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The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, the data of which are static and do not necessarily capture the dynamic nature of urbanization. This is a particularly the case with low- and middle-income nations, where, according to the United Nations, urbanization is mostly being experienced in this century. This study aims to develop a more effective approach to comprehending urbanization patterns through big data fusion, using multiple data sources that provide more reliable information on urban activities. The study uses five open data sources: national polar-orbiting partnership/visible infrared imaging radiometer suite night-time light images; point of interest data; mobile network coverage data; road network coverage data; normalized difference vegetation index data; and the Python programming language. The findings challenge the currently dominant census data and statistics-based understanding of Sri Lanka's urbanization patterns that are either underestimated or overestimated. The proposed approach offers a more reliable and accurate alternative for authorities and planners in determining urbanization patterns and urban footprints. [ABSTRACT FROM AUTHOR]