{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/understanding-and-mapping-natural-beauty","title":"Understanding and Mapping Natural Beauty","arxiv_id":"1612.03142","date":"2016-12-09","proceeding":"ICCV 2017 10","authors":["Scott Workman","Richard Souvenir","Nathan Jacobs"],"abstract":"While natural beauty is often considered a subjective property of images, in\nthis paper, we take an objective approach and provide methods for quantifying\nand predicting the scenicness of an image. Using a dataset containing hundreds\nof thousands of outdoor images captured throughout Great Britain with\ncrowdsourced ratings of natural beauty, we propose an approach to predict\nscenicness which explicitly accounts for the variance of human ratings. We\ndemonstrate that quantitative measures of scenicness can benefit semantic image\nunderstanding, content-aware image processing, and a novel application of\ncross-view mapping, where the sparsity of ground-level images can be addressed\nby incorporating unlabeled overhead images in the training and prediction\nsteps. For each application, our methods for scenicness prediction result in\nquantitative and qualitative improvements over baseline approaches.","url_abs":"http://arxiv.org/abs/1612.03142v2","url_pdf":"http://arxiv.org/pdf/1612.03142v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"scenicornot","name":"ScenicOrNot","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.03142","atlas_url":"https://app.syntology.ai/?focus=1612.03142","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}