LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: Evidence from Mumbai

The satellite imagery classification task is fundamental to spatial knowledge discovery. Several image classification methods are used to create standardized Land use and Land cover (LULC) maps, which facilitate research on spatial and ecological processes and human activities. Local Climate Zones (LCZ) classification maps are an example of standardized maps which have been widely used to demarcate the homogeneity in built and natural character in the cities. The LCZ classification scheme is primarily focused on urban climate-related research, in which 17 climate zones are mapped in a city area with the 100-150m spatial resolution. Each zone exhibits physical properties related to urban form and functions essential for thermal behavior studies. Extending this widely adopted approach to create LULC maps at finer resolution using the LCZ mapping scheme would benefit the allied domains of urban planning, transportation, and water resources management. This study proposes a novel solution to generate classification maps with a 10-band Sentinel-2B dataset and Convolutional Neural Networks (CNN) at the 10m spatial resolution. The classification benefits from CNNs property to preserve local structures in the image datasets. The proposed CNN model outperforms traditional machine learning models such as Artificial Neural Network, Random Forests, and Support Vector Machines. The overall accuracy and kappa statistic of the CNN model trained on 14 urban and natural classes are 82 percent and 0.81, respectively. The study also discusses the utility of the model for specialized remote sensing tasks such as change detection, identification of slum settlements, and mapping pervious/impervious layers in urban settlements with higher accuracy.