Elements: Data: U-Cube: A Cyberinfrastructure for Unified and Ubiquitous Urban Canopy Parameterization

点击翻译按钮获取中文摘要

Urban canopy parameters (UCPs) can be used in model simulations to study the health and behavior of a city, determine the ability to sustain a growing population, and study potential impacts of extreme weather events. The ability to identify and compute urban canopy parameters has been a missing element in city models; this project develops that capability for use in city design and analysis, integrating weather models and remote sensing data to infer a 3D model of cities of various sizes. The project deploys innovative science-based analysis tools within an extensible, broadly-available cyberinfrastructure portal, allowing users to ingest satellite imagery and other geographic information system (GIS) data to calculate urban canopy parameters. The cyberinfrastructure would improve urban modeling and planning, particularly for extreme weather events. The tools and high-performance computing and storage resources would be usable by other researchers through a portal. Potential beneficiaries include smaller and disadvantaged cities and countries without the resources for urban characterization and modeling necessary for such urban planning. There are also plans to transfer the results of this research to communities beyond college students -- to local teachers and secondary students and museums, and to the GIS urban planning user communities at local, state, and international levels.The project develops cyberinfrastructure which would use a novel inverse modeling approach incorporating satellite images, social science and urban zonal data, to infer a 3D model of a city from which urban canopy parameters could be derived for use in simulation models. The focus is on weather modeling, urban parameterization and a desire to better understand sustainable urbanization. The main cyberinfrastructure products will be 3D urban models and UCP values for urban locations. These UCP parameters will be used for fine-scale urban weather modeling, and evaluation of various classification techniques and simulation models in an integrated portal. The approach differs from prior work that relied on simple urban canopy models, either tuned for a large metropolis or assuming that all cities are the same. The team uses a cyberinfrastructure platform at Purdue (HubZERO) and the Geospatial Data Analysis Building Blocks (GABBs), a suite of software modules developed during a previously funded NSF Data Infrastructure project. The resulting platform can be deployed using Amazon Web Services, extending built-in geospatial data capabilities and providing a scalable CI solution. This platform can be used by researchers to test predictive models or deploy applications that have been developed. The team has cultivated relationships with the research communities and stakeholders relevant to the proposed research. Through the World Urban Database and Access Portal Tools (WUDAPT) project -- a community-based project to gather a census of cities around the world -- the team is already connected to the urban planning community globally. The project will improve urban weather modeling accuracy and increase availability of and access to the new techniques, capabilities and dedicated cyberinfrastructure. The results have the potential to support city officials and urban planners, especially in regions with the fastest rate of urbanization and/or those in developing countries, where access to computational resources is likely to be limited. This award by the NSF Office of Advanced Cyberinfrastructure will be jointly supported by the Division of Chemical, Bioengineering, Environmental, and Transport Systems, within the NSF Directorate for Engineering; and the Division of Atmospheric and Geospace Sciences and the Integrative and Collaborative Education and Research (ICER) Program, within the NSF Directorate for Geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.