Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data

Abstract

Reliable and accurate land-use/land cover maps are vital for monitoring and mitigating urbanization impacts. This necessitates evaluating machine learning simulations and incorporating valuable insights. We used four primary models, logistic regression (LR), support vector machine, random decision forests, and artificial neural network (ANN), to simulate land cover maps for Tsukuba City, Japan. We incorporated an auxiliary input that used multinomial logistic regression to enhance the ANN and obtained a fifth model (ANN was run twice, with and without the new input). Additionally, we developed a sixth simulation by integrating the predictions of ANN and LR using a fuzzy overlay, wherein ANN had an additional new input alongside driving forces. This study employed six models, using classified maps with three different resolutions: the first involved 15 m (ASTER) covering a study area of 114.8 km2, for the second and third, 5 and 0.5 m (derived from WorldView-2 and GeoEye-1) covering a study area of 14.8 km2, and the models were then evaluated. Due to a synergistic effect, the sixth simulation demonstrated the highest kappa in all data, 86.39%, 72.65%, and 70.65%, respectively. The results indicate that stand-alone machine learning-based simulations achieved satisfactory accuracy, and minimalistic approaches can be employed to improve their performance.

Keywords

• remote sensing
• machine learning simulations
• urban land growth modeling
• neural network
• logistic regression
• fuzzy overlay