Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

From 1990 to 2018, built-up areas in Tallinn, Estonia’s capital city, increased by 25.03%, while its population decreased by −10.19%. Investigating the factors affecting urban expansion and modeling it are critical steps to detect future expansion trends and plan for a more sustainable environment. Different models have been used to investigate, predict, and simulate urban expansion in recent years. In this paper, we coupled the cellular automata, agent-based, and Markov models (CA–Agent model) in a novel manner to address the complexity of the dynamic simulation, generate heterogeneity in space, define more complicated rules, and employ the suitability analysis. In the CA–Agent model, cells are dynamic agents, and the model’s outcome emerges from cellular agents’ interactions over time using the rules of behavior and their decisions concerning the adjacent neighboring cells and probabilities of spatial changes. We performed the CA–Agent model run two times for 2018 and 2030. The first simulated results were used to validate the performance of the model. Kappa showed 0.86, indicating a relatively high model fit, so we conducted the second 12-year run up to the year 2030. The results illustrated that using these model parameters, the overall built-up areas will reach 175.24 sq. km with an increase of 30.25% in total from 1990 to 2030. Thus, implementing the CA–Agent model in the study area illustrated the temporal changes of land conversion and represented the present spatial planning results requiring regulation of urban expansion encroachment on agricultural and forest lands.

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