INTERPRETABLE SCENICNESS FROM SENTINEL-2 IMAGERY

Landscape aesthetics, or scenicness, has been identified as an important ecosystem service that contribute to human health and well-being. Currently there are no methods to inventorize landscape scenicness on a large scale. In this paper we study how to upscale local assessments of scenicness provided by human observers, and we do so by using satellite images. Moreover, we develop an explicitly interpretable CNN model that allows assessing the connections between landscape scenicness and the presence of specific landcover types. To generate the landscape scenicness ground truth, we use the ScenicOrNot crowdsourcing database, which provides geo-referenced, human-based scenicness estimates for ground based photos in Great Britain. Our results show that it is feasible to predict landscape scenicness based on satellite imagery. The interpretable model performs comparably to an unconstrained model, suggesting that it is possible to learn a semantic bottleneck that represents well the present landcover classes and still contains enough information to accurately predict the location's scenicness.