The scope of urbanization variation due to landscape alterations, profoundly affect the ecological features and decision-making process for built-up standards. In various parts of the globe, urbanization studies have identified a significant correlation between substantial human benefits on quality-of life, quantifiable resource utilization and local dimatic parameters. Remote sensing environment has developed techniques to estimate change in spatio-temporal attributes in a georeferenced imagery. The Intricate multi-signature classes and massive data interpretation for minute urban change detection for Kancheepuram, Tamilnadu. Supervised Land-use-and-Land-Cover (LULC) classification using Neural Network(NN), Minimum Distance, Support Vector Machine(SVM) & Maximum Likelihood technique to estimate change In urban class is performed. Unsupervised classification, Inbuilt programmed distance learning algorithm, with ISO and K-means, is performed on preprocessed and enhanced PCA image. Support vector machine and ISO classification techniques with enhanced imagery, on the prior basis shows more accuracy (80-85%) amongst mentioned techniques. Thus, following SVM to categorize attributes to classes and performing urban change detection. The discrepancy between new land and barren land via SVM practice remain uncertain. © 2018 Ecological Society of India. All Rights Reserved.