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Alysha van Duynhoven and Suzana Dragicevic
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to m...
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Joao Fonseca, Georgios Douzas and Fernando Bacao
Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing co...
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Fei Sun, Run Wang, Bo Wan, Yanjun Su, Qinghua Guo, Youxin Huang and Xincai Wu
Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly ...
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Alan K. Betts and Raymond L. Desjardins
Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction t...
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Petros Chatzimpiros, Natalia Roumelioti, Anna Zamba, Kimon Hadjibiros
One important component of the urban contribution to carbon dioxide atmospheric emissions is road transport. Carbon dioxide (CO2) emissions from urban road transport in the centre of Athens recorded over a period of five years (2000?2005) are compared wi...
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