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Many regional environmental problems are the consequence of anthropogenic activities involving land cover changes. Temporal land cover data with social aspects are critical in tracing relationships of cause and effect on variables of interest with the effects of context on behaviour, or with the process of human environmental interaction and are also useful for the governance of urbanising cities. Many cities are now rapidly becoming urbanised and undergoing redevelopment for economic purposes with new roads, infrastructure improvements, etc. raising the necessity to understand the dynamics of the urban growth process for the planning of natural resources. Cellular automata (CA), an artificial intelligence technique based on pixels, states, neighbourhoods and transition rules is useful in modelling the urban growth process due to its ability to fit such complex spatial nature, using simple and effective rules. This study develops the calibration of a CA model by taking into account spatial and temporal dynamics of urban growth. The effectiveness of this technique is demonstrated by capturing the growth pattern of Bangalore, a city in India, with historical remote sensing and population data.