Urban expansion simulation with an explainable ensemble deep learning framework
OPEN ACCESS
Loading...
Author / Producer
Date
2024-04-15
Publication Type
Journal Article
ETH Bibliography
yes
OPEN ACCESS
Data
Rights / License
Abstract
Urban expansion simulation is of significant importance to land management and policymaking. Advances in deep learning facilitate capturing and anticipating urban land dynamics with state-of-the-art accuracy properties. In this context, a novel deep learning-based ensemble framework was proposed for urban expansion simulation at an intra-urban granular level. The ensemble framework comprises i) multiple deep learning models as encoders, using transformers for encoding multi-temporal spatial features and convolutional layers for processing single-temporal spatial features, ii) a tailored channel-wise attention module to address the challenge of limited interpretability in deep learning methods. The channel attention module enables the examination of the rationality of feature importance, thereby establishing confidence in the simulated results. The proposed method accurately anticipated urban expansion in Shenzhen, China, and it outperformed all the baseline methods in terms of both spatial accuracy and temporal consistency.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
10 (7)
Pages / Article No.
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Urban expansion simulation; Deep learning; Machine learning; Ensemble framework; Spatial modeling
Organisational unit
02608 - Institut für Umweltingenieurwiss. / Institute of Environmental Engineering