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ANNUAL PAST-PRESENT LAND COVER CLASSIFICATION FROM LANDSAT USING DEEP LEARNING FOR URBAN AGGLOMERATIONS

CHINCHUTHAKUN Varquez Alvin Christopher Galang 山下 幸彦 神田 学 Worameth CHINCHUTHAKUN Alvin C.G. VARQUEZ Yukihiko YAMASHITA Manabu KANDA 東京工業大学 DOI:https://doi.org/10.2208/journalofjsce.23-16151

2023.09.12

概要

for long-term analysis of urban land cover changes due
to their limited temporal coverage. Hence, previous
studies address this by training classifiers specifically
for their study areas using these datasets instead.
Prior works generally employ traditional machine
learning methods such as support vector machine
(SVM)5) , random forest6) , and maximum likelihood estimation (MLE)7) due to their good trade-off between
accuracy and computational costs. While these methods achieve high performance on a regional scale, they
suffer from limited generalization capability when processing data from multiple urban agglomerations due
to their pixel-level operation. ...

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en/dataset/lulc e.htm

(Received May 31, 2023)

(Accepted September 12, 2023)

...

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