Optimized autoencoder deep learning for carbon stock estimation using multi source remote sensing data in tropical rainforests

Abstract Accurate estimation of aboveground carbon stocks in tropical rainforests is fundamental for global climate change mitigation, sustainable forest management, and carbon accounting. Traditional field-based carbon assessment methods are often time-consuming, labour-intensive, and spatially lim...

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Principais autores:	John Khoo, Rayner Alfred, Kim On Chin, Ervin Gubin Moung, Oliver Valentine Eboy, Normah Awang Besar Raffie, Luaran Nosius
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Springer 2026-02-01
סדרה:	Discover Artificial Intelligence
נושאים:	Carbon stock estimation Auto-encoders Deep learning Remote sensing Hyper-parameter optimization Environmental monitoring
גישה מקוונת:	https://doi.org/10.1007/s44163-026-01010-y
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