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.

时间序列遥感数据提供了丰富的信息来源，可用于广泛的应用，从监测土地覆盖变化到监测作物、沿海变化、洪水风险评估和城市扩张。在本文中，时间序列卫星图像用于预测城市扩张。由于时间序列卫星图像中没有地面实况，因此使用基于深度学习的无监督图像分割方法来生成用于训练和验证的地面实况。然后使用谷歌地图手动验证自动注释的图像以生成基本事实。然后手动注释剩余的数据。城市扩张的预测是通过使用 ConvLSTM 网络实现的，该网络可以在不缩小空间特征图大小的情况下学习全局时空信息。将基于 ConvLSTM 的模型应用于时间序列卫星图像，并将预测结果与 Pix2pix 和 Dual GAN 网络进行比较。在本文中，使用代表沙特阿拉伯三个最大城市（即：利雅得、吉达和达曼）的多个多日期卫星图像进行了实验结果。评估结果表明，与 Pix2pix 和 Dual GAN 相比，所提出的基于 ConvLSTM 的模型在均方误差、均方根误差、峰值信噪比、结构相似性指数和整体分类精度方面产生了更好的预测结果。此外，所提出架构的训练时间少于 Dual GAN 架构。