{"id":18339935,"url":"https://github.com/mxagar/space_exploration","last_synced_at":"2025-09-29T07:42:52.906Z","repository":{"id":235720988,"uuid":"613720318","full_name":"mxagar/space_exploration","owner":"mxagar","description":"This repository is a collection of mini-projects and tutorials related to space images and geo-spatial data.","archived":false,"fork":false,"pushed_at":"2023-05-02T08:12:55.000Z","size":25211,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-09T20:47:45.888Z","etag":null,"topics":["data-analysis","deep-learning","geospatial","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mxagar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-03-14T06:13:33.000Z","updated_at":"2023-03-14T11:24:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"47f86f3f-92bb-41fb-80c2-619b3acfaa66","html_url":"https://github.com/mxagar/space_exploration","commit_stats":null,"previous_names":["mxagar/space_exploration"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mxagar/space_exploration","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fspace_exploration","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fspace_exploration/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fspace_exploration/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fspace_exploration/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mxagar","download_url":"https://codeload.github.com/mxagar/space_exploration/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mxagar%2Fspace_exploration/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":277483294,"owners_count":25825560,"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":"2025-09-29T02:00:09.175Z","response_time":84,"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":["data-analysis","deep-learning","geospatial","machine-learning"],"created_at":"2024-11-05T20:19:54.068Z","updated_at":"2025-09-29T07:42:52.876Z","avatar_url":"https://github.com/mxagar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Space Exploration\n\nThis repository is a collection of mini-projects and tutorials related to space images and geo-spatial data. It is a sandbox where I'll try tools and techniques; as such, it will grow organically.\n\nEach mini-project has a dedicated folder with a `README.md`. Datasets should be contained in `data`, however, image data is usually not commited; instead, I provide the necessary download links.\n\n## Contents\n\n- [`space_image_classification`](./space_image_classification/): CNN which classifies space images from  [Satellite Image Classification](https://www.kaggle.com/datasets/mahmoudreda55/satellite-image-classification); RBG images are used and the classification is per image.\n- [`geospatial_data_guide`](./geospatial_data_guide/): a guide on how to visualize and work with geospatial data, based on two courses from Datacamp:\n  - [Visualizing Geospatial Data in Python](https://app.datacamp.com/learn/courses/visualizing-geospatial-data-in-python)\n  - [Working with Geospatial Data in Python](https://app.datacamp.com/learn/courses/working-with-geospatial-data-in-python)\n- [`satellite_image_analysis`](./satellite_image_analysis): classification, clustering and dimensionality reduction examples with satellite images; images with several bands (channels) are processed and the ML techniques are applied pixel-wise. The examples come originally from [syamkakarla98/Satellite_Imagery_Analysis](https://github.com/syamkakarla98/Satellite_Imagery_Analysis).\n\n## Requirements\n\nThe notebooks can be opened with Google Colab (if link provided in each notebook) or in a dedicated environment. A brief recipe to set one up using [conda](https://docs.conda.io/en/latest/) is the following:\n\n```bash\n# Create and activate e\nconda create --name ds pip python=3.7\nconda activate ds\n\n# Install pip dependencies\npip install -r requirements.txt\n```\n\n## Authorship\n\nMikel Sagardia, 2023.  \nNo guarantees.\n\nIf you find this repository useful and use it, please, cite the original source. This work is protected by the  GPL-3.0 license; see [`LICENSE.md`](LICENSE.md) for more information.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fspace_exploration","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmxagar%2Fspace_exploration","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmxagar%2Fspace_exploration/lists"}