Between the Poles

Automated feature recognition helps democratize utility mapping for producing infrastructure digital twins

In jurisdictions worldwide the low quality of underground location data has been identified as a top cause of underground utility damage. Existing underground utility data, most of which is stored in network owners' records, is often inaccurate, out of date or simply missing.  In North America in a survey of 402 locate professionals better facility maps received nearly unanimous agreement as a top strategy for improving the efficiency and quality of locates.  Singapore's Digital Underground project is unequivocal about the economic and social impact of low quality underground location data stating that "virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities."

Challenges facing the utility mapping industry

The root cause of the low quality of underground utility data is how utility mapping is structured as an industry. The primary activities of the utility mapping industry are data collection and how we keep and share this data with other parties.

Data collection

The way we collect is slowly changing, but predominantly we're still using traditional survey and locate methods. The survey methodology we are using hasn't changed in a hundred years. It's a very time and materials based, manual and analog process. Survey processes are slow and expensive to execute and require specialized equipment and skill sets, and safety is a big concern.

Locating underground utilities is one of the biggest causes of delays in construction schedules, service disruptions are a common occurrence, safety is always a major concern. and locate operations are slow to execute. Accuracy and timeliness are major issues. Locate operations require specialized equipment and skill sets which restricts locators to a small number of industry insiders.  A major shortfall is that underground utility location data is rarely available during the crucial planning and design stages of civil engineering projects.

Democratization of utility mapping

The utility mapping industry needs some fundamental changes. We need to move away from the monopoly that exists right now. There is just a handful of companies that are able to go out and collect underground utility data. We need to democratize data collection so that many more people can collect this data, targeting a crowd sourced approach and enabling multiple creators or collectors providing this data. This distributes the responsibility for the data as well. The more distributed the responsibilities, the lower risk and the lower the cost of failures will be.

We also need to change the skill level required. With current technology, we should be able to automate much of routine locating and thus enable many more people to do it. Using inexpensive technology will lower the cost of collecting and aggregating locate data. As data becomes lower cost to capture, data retention will become less expensive which will encourage people to retain the location data they acquire rather than simply painting/marking the ground and not capturing the data digitally.

Democratization is the real future of 3D infrastructure mapping. Right now data is still very siloed. We'll always have the traditional mapping companies, survey companies, line locating companies and asset owners going out and collecting their own data. But through crowdsourcing data from routine day to day locating and mapping, infrastructure projects, and other sources, aggregating that data and distributing access to that data is how utility mapping will really come to the forefront.  It should be easy to upload data into a central register where it can be made accessible to multiple stakeholders. Compliance with Open Geospatial Consortium (OGC) standards provides a basis for full interoperability enabling open access to every single conduit, cable, pipe, or other piece of infrastructure in real time from your phone, your tablet, your laptop or other applications.

Infrastructure digital twin

We think the best paradigm for producing and capturing asset data is through collecting it into an infrastructure digital twin. Digital twins have made a lot of progress in a variety of industries. Most importantly, for the utility mapping industry, it should be noted that creating a digital twin is much easier than it was even just a few years ago. We think the time is right for an infrastructure digital twin and it can be just at the asset level for one particular company, but we believe it should be at a regional level. Cities, municipalities and regional governments around the world are beginning to develop comprehensive digital twins of all of their physical infrastructure; buildings, roads, trees, power lines, and subsurface utilities and they're pouring resources into it.

And we think that Canada and the United States should start doing the same thing. And for us in the utility industry, data collection costs are lower than they ever have been. There's clearly interest among stakeholders and governments to start dealing with infrastructure and utility mapping problems, particularly to lower the number and costs of strikes, injuries and the loss of life. We now know a lot more about how to create data from other data sets, using different types of analysis and artificial intelligence to piece disparate data sets together. Using AI machine learning in the cloud is much less inexpensive compared to just a few years ago. And nowadays one of the things that makes infrastructure digital twins readily accessible to the utility industry is the fact that storing huge volumes of data in the cloud has become very inexpensive.

