Urban areas are under increasing pressure due to the economic, social, and environmental domains since the extraordinary rise in global urbanization, which is predicted to reach roughly 70% by 2050. The rapid pace of urbanization calls for intensive studies on mapping urban growth, assessing infrastructure, managing water resources, and monitoring natural land cover. The Urban environment, however, presents one of the most challenging research fields for remote sensing data analysis tasks due to the complexity of urban landscapes, the wide range of land cover materials, and the variety of land use classes. To address the challenges related metropolitan settings, recent Earth observation satellites provide a wide range of valuable spatial and quantitative information, large geographic coverage, and real-time monitoring. About one thousand Earth Observation (EO) satellites are in orbits, according to Union of Concerned Scientists (UCS), offering various multimodal remote sensing data including Multi/ Hyper-Spectral (MS/HS) images, Digital Surface Models (DSMs), and LiDAR point clouds. It is more challenging to accurately classify urban land cover and pinpoint particular features when multimodal data is interpreted using traditional methods. The integration of remote sensing techniques and Artificial Intelligence (AI) approaches provide affordable tools for gathering and analyzing large amounts of data in comparison to resource-intensive traditional scenarios like human based surveying and field observation. AI enhances data processing accuracy and efficiency, providing valuable information for urban development, infrastructure management, and environmental surveillance. AI tools interpret remote sensing data employing cutting-edge Neural Networks (NNs) to identify relevant land use and land cover characteristics and over-come human development challenges. The webinar explores the effectiveness of AI strategies especially neural networks (e.g., CNN, DNN, SNN, KAN) for remote sensing dataset analysis in urban areas and exploring their applications.