applications in future environmental monitoring and vulnerability assessment.