What is infrastructure digital twin ? First of all, a digital twin needs to result in effective action. It's not just enough say we have a digital twin philosophy The purpose of having a digital twin is to result in effective action that improves your business. It needs to use real time and historical data, which is paramount for us in the utility mapping industry, and it's going to be motivated by outcomes. For us in the utility industry, it is to reduce and even eliminate utility strikes during construction and the associated risks of injuries and fatalities.

The infrastructure digital twin has to be stored and managed in a relational database so it can be queried, analyzed and improved on through time, and related to other data sets. Processes for collecting data and maintaining data must be simple so that we can break the barrier of restricting underground utility mapping to a specialized group of people. If we truly want to democratize and compile data from disparate data sets, we need very simple processes to get data into the database, analyzed and maintained.

An infrastructure digital twin should be captured and maintained in 3D, even though most utilities in the ground are within just a couple of meters of the top of the ground. The 3D component is vital, because part of analyzing a data set is that much ancillary data is 3D such as overhead power lines that come down the side of a pole into conduits below the ground,

Imagery that's taken looking straight down at the ground, like traditional satellite imagery, can be visualized on a map and incorporated directly into a GIS database. But other types of imagery such as oblique, 360-degree, street-side, and inspection imagery need to be treated differently.  Oriented Imagery has a range of possible applications. For example, a construction company might use it to review asset inventory photos taken by field workers at a construction site. A telecom company or electric utility would use it to examine close range inspection images of electrical and telecom towers from many different angles. Oriented imagery applications enable you to see exactly where the image is on the map as you pan or zoom in the image. There are spatial navigation tools designed to work specifically with oriented imagery. You can query oriented images using spatial and temporal filters.

Finally, it is very important to have standardized formats, including storage formats and cloud formats.  It improves our ability to inexpensively use machine learning to do associative analyses of disparate data sets to make a larger, more conflated data sets.

Automated feature extraction

Maintaining accurate, up-to-date, comprehensive data about assets, above ground and below ground, has been a challenge for utilities for decades. Now new technologies including machine learning are being applied to automate real-time maintenance of utility network information and provide a foundation for infrastructure digital twins.

Automated feature extraction has advanced dramatically in the last few years.  This is an example where high density LiDAR and ultra high resolution imagery was used on a very low, very small diameter gas distribution install from an open trench. You can clearly see the ease with which the LiDAR produces a 3D representation of all of the local utility infrastructure.

Much of the the advancement in in automated 3D mapping have derived from the autonomous vehicle industry which have trickled down to other parts of the GIS industry.  The hardware and software are vastly easier to use and produce vastly better data. You can see from these ortho images collected from our system and the LiDAR overlaid on it. Feature extraction was used to automatically define top of pipe, top of ditch and actual joints in the pipe. Automated classification of LiDAR removes the need for someone to go out there with a Pogo stick and find the top of pipe. It makes it possible to simply drive alongside and scan with a mobile LiDAR system an open trench containing a newly installed pipe to produce a detailed 3D model with centimetre accuracy without having a person present. It is much safer - no boots on the pavement - and it drastically reduces the cost.

We prefer mobile mapping systems as opposed to handhelds because mobile mapping systems produce a tremendous amount more data and its registration tends to be vastly superior. It is much safer because it eliminates a need for someone to get out of a vehicle - so no boots on the pavement. Anytime you're still getting out, whether you're using handhold laser handhold mapping system, or a Pogo stick, you still have a person outside the vehicle walking around a street. With a mobile system you eliminate that risk, and you also drastically decrease the time required to do the collection. Here is another example where automated feature extraction was used to identify the electro fused coupling. You can see the butt joints, you can see the Ts and so forth. All this can be done very simply now with automated feature extraction.

