The global increase in urban population necessitates proper intervention in balancing necessities and urban infrastructure. This paves the way for intelligent tool-based planning of smart and sustainable cities. As a result, several recommendations and considerations are made in Sustainable Development Goals for better future endeavors. Advanced geospatial and remote sensing technologies are becoming the root of harnessing data that works as fuel to drive urban planning, management, and development decisions. Remotely sensed high-resolution data helps to find a detailed overview of urban landscapes, which includes accurate planning and applications of multiple initiatives ranging from solar energy projects to emergency response planning. Effective use of geospatial data calls for integrating artificial intelligence and/or deep learning approaches for enhanced data accuracy and implementation. They also reduce the time and effort required to deal with large geospatial data, so researchers and decision-makers focus more on the analysis than on data preparation. The tie between these technologies can bring more sophisticated methods to manage urban environments. This helps cities become resilient and adaptive to the challenges of rapid urban growth. The field of remote sensing taught us that gathering images in raster format is simple and easy. Still, analysis and information extraction are more significant in the case of vector images. Hence, generating vector polygons from raster images becomes a prominent process. One such approach includes the vector polygon generation of building rooftops, which serves many purposes to provide crucial urban efficiency and sustainability data. Deep-learning-based approaches have shown promising results in extracting features in vector formats from remotely sensed images. The strategic use of rooftop vector polygons exhibits how targeted data collection and implementation can contribute to improved urban sustainability and efficiency.