首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI
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On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI

机译:论景观美容与土地覆盖的关系 - 以译员AI在Sentinel-2分辨率下的案例研究

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The environment where we live and recreate can have a significant effect on our well-being. More beautiful landscapes have considerable benefits to both health and quality of life. When we chose where to live or our next holiday destination, we do so according to some perception of the environment around us. In a way, we value nature and assign an ecosystem service to it. Landscape aesthetics, or scenicness, is one such service, which we consider in this paper as a collective perceived quality. We present a deep learning model called ScenicNet for the large-scale inventorisation of landscape scenicness from satellite imagery. We model scenicness with an interpretable deep learning model and learn a landscape beauty estimator based on crowdsourced scores derived from more than two hundred thousand landscape images in the United Kingdom. Our ScenicNet model learns the relationship between land cover types and scenicness by using land cover prediction as an interpretable intermediate task to scenicness regression. It predicts landscape scenicness and land cover from the Corine Land Cover product concurrently, without compromising the accuracy of either task. In addition, our proposed model is interpretable in the sense that it learns to express preferences for certain types of land covers in a manner that is easily understandable by an end-user. Our semantic bottleneck also allows us to further our understanding of crowd preferences for landscape aesthetics.
机译:我们生活和重新创建的环境可能对我们的福祉产生重大影响。更美丽的景观对健康和生活质量具有相当大的利益。当我们选择住在哪里或我们的下一个假期目的地时,我们会根据对我们周围环境的一些感知来这样做。在某种程度上,我们将重视性质并为其分配生态系统服务。景观美学或景区是一种这样的服务,我们认为本文作为集体感知的质量。我们为卫星图像展示了一个名为ScalicNet的深度学习模型,卫星图像的景观风景。我们使用可解释的深度学习模型模型,并根据英国的超过二十万景观形象,了解众群分数的景观美容估算。我们的风景模型通过使用土地覆盖预测作为一种可解释的中间任务来了解土地覆盖类型和风景的关系。它预测了普通景观景观和陆地覆盖,同时从Corine覆盖产品,而不会影响任一任务的准确性。此外,我们提出的模型是可解释的,即它学会以最终用户容易理解的方式表达某些类型的土地覆盖的偏好。我们的语义瓶颈还允许我们进一步了解对景观美学的人群偏好。

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