Data registers

While surveyors may have had experience with a provincial or other types of survey register, utility location data identified during routine locate (one call) operations in general is only used to mark the ground and is rarely recorded, digitally or otherwise. Practically this means locators going back to the same location they were at just a few weeks ago to repeat the locate operation for another project. People in the locate industry are very familiar with this and the  amount of schedule and cost expense it causes. Exposing people to relocate or remap an area that was just done not too long ago is doubling their risk. Secondly, because the data is not retained or kept in easily accessible ways, we can't analyze it.  For example, we might want to confirm that we have correctly identified the type of utility, the accuracy of its location, or determine if there have been changes in its location, type, or current status since we last detected it. And because the data is not being retained and centrally managed at a global level or at a large scale level, there's no real opportunity for organic data enhancements or improvements.

The CGA Next Practices Report to Industry has made the case that a comprehensive national GIS map of buried infrastructure is among the opportunities for systemic improvement with the greatest ROI for industry. The benefits of a national register are greater safety, enhanced construction efficiency; reduced design, construction, and maintenance costs; greater regulatory compliance; improved locate efficiency; fewer project delays and budget overruns; fewer service disruptions to the public, less traffic disruptions and loss of custom and other inconveniences for the public.

Sharing maps of underground infrastructure is not totally new to Canada. For example, the City of Calgary's Joint Utility Mapping Project (JUMP) system, the ICI Society in British Columbia, the City of Toronto's Digital Map Owners Group (DMOG) and others have been sharing underground utility information for years.

The Canadian Underground Infrastructure Register (CUIR) is a new national initiative started at the beginning of the year. The CUIR initiative is intended to enhance current practices in a way that enables all stakeholder groups to benefit from the initiative; construction contractors, network owners, government transportation agencies, design engineers, subsurface utility engineering (SUE) engineers, surveyors, locators, workers in the field and the public. The CUIR initiative has been inspired by the National Underground Asset Register (NUAR) project currently being implemented in the UK. The objective of NUAR is to develop a register of location data for all underground infrastructure in England, Wales and Northern Ireland that is accessible to all network owners and government agencies.

This post is based on Joseph Hlady's (Lux Modus) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

Ontario adopts policy to reduce underground utility risk on public construction projects through collaboration

Utility locates and relocation work have historically been the cause of construction delays on public construction projects, particularly in urban areas with dense population and the commensurate underground utility and telecom infrastructure. With the massive investment require to improve infrastructure not only in Ontario but around the globe, improving outcomes and reducing risk has become absolutely essential. In recent bills the Ontario Legislature has addressed the problem of underground utility locates and relocations by providing strong enforcement options when the work is not completed accurately and in a timely manner. While the legislation provides strong measures for compelling utilities and other infrastructure owners to improve utility treatments and relocations, the legislation has resulted in a new approach to utility coordination based on a collaborative approach among utility owners and government agencies replacing the traditional regulatory mechanisms involving fines and other penalties for noncompliance.

Mitigating underground utility risk through collaboration

Infrastructure Ontario is an agency of the provincial government working with the private sector to renew the province's infrastructure by developing new linear projects with a focus on transit. Underground utilities represent one of the top transit project delivery risks identified by Infrastructure Ontario. Mitigating utility risk for transit projects is a continuous process involving many stakeholders.  Infrastructure Ontario and Metrolinx, the regional rail organization that runs the regional transit system in the Toronto area and South Central Ontario, have developed an approach to utility risk mitigation based on early collaboration, a single source of project truth, subsurface utility engineering (SUE) surveys to improve utility location data, shared workflows that ensure data freshness, options to avoid, protect or relocate utilities, and making underground utility location data accessible to all stakeholders including engineering firms and contractor bid teams.

Legislative foundation for new approach to utility coordination

The Ontario government identified an opportunity to expand on its transit agenda and launched a very ambitious program with four priority subway projects in the GTA worth just under $30 billion. In February 2020 the government introduced enabling legislation, referred to as the Building Transit Faster Act (BTFA). Among other things it provided mechanisms for compelling utility network owners to better coordinate their activities with government transit agencies. Under the legislation Metrolinx has authority to manage and maintain its own right of way like a municipality. It is responsible for issuing consents for utility conflicts or new utility construction activity within its right of way and easements.

