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Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation

Data pre-processing for developing a generalised land use and land cover (LULC) deep learning model using earth observation data is important for the classification of a different date and/or sensor. However, it is unclear how to approach deep learning segmentation problems in earth observation data...

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Principais autores: Andrew Clark, Stuart Phinn, Peter Scarth
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2023-06-01
Serier:Land
Fag:
convolutional neural network
deep learning
semantic segmentation
land use
land cover
aerial imagery
Online adgang:https://www.mdpi.com/2073-445X/12/7/1268
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