Modelling cultural ecosystem services of river landscapes in the Iberian Peninsula with deep learning and social media images

Abstract

Cultural Ecosystem Services (CES) are essential for human well-being, particularly those provided by river landscapes. Yet, CES remains overlooked in river conservation strategies due to its intangible nature and the methodological challenges involved in their assessment. This study introduces a novel AI-based framework that integrates deep learning for image recognition and machine learning for modelling to assess CES across river landscapes at regional scale. ResNet-152 convolutional neural network was fine-tuned to classify 6911 Flickr images into CES categories. The classified photos were then linked to biophysical variables using an XGBoost model, enabling interpretable predictions of biophysical CES drivers across heterogeneous landscapes. Residual analysis of population-based predictions revealed spatial clusters of “added CES value,” highlighting cultural benefits not explained by demographic factors alone. This integrated approach goes beyond previous CES assessments by combining automated image classification, large-scale spatial mapping of CES, and interpretable modelling of biophysical variables, allowing the cost-effective identification of under-recognized CES hotspots. Findings highlight the value of quotidian urban rivers and protected areas as key CES hotspots. The framework is transferable, reproducible, and openly available, thereby bridging AI methods and conservation planning

This research was funded by the European Union Horizon 2020 project MERLIN (H2020-LC-GD-2020-3: 101036337). Authors acknowledge the support from the Economy and Knowledge Department of the Catalan Government through Consolidated Research Groups (ICRA-ENV 2021 SGR 01282), as well as from the CERCA program. F. Comalada acknowledges funding from the Department of Research and Universities of the Generalitat de Catalunya and the European Social Fund for her FI fellowship (2024 FI-1 00133). Open Access funding provided thanks to the CRUE-CSIC agreement with Elsevier

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