{"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/a-step-towards-procedural-terrain-generation","title":"A step towards procedural terrain generation with GANs","arxiv_id":"1707.03383","date":"2017-07-11","proceeding":null,"authors":["Christopher Beckham","Christopher Pal"],"abstract":"Procedural terrain generation for video games has been traditionally been\ndone with smartly designed but handcrafted algorithms that generate heightmaps.\nWe propose a first step toward the learning and synthesis of these using recent\nadvances in deep generative modelling with openly available satellite imagery\nfrom NASA.","url_abs":"http://arxiv.org/abs/1707.03383v1","url_pdf":"http://arxiv.org/pdf/1707.03383v1.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":[{"paper_slug":"a-step-towards-procedural-terrain-generation","repo_url":"https://github.com/christopher-beckham/gan-heightmaps","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.03383","atlas_url":"https://app.syntology.ai/?focus=1707.03383","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}