IUCRC Planning Grant, Purdue University: Center for Accurate Georeferencing of the Environment (CAGE)
IUCRC 规划拨款,普渡大学:环境精确地理配准中心 (CAGE)
基本信息
- 批准号:2113881
- 负责人:
- 金额:$ 2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-06-01 至 2022-11-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The global economic impact of Global Positioning System (GPS) use is estimated at ~$1B/day. GPS and resulting geospatial location information (i.e., georeferencing) serves a diverse ecosystem of companies across the spectrum of users including manufacturers, system integrators, service providers, and end users. It is a technology that has exploded in recent decades with the private sector superseding the historic dominance of U.S. federal agencies in geospatial technology development and deployment. However, this proliferation has revealed vulnerabilities due rapidly advancing technology and data analytics for both commercial and government entities. The resulting evolution and fragmentation of geospatial technologies has precipitated a need for strategic investment in foundational elements related to GPS and georeferencing. To address this issue, funding is being provided for The Ohio State University, Purdue University, and Saint Louis University to explore the formation of a three-Site industry-university cooperative research center: The Center for Accurate Georeferencing of the Environment (CAGE). The Center will generate and disseminate new foundational knowledge in three interrelated areas of potential strong interest to prospective Members. These include foundational research on GPS-based and alternative AI-based navigation and mapping technologies to advance geospatial data referencing platforms upon which commercial innovation depends; refine and work with industry to codify and adopt geospatial data standards and best practices for functions such as acquisition of multi-sensor/multi-platform/multi-temporal geospatial data such that diverse members will be able to build interoperable systems; and develop new AI-based methods for the reconstruction and labelling of physical locations/object space from diverse geospatial data will unlock significant potential for geospatial data analytics. Through a Center planning meeting, The Ohio State University, Purdue University, and Saint Louis University faculty and administrators will engage a broad spectrum of public and private entities to identify and discuss collective needs with the goal of creating a viable research roadmap and associated research thrusts in which partners are willing to participate and invest. Topics and potential Center research thrusts will be discussed at the meeting, and those of highest importance to industry will be identified upon which a viable Center can be created. Broader impacts of the work include increased economic productivity and integration of the economic sectors that use or wish to use georeferencing; workforce training where students participate in industry-relevant, basic, pre-competitive research projects proposed by university faculty; the engagement of underrepresented groups via various partnerships with institutional entities focused in recruiting and retraining students from underserved populations; and outreach to the public and K-12. Industry-university cooperative research centers, like the three-Site Center jointly proposed to be focused on georeferencing in the environment and geospatial techniques and applications by The Ohio State University, Purdue University, and Saint Louis University are powerful partnerships between universities; the private sector; government; non-profits; and local communities. These Centers operate as consortia where university faculty and students perform fundamental research that addresses collective industry and community needs and where projects are funded by membership fees and other financial contributions from Center members. In this construct, the intellectual power; infrastructure; and student talent of universities is focused on real world problems of critical interest of a segment of the economy in need of innovation to overcome conceptual and technological hurdles that individual entities are unable to overcome themselves. The planning meeting proposed will bring together interested parties to hear proto-Center leadership describe the Center’s value proposition and potential return-on-investment of those entities wishing to become members of the Center. Those at the planning meeting will also help the Center create a research road map and research thrusts that best address the most pressing needs of the collective membership in the Center.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.
