Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images

This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighborhood development level, which is taken by cellular automata as initial approximation in its transition rules. Such approximations are updated through spatial calibration on a township gird and temporal calibration with multi temporal satellite images. Assessment of the modeling results is based on three evaluation measures: Fitness, Type I and Type II errors. The approach is applied to model the growth of city Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100% 20% with 30% average errors can be achieved for 80% of the townships in urban growth prediction. ±