Long Short-Term Memory and Attention Models for Simulating Urban Densification.

This paper introduces a novel cellular automata model that combines Long Short-Term Memory, Attention, and Neural Network models to capture spatio-temporal Land Use Change (LUC) behaviors while addressing the challenge of imbalanced datasets. The proposed method is developed and validated using data from Belgium, defined as three (100x100) m raster-based built-up maps for 2000, 2010, and 2020. The model is trained and validated using data from 2000 to 2010, and its effectiveness is tested using data from 2010 to 2020. The key contribution of our approach lies in its ability to tackle long-term temporal dependency and class imbalance problems in LUC science. Our proposed method significantly enhances the performance of spatio-temporal LUC simulation. Additionally, we adopt a data splitting strategy that takes into account the different transitions between classes, improving the accuracy of the model predictions of minority class. The obtained results demonstrate the efficiency of the proposed model in capturing complex spatio-temporal dynamics and reducing the impact of imbalanced datasets surpassing existing methods. The implications of our study extend beyond LUC modeling, as the proposed approach can be applied to a wide range of applications where machine learning is used to model complex environmental and geographical phenomena. • This paper presents a novel cellular automata model to simulate urban densification. • It combines the Long short term memory, the Attention, and the neural network models. • ALSTM-CA captures the temporal dependency and addresses the minority classes in LUC. • ALSTM-CA is developed, calibrated, and verified using data from Belgium. • It achieved promising results based on F1-score, accuracy, and precision metrics. [ABSTRACT FROM AUTHOR]