Deep learning based local climate zone classification using multispectral and sentinel 1 images

The way urban climates are classified affects both sustainable urban development and environmental planning. Local Climate Zone (LCZ) classification offers a comprehensive framework to classify different urban areas based on their climate-related characteristics. This paper investigates the application of deep learning techniques for LCZ categorization using multispectral Sentinel-2 satellite images. Sentinel-2's capacity to record optical data over a wide range of spectrum bands makes it an invaluable tool for understanding variations in urban climate. This study uses a deep learning model called convolutional neural networks (CNNs) to effectively extract and learn spatial attributes from the multispectral Sentinel images. The work uses a labeled dataset with Sentinel images for training the model and classifications of LCZ. During the training phase, the model parameters are tuned to enhance the interpretability of climate-related patterns in urban environments. Using a validation dataset, classification metrics such as accuracy, precision, recall, and F1 score are used to evaluate the model performance. These conclusions offer useful information to environmental scientists, urban planners, legislators, and those involved in climate-resilient urban design. This demonstrates the efficacy of using multispectral and SAR images for precise LCZ categorization, advancing our understanding of the variability of urban climate and assisting planners in making well-informed decisions regarding urban development strategies.