Rule based End-to-End Learning Framework for Urban Growth Prediction

Due to the rapid growth of urban areas in the past decades, it has become increasingly important to model and monitor urban growth in mega cities. Although several researchers have proposed models for simulating urban growth, they have been primarily dependent on various manually selected spatial and nonspatial explanatory features for building models. A practical difficulty with this approach is manual selection procedure, which tends to make model design process laborious and non-generic. Despite the fact that explanatory features provide us with the understanding of a complex process, there has been no standard set of features in urban growth prediction over which scholars have consensus. Hence, design and deploying of systems for urban growth prediction have remained challenging tasks. In order to reduce the dependency on human devised features, we have proposed a novel End-to-End prediction framework to represent remotely sensed satellite data in terms of rules of a cellular automata model in order to improve the performance of urban growth prediction. Using our Endto-End framework, we have achieved superior performance in Figure of Merit, Producer’s accuracy, User’s accuracy, and Overall accuracy metrics respectively over existing learning based methods. Keywords— End-to-End learning, Ensemble methods, Decision trees, Urban growth prediction, Landsat.