{"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/earthmapper-a-tool-box-for-the-semantic","title":"EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery","arxiv_id":"1804.00292","date":"2018-04-01","proceeding":null,"authors":["Ronald Kemker","Utsav B. Gewali","Christopher Kanan"],"abstract":"Deep learning continues to push state-of-the-art performance for the semantic\nsegmentation of color (i.e., RGB) imagery; however, the lack of annotated data\nfor many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents\nresearchers from taking advantage of this recent success. Since generating\nsensor specific datasets is time intensive and cost prohibitive, remote sensing\nresearchers have embraced deep unsupervised feature extraction. Although these\nmethods have pushed state-of-the-art performance on current HSI benchmarks,\nmany of these tools are not readily accessible to many researchers. In this\nletter, we introduce a software pipeline, which we call EarthMapper, for the\nsemantic segmentation of non-RGB remote sensing imagery. It includes\nself-taught spatial-spectral feature extraction, various standard and deep\nlearning classifiers, and undirected graphical models for post-processing. We\nevaluated EarthMapper on the Indian Pines and Pavia University datasets and\nhave released this code for public use.","url_abs":"http://arxiv.org/abs/1804.00292v1","url_pdf":"http://arxiv.org/pdf/1804.00292v1.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":"earthmapper-a-tool-box-for-the-semantic","repo_url":"https://github.com/rmkemker/EarthMapper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"segmentation-of-remote-sensing-imagery","task_name":"Segmentation Of Remote Sensing Imagery"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"the-semantic-segmentation-of-remote-sensing","task_name":"The Semantic Segmentation Of Remote Sensing Imagery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}