Infrastructure Ontario and Metrolinx decided to keep the regulatory “big stick” as a rarely used exception and focused on developing a collaborative approach to utility coordination as a more productive and efficient way to achieve better utility coordination. In this vein the Utility Coordination Program (UCP) was launched in March 2020 with the goal of formalizing a new, more streamlined way of performing utility relocation work by enhancing coordination between stakeholders groups. The immediate objectives of the UCP were the development of a Utility Relocation Guideline and associated workflows. At the same time the Office of Utility Coordination (OUC) was created to monitor compliance with the processes designed by the UCP. Infrastructure Ontario began working with key utilities and telecoms, the Ministry of Transportation, the ministry responsible for the act, and Metrolinx, to develop a program to improve utility coordination. The goal of ths UCP initiative was a coordination scheme that would allow Metrolinx and utilities to work together to coordinate underground utilities without constantly relying on the legislation. To achieve this all of the industry stakeholders met at the beginning of the project to map out step-by-step how the work would progress. It was recognized that construction does not flow in a linear fashion, a lot of the work is happening simultaneously and there are hundreds of thousands of conflicts constantly occurring within the program at different stages. But major workflows were mapped out and a consensus was arrived at with all the utilities impacted by those workflows. The result was a well-documented understanding how construction would progress and what was required of each stakeholder at each step along the way. The results of these efforts were encapsulated in the Utility Relocation Guideline, which formed a set of regulations that became part of the BTFA act. It is a manual for how organizations will use the utility relocation workflows that have been developed.

Single source of project truth key to effective collaboration

It was recognized that the foundation for a collaborative approach to utility coordination is a single source of project truth. The Office of Utility Coordination (OUC) was setup to became the single custodian of project data and applications. The OUC has compliede a Spatial Library containing the alignments and engineering designs for all of the transit projects, the location of all utilities and telecom infrastructure, and identified utility conflicts. Shared workflows are designed to ensure that the data is kept fresh enabling an OUC dashboard that allows project activities to be monitored in near real time.

One of the major issues recognized by Metrolinx/Infrastructure Ontario is incomplete, inaccurate and out of data records of the location of underground utilities. Therefore to improve the quality of the data on all projects Metrolinx conducts subsurface utility engineering (SUE) surveys in accordance with ASCE 38 along the entire transit alignment. The level of SUE required is typically a minimum QLC/QLB with QLA at locations of interest including stations and transmission utilities. Combining the results of the SUE surveys with the alignments and designs in the Spatial Library results in a conflict matrix identifying the locations of utility conflicts. Based on this information treatments and relocation plans are developed and added to the Spatial Library.

The primary source of the data is provided by utility and other partners. Metrolinx improves on that data through SUE surveys. But in dense urban environments there is often unidentified infrastructure because it was mislocated, unknown or has no known owner. It has been found that many SUE providers and surveying organizations that Metrolinx/Infrastructure Ontario work with have institutional memory of their own experience independent of the records that the utility holders own and they are able to provide valuable insights and data. For example, they may have found that a specific utility's data is not reliable but that they, the SUE provider or surveyor, had actually already done a survey in the area and can share the improved data set with Metrolinx.

The OUC dashboard provides a number of spatial views that allows conflicts and construction activities to be tracked at the transit line level, the utility level, and the conflict level. For example it is possible to view one of the entire transit lines to see all the utility conflicts. Hovering over any of the conflicts provides data about that conflict; the identification and type of asset, the asset owner, information about asset from asset registries or from a SUE survey, and any plans to develop a treatment or relocation for it.

The Spatial Library is being compiled for current priority transit projects, but it is designed to be persistent. This is in contrast to the past approach where data was captured on a project basis, not maintained and effectively lost at the completion of the project. Now Metrolinx/Infrastructure Ontario will be retaining and maintaining the data within the transit right of way. The OUC dashboard and OUC spatial library will eventually be expanded beyond Ontario's four priority transit projects to also monitor public service works for transit in other parts of the province.

