Idioma:

Training dataset for semantic segmentation (U-Net) of structural conservation practices

Martins, Vitor Souza

Texto completo disponível

Citações Citado por

Título:
Training dataset for semantic segmentation (U-Net) of structural conservation practices
Autor: Martins, Vitor Souza
Assuntos: BMP ; Deep Learning ; Fully convolutional network ; Mapping
Notas: RelationTypeNote: HasVersion -- 10.5281/zenodo.3762370
10.5281/zenodo.3762370
Descrição: In this research, the best management practices include vegetative/structural conservation practices (SCP) across crop fields, such as grassed waterways and terraces. This reference dataset includes 500,000 pair patches (false-color image (B1: NIR, B2: Red, B3: Green) and binary label (SCP: yes[1] or no[0]). These training samples were randomly extracted from Iowa BMP project (https://www.gis.iastate.edu/gisf/projects/conservation-practices) and present 90% of patches with SCP areas and 10% of patches non-SCP area. The patch dimension is 256 x 256 pixels at 2-m resolution. Due to the file size, the images were upload in different *.rar files (imagem_0_200k.rar, imagem_200_400k.rar, imagem_400_500k.rar), and the user should download all and merge them in the same folder. The corresponding labels are all in "class_bin.rar" file. Application: These pair images are useful for conservation practitioners interested in the classification of vegetative/structural SCPs using deep-learning semantic segmentation methods. Further information will be available in future.
Editor: Zenodo
Data de criação/publicação: 2020
Idioma: Inglês

Links

Voltar para lista de resultados