{"id":20763140,"url":"https://github.com/dymaxionlabs/basurales","last_synced_at":"2025-04-30T07:49:23.919Z","repository":{"id":80673212,"uuid":"344142583","full_name":"dymaxionlabs/basurales","owner":"dymaxionlabs","description":"Detection of Open Landfills - Basurales a Cielo Abierto","archived":false,"fork":false,"pushed_at":"2022-03-15T13:56:27.000Z","size":18881,"stargazers_count":12,"open_issues_count":0,"forks_count":8,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-30T07:49:21.255Z","etag":null,"topics":["landfill","machine-learning","satellite-imagery","segmentation","unet"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dymaxionlabs.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-03-03T13:50:24.000Z","updated_at":"2025-03-30T09:46:21.000Z","dependencies_parsed_at":"2023-09-22T07:05:51.372Z","dependency_job_id":"94367c60-9c36-4ce6-9093-8d3b12806f75","html_url":"https://github.com/dymaxionlabs/basurales","commit_stats":{"total_commits":25,"total_committers":6,"mean_commits":4.166666666666667,"dds":0.6799999999999999,"last_synced_commit":"7497b93fee768f7611fc53eae7cfdee3b332ab27"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dymaxionlabs%2Fbasurales","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dymaxionlabs%2Fbasurales/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dymaxionlabs%2Fbasurales/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dymaxionlabs%2Fbasurales/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dymaxionlabs","download_url":"https://codeload.github.com/dymaxionlabs/basurales/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251666226,"owners_count":21624290,"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":["landfill","machine-learning","satellite-imagery","segmentation","unet"],"created_at":"2024-11-17T10:42:56.724Z","updated_at":"2025-04-30T07:49:23.883Z","avatar_url":"https://github.com/dymaxionlabs.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deteccion de basurales a cielo abierto\n\n## Descripción\n\nLos basurales a cielo abierto son aquellos donde los residuos se disponen de forma indiscriminada y con escasas medidas de protección ambiental. \n\nEste proyecto, desarrollado en conjunto entre Dymaxion Labs y la [Fundación Bunge \u0026 Born](https://www.fundacionbyb.org/), tiene por objetivo la detección y seguimiento de los basurales a cielo abierto, aplicando técnicas basadas en machine learning (ML) para el procesamiento de imágenes satelitales. Esto permite mapear grandes áreas de manera rápida y con bajos recursos.\n\n![](img_readme/A.png)![](img_readme/B.png)\n\nEl modelo está optimizado para las imágenes multiespectrales del satélite Sentinel-2. \n\n## Requerimientos\n\nSe utilizan las herramientas **GDAL** y [Orfeo Toolbox](https://www.orfeo-toolbox.org/) en la primera etapa del pre-procesamiento de los datos. Luego, se emplean nuestros paquetes [satproc](https://github.com/dymaxionlabs/satproc) y [unetseg](https://github.com/dymaxionlabs/satproc) para la generación del dataset y modelo de ML respectivamente.\n\n## Notebooks\n\nEste repositorio contiene un conjunto de notebooks de Jupyter, que describen los pasos necesarios:\n\n1. [Pre-procesamiento](notebooks/1_Preprocesamiento.ipynb): Se procesan las imágenes satelitales y la verdad de campo para generar el dataset de entrenamiento y de predicción del modelo.\n2. [Entrenamiento](notebooks/2_Entrenamiento.ipynb): Entrenamiento y evaluación del modelo.\n3. [Predicción](notebooks/3_Prediccion.ipynb): Predicción sobre la región de interés.\n4. [Post Procesamiento](notebooks/4_Post-procesamiento.ipynb): Procesamiento de los resultados de la predicción.\n\n## :handshake: Contribuciones\n\nReportes de bugs y *pull requests* pueden ser reportados en la [página de issues](https://github.com/dymaxionlabs/basurales) de este repositorio. Este proyecto está destinado a ser un espacio seguro y acogedor para la colaboración, y se espera que los contribuyentes se adhieran al código de conducta [Contributor\nCovenant](http://contributor-covenant.org).\n\n## :page_facing_up: Licencia\n\nEl código está licenciado bajo Apache 2.0. Refiérase a [LICENSE.txt](LICENSE.txt).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdymaxionlabs%2Fbasurales","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdymaxionlabs%2Fbasurales","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdymaxionlabs%2Fbasurales/lists"}