{"id":20202758,"url":"https://github.com/froukje/ai4eo-hyperview","last_synced_at":"2025-08-06T16:23:31.150Z","repository":{"id":178146196,"uuid":"541743897","full_name":"froukje/ai4eo-hyperview","owner":"froukje","description":"ML challenge to predict soil parameters from hyperspectral images.","archived":false,"fork":false,"pushed_at":"2022-10-17T21:13:12.000Z","size":23136,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-03T09:16:12.254Z","etag":null,"topics":["challenge","earth-observation","hyperview","machine-learning","random-forest"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/froukje.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-09-26T19:09:28.000Z","updated_at":"2025-02-10T01:34:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"97386c70-7b90-4cff-abfd-7cb52f791364","html_url":"https://github.com/froukje/ai4eo-hyperview","commit_stats":{"total_commits":255,"total_committers":11,"mean_commits":"23.181818181818183","dds":"0.19215686274509802","last_synced_commit":"27d79aff8ba280bcfc77d070164701419b79d625"},"previous_names":["froukje/ai4eo-hyperview"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/froukje/ai4eo-hyperview","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fai4eo-hyperview","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fai4eo-hyperview/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fai4eo-hyperview/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fai4eo-hyperview/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/froukje","download_url":"https://codeload.github.com/froukje/ai4eo-hyperview/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/froukje%2Fai4eo-hyperview/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269111098,"owners_count":24361648,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-06T02:00:09.910Z","response_time":99,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["challenge","earth-observation","hyperview","machine-learning","random-forest"],"created_at":"2024-11-14T04:58:06.712Z","updated_at":"2025-08-06T16:23:31.103Z","avatar_url":"https://github.com/froukje.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI4EO Hyperview\n\nThis repository contains the contribution of the team EagleEyes to the [AI4EO Hyperview Machine Learning Challenge](https://platform.ai4eo.eu/seeing-beyond-the-visible)\n\n**Overview**\n\nThe objective of the AI4EO HYPERVIEW challenge is to predict agriculturally relevant soil pa-\nrameters (K, Mg, P2O5, pH) from airborne hyperspectral images. We present a hybrid model fusing\nRandom Forest and K-nearest neighbor regressors that exploit the average spectral reflectance, as\nwell as derived features such as gradients, wavelet coefficients, and Fourier transforms. The solution\nis computationally lightweight and improves upon the challenge baseline by 21%.\n\n**This Repository contains the following:**\n\n* A jupyter notebook containing the final solution can be found in [final-submission](final-submission).\n* Conference paper: Kuzu, R.S., Albrecht, F., Arnold, C., Kamath, R.,Konen, K. (2022), [Predicting Soil Properties from Hyperspectral Satellite Images](hyperview_for_ICIP_camera_ready_eagleeyes.pdf), in 29th IEEE International Conference on Image Processing (IEEE ICIP 2022), Bordeaux, France.\n* The folder [notebooks](notebooks) contains some jupyter notebooks for data exploration and first simple models\n* We explored several other approaches, which can be found in [hyperview](hyperview)\n    * Different Neural Network architectures (based on keras) [NN keras](hyperview/keras])\n    * [PSELTAE model](https://github.com/VSainteuf/pytorch-psetae) (based on pytorch-lightning) [PSELTAE](hyperview/pytorch_lightning) \n    * [Random Forest and XGBoost models](random-forest)\n\n![hyperview_award.png](hyperview_award.png)\n\nA more detailed README can be found [here](https://github.com/ridvansalihkuzu/hyperview_eagleeyes)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffroukje%2Fai4eo-hyperview","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffroukje%2Fai4eo-hyperview","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffroukje%2Fai4eo-hyperview/lists"}