GateSTNet: a novel model for mapping spatiotemporal forest cover

Bao Liu; Siqi Chen; Lei Gao

doi:10.1117/12.3048637

27 November 2024 GateSTNet: a novel model for mapping spatiotemporal forest cover

Bao Liu, Siqi Chen, Lei Gao

Author Affiliations +

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134021N (2024) https://doi.org/10.1117/12.3048637
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Forest resources have important environmental and economic values. Although many models have been developed to analyze the dynamic changes of forest cover, significant challenges remain in explaining the relationships between geographically non-contiguous regions with similar forest cover changes. To address this issue, we propose Gate Spatiotemporal Network (GateSTNet), a new model for predicting multi-region, multi-feature, and multi-network datasets. The GateSTNet comprises the gate GCAN (Graph Convolutional Attention Network) information focus module and the ConvOut spatiotemporal predict module. The network effectively combines the capabilities of graph convolutional networks (GCNs) for processing non-Euclidean spatial data and convolutional LSTM (ConvLSTM) networks for spatiotemporal data prediction. Experimental results show that GateSTNet performs well in both prediction accuracy and efficiency. Compared with existing models, including LSTM, CNN-LSTM, and ConvLSTM, GateSTNet exhibits excellent prediction accuracy and stability. This enhanced performance is attributed to the network's ability to effectively integrate spatial and temporal information, making it a powerful tool for predicting forest cover dynamics and informing environmental management and policy decisions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bao Liu, Siqi Chen, and Lei Gao "GateSTNet: a novel model for mapping spatiotemporal forest cover", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134021N (27 November 2024); https://doi.org/10.1117/12.3048637

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE