AI-BASED URBAN SPRAWL ANALYSIS AND PREDICTION MODELING
Globally, 55% of the world’s population lives in urban areas. The urban population is
expected to be 6 billion, an increase of 1.5 times, by 2050. As the urban landscape grows,
the increasing concentration of population in fewer settlements demands for more resources
and land. Understanding the urban-growth process in the past, present and preparing for
the future are fundamentals to the land management. It can be measured through urban-
sprawl - the volume, the direction and the extent of the built-up. Satellite remote-sensing
provides temporal data to map the changes and assess the spatial growth of cities.
With the advancement in the geospatial technology, spatial decision making by computing
has become quicker, easier and accurate using machine-learning techniques. Spatial and
temporal predictions at higher spatial resolution play important role in understanding the
growth dynamics.
