A novel CNN-LSTM-based approach to predict urban expansion

A novel CNN-LSTM-based approach to predict urban expansion

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveillance of crops, coastal changes, flood risk assessment, and urban sprawl. In this paper, time-series satellite images are used to predi...

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Bibliographic Details
Main Authors: Ahmad, J. (Author), Ahmed, F. (Author), Boulila, W. (Author), Ghandorh, H. (Author), Khan, M.A (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
accuracy assessment
Convolutional neural networks
Dammam
Deep learning
detection method
Eastern Province [Saudi Arabia]
Jeddah
Long short term memory
Makkah [Saudi Arabia]
remote sensing
Riyadh [Riyadh (PRV)]
satellite data
Satellite image
satellite imagery
Saudi Arabia
segmentation
spatiotemporal analysis
Urban change prediction
urbanization
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Internet

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