Infrastructure Ontario is working with the Centre for Advancement of Trenchless Technologies (CATT) at the University of Waterloo on a qualitative and quantitative assessment of the utility coordination program resulting from the BTFA. A report is expected at the end of the year that will not only describe how the improved utility coordination program provides benefits to Ontarians in managing the risks of transit project construction, but will also provide quantitative financial data on how this program improved outcomes and reduced costs and risk.

Expanding improved utility coordination to other public projects

In December 2020, Bill 222 Ontario Rebuilding and Recovery Act, went into force.  It extended the special regulations applicable to designated transit corridors in the GTA to other provincial “priority transit projects”.  It also provided measures to enforce compliance on highway projects by amending the Public Service Works on Highways Act.

Furthermore, the recently passed Bill 257 Building Broadband Faster Act, 2021 which is intended to expedite the delivery of broadband to under served areas of Ontario, revises the 2012 Ontario One Call Act in several way to improve utility coordination. Specifically it requires network owners to accurately locate their equipment within 10 business days after notification by Ontario One Call or face “pay for delay” or “pay for mislocate” penalties. The legislation mandates that the cost of these penalties cannot be passed on to ratepayers but are to be borne by shareholders. But the focus is on collaboration rather than on the regulatory stick. The Building Better Broadband Faster Act is built on the experience of the utility coordination achievements of the BTFA. The data, the application of the data, utility coordination program and the capabilities of the office of utility coordination to enable a single source of project truth have made it possible to work with all the key stakeholders on the broadband initiative in a transparent coordinated fashion.

Vision for the future

Effectively what Mterolinx and Infrastructure Ontario is the foundation for a digital twin. In the priority transit corridors the location and other properties of existing infrastructur including all the utilities and infrastructure such as the station boxes and the alignments, the tunnels, whether it's low grade, at grade or above grade all captured in 3D.  This approach is being expanded to other public projects such as highway expansion and improvement projects and broadband. Combining this data and applications could provide reliable data and capabilities for a significant proportion of public projects in the province. There is also evidence that industry is finding that the advantages of the new utility coordination regime can be applied to other non-public projects raising the prospect of a single utility coordination approach being adopted throughout the construction industry.

Conclusion

Since 2020 Ontario has put in place the legislative foundation for improving utility coordination on public construction projects in the province. It has used this as a way of motivating a concrete collaborative program to utility coordination that aims to avoid the combative environment that often characterized the traditional regulatory mechanisms involved in utility locates, treatments, and relocations.  An essential key to enabling this collaborative project environment is a single source of project truth based on shared data including the location of existing utility and other infrastructure, alignments and designs, utility conflicts and treatment relocation plans.  Shared workflows are designed to ensure that the remains fresh enabling real-time monitoring of the status of utility conflicts, treatments, and relocations.  The legislation together with the implementations of the  Utility Coordination Program, the Office of Utility Coordination, the Utility Relocation Guideline and shared workflows provide a practical approach for improved utility coordination on construction projects that represents a model for other jurisdictions that have recognized the human and economic risk associated with underground utilities and are looking for practical ways to mitigate that risk.

This post is based on Gord Reynolds's talk at the Canadian Underground Forum. You can listen to Gord's talk on the GeoIgnite CUF Youtube channel.   Further insights can be found in the contributions of Alon Gat and Gord Reynolds to the panel discussion on the role of government in underground infrastructure.

British Columbia's ICI Society provides province-wide shared map of underground utilities

The only instance I know of in North America of a province or state wide database that includes maps of utility infrastructure is the Integrated Cadastral Information Society (ICI Society) in British Columbia. The ICI Society maintains a database of geospatial data, referred to colloquially as BC's "Costco for geospatial data", including utility infrastructure data (telecom, gas, electric power, water, and storm and sanitary sewers) updated weekly from the utility owners and other sources. Recently I had a chance to speak with Steve Mark, Director of Operations, about the data contained in the ICI Society's database and how it is used.

The database includes 30 million records of which 45% are utility records, 35 % parcel data, and the remainder address and assessment data.  ICI Society relies on collaborative processes among members with each member remaining a custodian of their contributed data and is similar to the Scottish Vault system in this regard. Access to ICI Society data is restricted to members and their contractors.

