Predicting road quality using high resolution satellite imagery: A transfer learning approach.
Recognizing the importance of road infrastructure to promote human health and economic development, actors around the globe are regularly investing in both new roads and road improvements. However, in many contexts there is a sparsity-or complete lack-of accurate information regarding existing road...
Guardado en:
Autores principales: | Ethan Brewer, Jason Lin, Peter Kemper, John Hennin, Dan Runfola |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/3049a5bdc60d4bcda31227041cbc08dd |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
A generalizable and accessible approach to machine learning with global satellite imagery
por: Esther Rolf, et al.
Publicado: (2021) -
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
por: H. Jiang, et al.
Publicado: (2021) -
Tree counting with high spatial-resolution satellite imagery based on deep neural networks
por: Ling Yao, et al.
Publicado: (2021) -
Using sentinel-2 satellite imagery to develop microphytobenthos-based water quality indices in estuaries
por: Simon Oiry, et al.
Publicado: (2021) -
ATMOSPHERIC CORRECTION OF THE LANDSAT SATELLITE IMAGERY FOR TURBID WATERS
por: Hwan Ahn,Yu, et al.
Publicado: (2004)