全球定位系统(GPS)的使用对全球经济的影响估计约为10亿美元/天。GPS和由此产生的地理空间位置信息(即,地理参考)服务于包括制造商、系统集成商、服务提供商和最终用户在内的用户范围内的公司的多样化生态系统。近几十年来,随着私营部门取代美国联邦机构在地理空间技术开发和部署方面的历史主导地位,这一技术出现了爆炸式增长。然而,由于商业和政府实体快速发展的技术和数据分析,这种扩散暴露了漏洞。由此产生的地理空间技术的演变和分散,使得有必要对与全球定位系统和地理参照有关的基本要素进行战略投资。为了解决这一问题,正在为俄亥俄州州立大学、普渡大学和圣刘易斯大学提供资金,以探索建立一个三个地点的产学合作研究中心:环境精确地理参考中心(CAGE)。该中心将产生和传播新的基础知识,在三个相互关联的领域的潜在的强烈兴趣,以未来的成员。其中包括对基于全球定位系统和替代人工智能的导航和绘图技术进行基础研究,以推进商业创新所依赖的地理空间数据参考平台;完善并与业界合作,编纂和采用地理空间数据标准和最佳做法,以实现获取多传感器/多平台/多时相地理空间数据等功能,使不同成员能够建立可互操作的系统;开发新的基于人工智能的方法,从不同的地理空间数据中重建和标记物理位置/对象空间,将释放地理空间数据分析的巨大潜力。通过中心规划会议,该俄亥俄州州立大学,普渡大学和圣刘易斯大学的教师和管理人员将从事广泛的公共和私营实体,以确定和讨论集体需求的目标,创造一个可行的研究路线图和相关的研究在合作伙伴愿意参与和投资的推力。会议将讨论主题和潜在的中心研究重点,并确定对行业最重要的主题,并据此创建可行的中心。这项工作的更广泛影响包括提高经济生产力和整合使用或希望使用地理参照的经济部门;劳动力培训,学生参加大学教师提出的与行业相关的基本竞争前研究项目;通过与专注于从服务不足的人口中招聘和再培训学生的机构实体建立各种伙伴关系,使代表性不足的群体参与进来;以及对公众和幼儿园的宣传产学合作研究中心,如由俄亥俄州州立大学、普渡大学和圣刘易斯大学共同提出的专注于环境地理参考和地理空间技术及应用的三站点中心,是大学、私营部门、政府、非营利组织和当地社区之间的强大伙伴关系。这些中心作为财团运作,大学教师和学生进行基础研究,解决集体工业和社区的需求,项目由会员费和中心成员的其他财政捐款资助。在这一结构中,大学的智力、基础设施和学生才能集中于真实的世界问题,这些问题是需要创新的经济部门的关键利益,以克服个别实体无法克服的概念和技术障碍。拟议的规划会议将使有关各方聚集一堂,听取原型中心领导层描述中心的价值主张和希望成为中心成员的实体的潜在投资回报。参加计划会议的人员还将帮助中心制定研究路线图和研究重点,以最好地满足中心集体成员最迫切的需求。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
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Ayman Habib其他文献
Forest feature LiDAR SLAM (Fsup2/sup-LSLAM) for backpack systems
用于背包系统的森林特征激光雷达 SLAM(F²-LSLAM)
- DOI:
10.1016/j.isprsjprs.2024.04.025 - 发表时间:
2024-06-01 - 期刊:
- 影响因子:12.200
- 作者:
Tian Zhou;Chunxi Zhao;Cameron Patrick Wingren;Songlin Fei;Ayman Habib - 通讯作者:
Ayman Habib
Multi-dimensional and multi-temporal motion estimation of a beam surface during dynamic testing using low-frame rate digital cameras
- DOI:
10.1007/s12518-017-0184-0 - 发表时间:
2017-04-10 - 期刊:
- 影响因子:2.300
- 作者:
Ivan Detchev;Derek Lichti;Ayman Habib;Mamdouh El-Badry - 通讯作者:
Mamdouh El-Badry
Large-scale inventory in natural forests with mobile LiDAR point clouds
- DOI:
10.1016/j.srs.2024.100168 - 发表时间:
2024-12-01 - 期刊:
- 影响因子:
- 作者:
Jinyuan Shao;Yi-Chun Lin;Cameron Wingren;Sang-Yeop Shin;William Fei;Joshua Carpenter;Ayman Habib;Songlin Fei - 通讯作者:
Songlin Fei
A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds
用于从移动激光扫描点云中提取、分类和完成道路标记的深度学习框架
- DOI:
10.1016/j.isprsjprs.2018.10.007 - 发表时间:
2019-01 - 期刊:
- 影响因子:12.7
- 作者:
Chenglu Wen;Xiaotian Sun;Jonathan Li;Cheng Wang;Yan Guo;Ayman Habib - 通讯作者:
Ayman Habib
Scan Angle Analysis of Airborne Lidar Data for Missing Return Approximation in Urban Areas
城市地区机载激光雷达数据丢失回波近似的扫描角度分析
- DOI:
10.14358/pers.23-00018r2 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Hamid Gharibi;Ayman Habib - 通讯作者:
Ayman Habib
Ayman Habib的其他文献
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