I-Corps: Artificial Intelligence Generated Labeled Maps from Reality Capture Data

这个I-Corps项目的更广泛的影响/商业潜力是简化建筑、工程和施工社区的工作流程，从而将可观的成本节约传递给他们的客户。I-Corps项目将加强对在广泛的野外条件下从各种多传感器数据和图像中自动提取特征的科学理解。这个项目需要创新的算法来处理和压缩庞大的数据集。该项目通过自动处理从地球轨道卫星、无人机或船只收集的大量数据，对应对气候变化具有潜在影响。该项目的要素可用于加强劳动力发展计划，包括地理空间信息系统的职业培训。这个I-Corps项目的可视化模块可以通过动手操作建筑环境的数字模型来支持大学预科课程的空间思维发展。空间思维与数学能力高度相关。该项目的商业影响将改变地理空间测绘行业，消除人工干预，允许机器以更高的精度和速度执行重复的测绘任务。因此，该项目将实现更准确和有效的数据收集、分析和决策过程，减轻人类的压力和紧张，并提高生产力。I-Corps项目基于自动化数据处理系统的开发，该系统将使用现实捕获数据提高绘制工作流程的生产力。建筑、工程和建筑公司在现实捕捉技术上投入了大量资金，以从点云和图像中绘制建筑环境的基础设施。这种投资的目的是减少与实地数据收集和随后编制建筑设计计划以及为民用资产管理项目开发信息系统有关的业务费用。然而，包含现实捕获数据的过时工作流通常会与非生产性流程纠缠在一起，从而减慢映射操作，增加人为错误，并产生负面的收入影响。一个可行的解决方案是一个创新的软件系统，该系统使用人工智能算法从点云和图像中提取有意义的信息，以生成行业标准文档和注释计划，满足施工前批准/许可要求。I-Corps项目将能够为智能基础设施资产管理、状况评估、风险管理和整体项目管理提供数字信息。该项目减少了人为错误，减少了制图制作时间，为客户节省了大量成本。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this I-Corps project is to streamline the Architecture, Engineering and Construction community workflows so that considerable cost savings are passed on to their customers. This I-Corps project will enhance the scientific understanding of automatic feature extraction from a variety of multiple sensor data and imagery over a wide range of field conditions. This project will require innovative algorithms for processing and compression of huge datasets. This project has potential impact on combating climate change through automatic processing of massive amounts of data collected from earth orbiting satellites, drones, or ships. Elements from this project can be used to enhance workforce development programs to include occupational training on geospatial information systems. This I-Corps project’s visualization modules can support spatial thinking development in pre-college programs through hands-on manipulation of digital models of the built environment. Spatial thinking is highly correlated with competency in mathematics. The commercial impact of this project will transform the geospatial mapping industry by eliminating manual intervention and allowing machines to perform repetitive mapping tasks with greater accuracy and speed. Accordingly, this project will enable more accurate and efficient data collection, analysis, and decision-making processes, relieve human stress and strain, and enhance productivity.This I-Corps project is based on the development of an automated data processing system that will increase productivity in mapping workflows using reality capture data. Architecture, Engineering and Construction companies are heavily invested in reality capture technologies to map the infrastructure of the built environment from point clouds and imagery. Such investments are meant to reduce operational costs associated with field data collection and subsequent production of design plans for construction and the development of information systems for civil asset management projects. However, outdated workflows that incorporate reality capture data often become entangled with unproductive processes that slow down mapping operations, increase human-induced errors, and yield a negative revenue impact. A viable solution is an innovative software system that uses artificial intelligence algorithms to extract meaningful information from point clouds and imagery to produce industry-standard documents and annotated plans that satisfy pre-construction approvals/permitting requirements. This I-Corps project will be capable of producing digital information for intelligent infrastructure asset management, condition assessment, risk management, and overall project management. This project reduces human error and decreases the mapping production time with significant cost saving to customers.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.