AI-driven landscape values mapping

Understanding how people perceive and value landscapes is essential for sustainable planning and conservation; yet, traditional methods remain limited in scale and scope. This study introduces artificial intelligence (AI)-Perceptual Landscape Mapping (AI-PLM), an integrated analytical framework that combines geospatial intelligence, machine learning, and natural-language processing (NLP) to model collective human perception from social-media data. Using nearly 29 000 geotagged Flickr photographs and 148 000 user comments from Romania, AI-PLM operationalizes perception through three components: (1) Data collection and processing (systematic collection and normalization of multilingual, multimodal content), (2) AI-Spatial Cognition (identification of perception hotspots via Head/Tail Breaks and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering combined with viewshed analysis), and (3) Affective-Semantic Intelligence (sentiment and topic modeling using transformer-based NLP). Results reveal strong spatial hierarchies of landscape appreciation, with intensity peaks in the Carpathians, Braşov, Bucharest, Maramureş, and the Black Sea coast. Sentiment analysis shows predominantly positive emotions associated with nature-oriented regions, while topic modeling highlights the prevalence of themes related to photography, heritage, and recreation. Together, these multimodal insights demonstrate a clear relationship between visibility, spatial clustering, and affective tone. The AI-PLM framework, thus, bridges physical geography and emotional expression, providing a scalable and transferable methodology for assessing cultural ecosystem services. By translating unstructured digital traces into structured spatial and semantic indicators, it advances the understanding of human-landscape interactions and offers practical tools for data-driven landscape management, conservation, and tourism planning in Romania and beyond.