An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle

An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA...

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Bibliographic Details
Main Authors: Zhi Zhou, Xueling Wu, Bo Peng
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Remote Sensing
Subjects:
carbon stock
InVEST model
LULC spatial–temporal prediction
spatial lag and error model
SA-ConvLSTM model
Online Access:https://www.mdpi.com/2072-4292/16/23/4372
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