{"id":24782142,"url":"https://github.com/sowmyasree19/deforestation-project","last_synced_at":"2026-05-01T16:31:15.175Z","repository":{"id":274632717,"uuid":"923550325","full_name":"sowmyasree19/Deforestation-project","owner":"sowmyasree19","description":"This project is to find the deforested area of the given input aerial image. ","archived":false,"fork":false,"pushed_at":"2025-01-28T13:08:01.000Z","size":22001,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-24T05:31:45.034Z","etag":null,"topics":["deep-learning","digital-image-processing","machine-learning","python","streamlit"],"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/sowmyasree19.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":"2025-01-28T13:01:43.000Z","updated_at":"2025-01-28T13:08:04.000Z","dependencies_parsed_at":null,"dependency_job_id":"73ae1637-73eb-4744-bd5c-b533118850e4","html_url":"https://github.com/sowmyasree19/Deforestation-project","commit_stats":null,"previous_names":["sowmyasree19/deforestation-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sowmyasree19/Deforestation-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sowmyasree19%2FDeforestation-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sowmyasree19%2FDeforestation-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sowmyasree19%2FDeforestation-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sowmyasree19%2FDeforestation-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sowmyasree19","download_url":"https://codeload.github.com/sowmyasree19/Deforestation-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sowmyasree19%2FDeforestation-project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259293098,"owners_count":22835539,"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","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","digital-image-processing","machine-learning","python","streamlit"],"created_at":"2025-01-29T11:16:13.972Z","updated_at":"2026-05-01T16:31:14.849Z","avatar_url":"https://github.com/sowmyasree19.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Identifying-Deforestation-project\n\nI have implemented U-Net and sequential model for segmentation and classification of the satellite images. I have trained the model's weights and saved the with the names \"forest_segmentation_model.h5\" and \"segmentation.h5\". And to represent the results of my project with the help of streamlit app \"app2.py\" by recreating the models architecture and model weights that have been saved.\n\nDataset contains : The dataset is consisting of 5108 satellite images and their respective binary mask images. And also a meta_data.csv file containing names of the images and their masks names with the column names \"images\" and \"masks\".\n\nAcknowledgment : The dataset is obtained from the \"DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images\". I. Demir et al., \"DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images,\" 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 2018, pp. 172-17209, doi: 10.1109/CVPRW.2018.00031. keywords: {Satellites;Roads;Building management systems;Climate change;Urban areas;Computer vision;Image segmentation;Smart cities},\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsowmyasree19%2Fdeforestation-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsowmyasree19%2Fdeforestation-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsowmyasree19%2Fdeforestation-project/lists"}