{"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/crater-detection-via-convolutional-neural","title":"Crater Detection via Convolutional Neural Networks","arxiv_id":"1601.00978","date":"2016-01-05","proceeding":null,"authors":["Joseph Paul Cohen","Henry Z. Lo","Ting-ting Lu","Wei Ding"],"abstract":"Craters are among the most studied geomorphic features in the Solar System\nbecause they yield important information about the past and present geological\nprocesses and provide information about the relative ages of observed geologic\nformations. We present a method for automatic crater detection using advanced\nmachine learning to deal with the large amount of satellite imagery collected.\nThe challenge of automatically detecting craters comes from their is complex\nsurface because their shape erodes over time to blend into the surface.\nBandeira provided a seminal dataset that embodied this challenge that is still\nan unsolved pattern recognition problem to this day. There has been work to\nsolve this challenge based on extracting shape and contrast features and then\napplying classification models on those features. The limiting factor in this\nexisting work is the use of hand crafted filters on the image such as Gabor or\nSobel filters or Haar features. These hand crafted methods rely on domain\nknowledge to construct. We would like to learn the optimal filters and features\nbased on training examples. In order to dynamically learn filters and features\nwe look to Convolutional Neural Networks (CNNs) which have shown their\ndominance in computer vision. The power of CNNs is that they can learn image\nfilters which generate features for high accuracy classification.","url_abs":"http://arxiv.org/abs/1601.00978v1","url_pdf":"http://arxiv.org/pdf/1601.00978v1.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":"crater-detection-via-convolutional-neural","repo_url":"https://github.com/AlliedToasters/PyCDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}