The ICI Society is a non-governmental and non-profit organization that is supported entirely by its members including telecommunications and utility companies (Telus, Shaw, FortisBC, BC Hydro, Enbridge, and others), local governments, First Nations, the Provincial Government, Federal Government departments, Land Title Survey Authority, and BC Assessment. The data in the ICI SOCIETY database is comprised of

• utility infrastructure data contributed and maintained by major utility organizations operating in BC

• sewer, storm and water data contributed and maintained by local government and First Nations

• cadastral and parcel maps

• a Province wide Civic Address feature class known as AddressBC, provided by local governments and First Nations

• assessment data

ICI Society data is used primarily for the following use cases; efficient communication between telecoms, utilities,and local government, safe operations and emergency response, optimized infrastructure planning and operational service delivery, and shared spatial reference data for engineering and survey work.  There are legal agreements covering sharing of the data contained in the ICI Society database. Data is refreshed weekly as part of a shared process called Geoshare that even involves scripts being installed on members records system to extract data efficiently. There is a feedback process that allows corrections from the data consumers. Members are assiduous in making any corrections reported by their fellow Society members. All utility data can be viewed in a single map view using the Society's Web Map Viewer. A harmonized data model, common symbology and layout were determined by working in collaboration with all the data providers. All of the data contained in the database is searchable and available for download as vector files.

A use case that is not directly supported by ICI Society is preventing damage to underground infrastructure during construction. However, utility and telecom operators routinely consult the ICI Society data when planning, designing, constructing and maintaining underground infrastructure. But the ICI Society utility maps are not a substitute for BC One Call.

The ICI Society is a unique and remarkable non-governmental example of how in a collaborative environment infrastructure and other data can be shared voluntarily among utilities, government, survey companies and others.

Update on the Geospatial Act of 2017 presented at GITA Phoenix 2018

The Geospatial Data Act (GDA) of 2017, which was introduced in the Senate (S. 2128) and a companion bill in the House (H.R.4395), has some history. It was originally proposed in 2015 by Orrin Hatch, a Republican Senator from Utah, and sponsored by five Republicans and five Democrats. The 2015 bill consisted of ten sections and basically codified and strengthened federal government practices relating to geospatial data. The 2015 bill enjoyed wide support in the geospatial community throughout the U.S. and was generally viewed as a positive step forward.  In the Senate Committee on Commerce, Science, and Transportation, two additional sections were added to the bill that broadly extended the definition of survey data and mandated rules (Brooks Act) for procurement of geospatial services by the federal government.  Virtually the entire GIS and geospatial community reacted strongly and urged that the bill not be passed in its revised form because it would effectively exclude the entire geo community from recording the location of anything.  Since then the bill appears to have been revised back to its original form.  The texts of the latest Senate bill (S. 2128) and House bill (H.R.4395) no longer include Section 11 and 12 which were intended to apply the Brooks Act to a broad range of geospatial data and services.

At the GITA 2018 conference in Phoenix, Mark E. Limbruner, President of Geospatial Information & Technology Association (GITA), gave an update on the Geospatial Data Act of 2017.  As of 11/15/2017 the Senate bill had been read twice and referred to the Committee on Commerce, Science, and Transportation. The House bill has been referred to the Committee on Science, Space, and Technology and to the Committee on Oversight and Government Reform.

To summarize briefly, the bill formalizes the role that the Federal Geographic Data Committee (FGDC) shall have in leading the development, implementation, and review of policies, practices, and standards relating to geospatial data. It also establishes the National Geospatial Advisory Committee (NGAC) to provide advice and recommendations to the committee. The FGDC would exercise Federal oversight over federal geospatial expenditure and be responsible for ensuring a coordinated interoperable strategy for geospatial data in the Federal arena.  Each Federal agency would be expected to fit its plan for geospatial data into the FGDC's strategic plan. Four years after enactment of the bill, federal funding would be withheld for the collection, production, acquisition, maintenance, or dissemination of geospatial data that does not comply with the standards established by the committee. The bill also mandates that the FGDC would operate GeoPlatform, an electronic service that would provids access to geospatial data and metadata.

