cloud-processing, GEE, building height
Prakhar Misra, Project Assistant Professor, Institute of Industrial Science, University of Tokyo, Japan
Ram Avtar, Assistant Professor, Faculty of Environmental Earth Science, Hokkaido University, Japan
With rapid and unplanned urban growth in Asian region has adverse environmental and social consequences. Most of previous studies on urbanization has emphasized on importance of building types such as residential, industrial and commercial and their role in planning, management, and development of cities. However, the extraction of this valuable information is challenging in the traditional survey method. Only few studies related to extraction of building attributes using remote sensing data has been done. Most of previous studies covered urbanization and land use/land cover change extensively using remote sensing data. Extraction of building types still a challenging issue. Therefore, this hands-on-training programme is designed to extract various building types such as residential, industrial and commercial using cloud-based processing. This training is based on processing of multi-sensor satellite data such as Sentinel 2, Nighttime Light (NTL) data, and digital elevation data using google earth engine. The proposed training will support to implement the United Nations Sustainable Development Goals (SDGs) 11 sustainable cities and communities. It will aim to strengthen the capacity and knowledge of various stakeholders to monitor urban changes and support the urban planner in the decision-making process.