Análisis de imágenes satelitales de observación de la tierra y datos geoespaciales a través de machine learning
Today, there is a need for the rise of green, healthy and sustainable cities in the world, especially in less developed countries. Then, managing to detect the vegetation and green areas in the cities, can help determine the shortcomings that it has, in the field of sustainability. In addition, gree...
- Autores:
-
Posada Valcárcel, Stiven Fernando
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2020
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/51549
- Acceso en línea:
- http://hdl.handle.net/1992/51549
- Palabra clave:
- Imágenes de detección a distancia
Imágenes digitales
Sistemas de Información Geográfica
Datos geoespaciales
Computación en la nube
Descarga de datos
Desarrollo de software
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | Today, there is a need for the rise of green, healthy and sustainable cities in the world, especially in less developed countries. Then, managing to detect the vegetation and green areas in the cities, can help determine the shortcomings that it has, in the field of sustainability. In addition, green areas, apart from being the lungs of the city, also help to regulate the temperature and humidity of the environment. The objective of this project is to obtain a model that allows to classify the vegetation of any city in the world, as long as the Copernicus images available present low cloud cover where possible less than 20%. The classification is with 1 for vegetation and 0 for non-vegetation, then calculate the percentage of vegetation and the amount of green areas according to the established patterns |
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