{"id":20865567,"url":"https://github.com/sumanth44a/satellite_image_segmentation_using_deep_learning_model","last_synced_at":"2026-05-09T05:33:36.402Z","repository":{"id":246516940,"uuid":"821360471","full_name":"sumanth44a/Satellite_Image_segmentation_Using_Deep_Learning_Model","owner":"sumanth44a","description":"Satellite Image segmentation using Unet","archived":false,"fork":false,"pushed_at":"2024-06-28T11:31:47.000Z","size":3899,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-03T01:45:41.232Z","etag":null,"topics":["deep-learning","image-processing","python","u-net"],"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/sumanth44a.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-28T11:15:08.000Z","updated_at":"2024-08-20T13:23:37.000Z","dependencies_parsed_at":"2024-06-28T12:55:32.958Z","dependency_job_id":null,"html_url":"https://github.com/sumanth44a/Satellite_Image_segmentation_Using_Deep_Learning_Model","commit_stats":null,"previous_names":["sumanth44a/satellite_image_segmentation_using_unet"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sumanth44a/Satellite_Image_segmentation_Using_Deep_Learning_Model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sumanth44a%2FSatellite_Image_segmentation_Using_Deep_Learning_Model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sumanth44a%2FSatellite_Image_segmentation_Using_Deep_Learning_Model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sumanth44a%2FSatellite_Image_segmentation_Using_Deep_Learning_Model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sumanth44a%2FSatellite_Image_segmentation_Using_Deep_Learning_Model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sumanth44a","download_url":"https://codeload.github.com/sumanth44a/Satellite_Image_segmentation_Using_Deep_Learning_Model/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sumanth44a%2FSatellite_Image_segmentation_Using_Deep_Learning_Model/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276896549,"owners_count":25724047,"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":"2025-09-25T02:00:09.612Z","response_time":80,"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":["deep-learning","image-processing","python","u-net"],"created_at":"2024-11-18T05:49:35.611Z","updated_at":"2025-09-25T09:37:22.726Z","avatar_url":"https://github.com/sumanth44a.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Satellite image segmentation using Deep Learning Model\n## Introduction:\n  Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing using the U-Net model.\n\n## Model:\n![image](https://github.com/sumanth44a/Satellite_Image_segmentation_Using_Unet/assets/114097800/8a830e13-20d3-4398-8d59-f27c4bde9a22)\n\nThe U-Net model is employed in this project for semantic segmentation of satellite images. U-Net is known for its efficiency and accuracy in handling image segmentation tasks, making it a suitable choice for this application.\n\n## Dataset\n  - DatasetLink: https://www.kaggle.com/datasets/humansintheloop/semantic-segmentation-of-aerial-imagery?resource=download\n### Context\nHumans in the Loop is publishing an open-access dataset annotated for a joint project with the Mohammed Bin Rashid Space Center in Dubai, UAE.\n\n### Content\nThe dataset consists of aerial imagery of Dubai obtained by MBRSC satellites and annotated with pixel-wise semantic segmentation in 6 classes. The total volume of the dataset is 72 images grouped into 6 larger tiles. The classes are:\n  - Building: #3C1098\n  - Land (unpaved area): #8429F6\n  - Road: #6EC1E4\n  - Vegetation: #FEDD3A\n  - Water: #E2A929\n  - Unlabeled: #9B9B9B\n\n## Results:\n![Output](https://github.com/sumanth44a/Satellite_Image_segmentation_Using_Unet/assets/114097800/80d3ce0a-9342-4ba1-8359-478beb002db2)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumanth44a%2Fsatellite_image_segmentation_using_deep_learning_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsumanth44a%2Fsatellite_image_segmentation_using_deep_learning_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsumanth44a%2Fsatellite_image_segmentation_using_deep_learning_model/lists"}