{"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/in-domain-representation-learning-for-remote-1","title":"In-domain representation learning for remote sensing","arxiv_id":"1911.06721","date":"2019-11-15","proceeding":null,"authors":["Maxim Neumann","Andre Susano Pinto","Xiaohua Zhai","Neil Houlsby"],"abstract":"Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specifically, we investigate in-domain representation learning to develop generic remote sensing representations and explore which characteristics are important for a dataset to be a good source for remote sensing representation learning. The established baselines achieve state-of-the-art performance on these datasets.","url_abs":"https://arxiv.org/abs/1911.06721v1","url_pdf":"https://arxiv.org/pdf/1911.06721v1.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":"in-domain-representation-learning-for-remote-1","repo_url":"https://github.com/google-research/google-research/tree/master/remote_sensing_representations","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"multi-label-image-classification","task_name":"Multi-Label Image Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"scene-classification","task_name":"Scene Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-eurosat","task":"Image Classification","dataset":"EuroSAT","model":"ResNet50","rank_in_archive_order":5,"of":15,"metrics":{"Accuracy (%)":"99.2"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-resisc45","task":"Image Classification","dataset":"RESISC45","model":"ResNet50","rank_in_archive_order":1,"of":20,"metrics":{"Top 1 Accuracy":"96.83"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-so2sat-lcz42","task":"Image Classification","dataset":"So2Sat LCZ42","model":"ResNet50","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"63.25"},"uses_additional_data":false},{"leaderboard":"/sota/multi-label-image-classification-on","task":"Multi-Label Image Classification","dataset":"BigEarthNet","model":"ResNet50","rank_in_archive_order":6,"of":10,"metrics":{"mAP (macro)":"75.36"},"uses_additional_data":false},{"leaderboard":"/sota/scene-classification-on-uc-merced-land-use","task":"Scene Classification","dataset":"UC Merced Land Use Dataset","model":"ResNet50","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy (%)":"99.61"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1911.06721","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}