Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas

Ros-Candeira, Andrea; Moreno-Llorca, Ricardo; Alcaraz‐Segura, Domingo; Bonet‐García, Francisco J.; Vaz, Ana Sofia

doi:10.3897/neobiota.38.38325

Cited by 12 publications

(1 citation statement)

References 27 publications

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“…It is included in the European Long-Term Socio-Ecological Research Infrastructure and is considered one of the most important biodiversity hotspots in the Mediterranean region, being listed under multiple conservation designations, e.g., Natural and National Parks, Natura 2000 Special Protection Area and Special Area of Conservation. Sierra Nevada supports numerous social-cultural activities with a focus on rural tourism and sport activities (Ros-Candeira et al, 2020).…”

Section: Targeted Areasmentioning

confidence: 99%

Deep learning assessment of cultural ecosystem services from social media images

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Moreno-Llorca

et al. 2021

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Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Geres region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Geres performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.

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Section: Targeted Areasmentioning

confidence: 99%