NSF Convergence Accelerator Track D: Rapid Development of Intelligent, Built Environment Geo-Databases Using AI and Data-Driven Models

点击翻译按钮获取中文摘要

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to achieve what is seen as the Holy Grail by the Built Environment research community and industry. Infrastructure designers and planners increasingly require detailed three dimensional (3D) geospatial databases of the Built Environment, in order to consider human scale perceptions, which today are often overlooked but critical to improving cities in order to better meet the needs of its citizens and other emerging technologies, such as autonomous vehicles. The impact on society of applying Artificial Intelligence (AI) to rapidly create secure and intelligent 3D models of the Built Environment, including horizontal (e.g., highway, bridges) and vertical (e.g., buildings, plants) facilities, by turning raw data into actionable information cannot be overstated. However, to-date, assembling and maintaining those databases has been too expensive for even the most progressive cities due in large part to the cost of manual feature extraction. Built Environment data that is accurate and reliable is critical for the well-being of communities as such data is used for a variety of purposes including emergency preparedness, asset operations, maintenance, public safety, and more. A convergent, innovative team will be formed with industry partners to ensure that the knowledge developed through this research effectively transitions into many aspects of practice. From the outset, and through both phases of the project, the team is taking steps through team building and intentional engagement with a variety of stakeholders to broaden the scope of potential impacts of the proposed innovation. Specifically, the research and implementation activities will be designed to consistently include dialogue and invite input from a broad range of interests, intentionally seeking involvement with segments of the public that are traditionally left out or neglected in technology implementation endeavors. This research also implements activities for workforce development in computer vision and geomatics, which currently has a large gap between employment needs and a workforce of appropriately skilled personnel, particularly from underrepresented backgrounds. Current workflows and procedures to develop 3D models of the Built Environment require substantial manual effort. Those processes that are automated are limited to small datasets that are not representative of the current point clouds and other data being acquired or needed for Building Information Modeling (BIM). They are also limited in the types of objects that can be modeled. To this end, the interdisciplinary research team will work with stakeholders to develop a more holistic Scan-to-BIM process. Phase I of this project has two primary goals: (1) provide a scan-to-BIM validation tool by compiling a sizable collection of benchmark datasets with annotated point cloud scans and corresponding BIM models, creating a prototype validation server with metrics related to parameters of interest to stakeholders (e.g., evaluate the accuracy of modeled door widths, which are important to ADA compliance assessment, or evaluate these models for urban renewal, redevelopment projects), and create and host a Scan-to-BIM challenge for researchers all over the world to participate, and (2) develop a prototype tool to implement a holistic Scan-to-BIM framework to rapidly and reliably generate BIM models from scan data that can be used not only to facilitate the development of the benchmark datasets but also used by stakeholders. Based on the research resulting from these challenges, the project team plans to build a comprehensive cloud-based service for Scan-to-BIM, which will be deployed to serve the Architectural/Engineering/Construction (AEC) community, and ultimately the public in general as these models can decrease construction or renovation project costs of public infrastructure funded with taxpayer’s money. Users would be able to select desired algorithms for key stages of the Scan-to-BIM framework based on their performance for specific applications and use cases.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.