Research Output
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 predict urban expansion. As the ground truth is not available in time-series satellite images, an unsupervised image segmentation method based on deep learning is used to generate the ground truth for training and validation. The automated annotated images are then manually validated using Google Maps to generate the ground truth. The remaining data were then manually annotated. Prediction of urban expansion is achieved by using a ConvLSTM network, which can learn the global spatio-temporal information without shrinking the size of spatial feature maps. The ConvLSTM based model is applied on the time-series satellite images and the results of prediction are compared with Pix2pix and Dual GAN networks. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. The evaluation results show that the proposed ConvLSTM based model produced better prediction results in terms of Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy as compared to Pix2pix and Dual GAN. Moreover, the training time of the proposed architecture is less than the Dual GAN architecture.

  • Type:


  • Date:

    23 May 2021

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  • Publisher

    Elsevier BV

  • DOI:


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  • Funders:

    Edinburgh Napier Funded


Boulila, W., Ghandorh, H., Khan, M. A., Ahmed, F., & Ahmad, J. (2021). A novel CNN-LSTM-based approach to predict urban expansion. Ecological Informatics, 64, Article 101325.



Deep learning, Satellite image, Urban change prediction, Convolutional neural networks, Long short term memory

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