{"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/predicting-soil-properties-from-hyperspectral","title":"Predicting Soil Properties from Hyperspectral Satellite Images","arxiv_id":null,"date":"2022-10-18","proceeding":"Conference 2022 10","authors":["Rıdvan Salih Kuzu","Frauke Albrecht","Caroline Arnold","Roshni Kamath"],"abstract":"The AI4EO HYPERVIEW challenge seeks machine learning\r\nmethods that predict agriculturally relevant soil parameters\r\n(K, Mg, P2O5, pH) from airborne hyperspectral images.\r\nWe present a hybrid model fusing Random Forest and K-\r\nnearest neighbor regressors that exploit the average spectral\r\nreflectance, as well as derived features such as gradients,\r\nwavelet coefficients, and Fourier transforms. The solution is\r\ncomputationally lightweight and improves upon the challenge\r\nbaseline by 21.9%, with the first place on the public leader-\r\nboard. In addition, we discuss neural network architectures\r\nand potential future improvements.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9897254","url_pdf":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes/blob/master/challenge_submission_eagleeyes/hyperview_for_ICIP_camera_ready_eagleeyes.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":"predicting-soil-properties-from-hyperspectral","repo_url":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"seeing-beyond-the-visible","task_name":"Seeing Beyond the Visible"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/seeing-beyond-the-visible-on-hyperview","task":"Seeing Beyond the Visible","dataset":"HYPERVIEW","model":"RF + KNN","rank_in_archive_order":1,"of":1,"metrics":{"normalized MSE":"0.78113"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}