Knowledge Discovery in 3D Geospatial Spaces: Towards an Integrated BIM, 3D GIS and IoT Data Framework

世界正在进入智慧城市时代。物联网（IoT）和信息技术基础设施是智慧城市的基础，是连接智能设备、传感器和大数据分析的网络。据估计，到2020年，全球将有800亿台联网设备和超过50亿互联网用户。连接是不可避免的，将包括我们生活的各个方面，无缝地将工作，家庭和周围环境结合在一起。在这种情况下，3D虚拟城市空间是帮助智慧城市管理和监控室内-室外动态的关键。加拿大城市决心实现成为智慧城市的目标。为了促进动态的城市应用，如业务增长和监控基础设施的解决方案，多伦多和蒙特利尔都向公众推出了3D开放数据。然而，需要进一步的研究来处理多尺度分析和可视化在虚拟空间中，从城市规模的建筑结构，反之亦然，通过时间尺度无缝扩展。 这项研究的主要目标是增加我们对来自建筑信息模型（BIM），3D地理信息系统（GIS）和物联网的各种数据如何集成到一个环境中进行时空分析的知识。我们的研究将通过识别本体，语义和集成数据的几何定义，通过一个多维的图形结构的知识。9名HQP将接受培训，致力于开发1）处理BIM，GIS和物联网流数据的全面集成数据建模框架; 2）在3D室内-室外虚拟模型中进行数据挖掘的分析方法，以发现知识; 3）在无缝室内-室外虚拟模型中多尺度表示所发现知识的空间聚合操作; 4）地理空间数据共享，互操作性和质量评估框架，用于BIM，GIS和物联网的集成。 从我们的研究中获得的知识将使新技术能够通过与物联网流信息连接的虚拟空间来聚合和颗粒化大量室内-室外3D数据的图像。这些知识将对加拿大地理空间产业部门产生重大影响。我们的研究还将为3D数据共享的标准化提供技术进步、创新方法和深入见解。研究结果将应用于能源效率、应急管理、基础设施监测和设施管理等广泛的智慧城市应用。

The world is entering the period of smart cities. The Internet of Things (IoT) and information-technology infrastructure are fundamental to smart cities, a network of connected smart devices, sensors, and big-data analytics. By 2020, estimates are that there will be 80 billion connected devices worldwide and over 5 billion Internet users. Connectivity is inevitable and will include all facets of our lives, seamlessly bringing together work, home and surrounding environments. In this context, 3D virtual city space is key to helping smart cities manage and monitor indoor-outdoor dynamics. Canadian cities are determined to achieve the goal of becoming smart cities. To foster dynamic city applications such as business growth and solutions for monitoring infrastructure, Toronto and Montreal have both launched 3D open data to the public. However, further research is required to deal with multi-scale analysis and visualization in a virtual space, extended from city-scale to building structures and vice-versa seamlessly through the temporal scale. The main goal of this research is to increase our knowledge of how a variety of data coming from Building Information Model (BIM), 3D Geographical Information Systems (GIS) and IoT, integrate into a single environment to conduct spatiotemporal analytics. Our research will advance knowledge by identifying ontology, semantics and the geometry definition of integrated data through a multidimensional graph-structure. Nine HQP will be trained by working on the development of 1) A comprehensive, integrated data modeling framework handling BIM, GIS and IoT stream data; 2) An analytical approach for data mining in 3D indoor-outdoor virtual model in order to discover knowledge; 3) Spatial aggregation operations for multi-scale representation of discovered knowledge in a seamless indoor-outdoor virtual model; 4) A geospatial data sharing, interoperability and quality assessment framework for the integration of BIM, GIS and IoT. Knowledge gained from our research will enable new technologies that have an aggregated and granulated image of massive amounts of 3D indoor-outdoor data through a virtual space connected with IoT stream information. This knowledge will have a significant impact on the Canadian geospatial industry sectors. Our research will also provide technological advancement, innovative approaches and deep insight into the standardization of 3D data sharing. The results will be applied to a wide range of smart cities applications such as energy efficiency, emergency management, infrastructure monitoring, and facility management.