Mark pointed out that if sections 11 and 12 had been retained in the bill, the law could be interpreted so say that only a registered professional (engineer or surveyor) would be legally permitted to capture or manipulate any information tied to a point on the Earth including

• Most GIS, mapping, cartography, and imagery products and data;
• Data that may represented by points, lines, and polygons;
• Data depicting the distribution of natural or cultural resources, features, or phenomena;
• Data utilized by a Federal agencies;
• Data used to create general maps or prepared for atlases, educational materials, etc.
• Geospatial data used by law enforcement
• Geospatial data used by the military;
• Data that may be developed from; GPS units, aerial photography, unmanned aerial vehicles, etc.

Mark said that GITA has opposed the bill the appended procurement language because

• The bill creates an exclusionary  environment
• Has a negative impact on small firms.
• Stifles competition within the dynamic and innovative geospatial sector.
• Ignores the fact that the geospatial industry has implemented several certification programs to insure the ethical and  responsible performance of geospatial services
• Makes no allowance for GISPs to be considered certified professionals.
• Creates an atmosphere where GIS services are merely considered a function of the A&E industry.

Mark said that GITA is supportive of the original Geospatial Data Act because the benefits to federal, state and regional geospatial data users and technology are immeasurable. The combined efforts will elevate the profile of the relevant federal committees and councils, as well as the entire geospatial industry. Mark emphasized that should the  “procurement” language reappear in the bill during further debates, GITA will actively work to defeat the bill.

(Thanks to Glenn O'Grady of URISA for pointing me to the latest version of the Senate Bill.)

Open source-based mobile viewer creates value for electric power utilities with multiple GISs

One of the challenges faced by large utilities with multiple GIS, outage management, customer information and other systems, which are often the result of mergers, is how to make spatial and associated data, in different, often incompatible systems, accessible to all users not just GIS professionals.  At Distributech 2018 Duke Energy described an off-the-shelf web-based product based on open source components that is being used by 8000 users, 2000 of them using it 24/7 every day, to access GIS and associated data in several GIS systems, both online and offline, using laptops, tablets and phones.  An important advantage of the the selection process Duke used in selecting the product was that it made it possible for a relative newcomer to compete successfully with the traditional GIS incumbents.

Background

Large investor-owned utilities are often the result of mergers. For example, in 2012 Duke Energy and Progress Energy merged to create the largest U.S. utility with 7.4 million U.S. customers and approximately 52,700 megawatts of electric generating capacity. The challenge for the IT folks is how to manage the many disparate IT systems that such mergers bring together. Historically Duke Energy and Progress Energy have employed all the major GIS systems including Synercom, Intergraph FRAMME, GE Smallworld, ESRI, Intergraph GTechnology, and IBM GFIS. Accessing the valuable operational data in multiple systems is a challenge because each system has its own user interface and command structure. Over the years I have seen a number of solutions that have attempted to address the challenge of providing a common viewer for visualizing data from multiple data sources. But each has had limitations, for example, all data had to be synced to a central Oracle database, or it could only run online making it difficult to deploy to the field, or it had limited support for the many GISs and user files used by utilities, or it supported iOS on handheld devices but not Android, or did not support open standards such as OGC WMS and WFS, or did not support Google Maps, or was so complex users required extensive training and it was like pulling teeth to geth them to use iit.

At Distributech 2018 Ron York, Lead Consultant/ GIS, and Francisco Sarmento, Lead Consultant, both at Duke Energy described how Duke has resolved the problem of a common viewer for several different GIS systems which was brought to a head by the merger of Duke and Progress. They also described the somewhat unusual process by which Duke selected the vendor whose product provided the successful solution.

Open source geospatial components

A key differentiator of the product Duke chose is that it is leverages open source components; PostGIS, GeoServer and Leaflet being the main ones. The product builds on the collective wisdom, code and support provided by the worldwide open source geospatial community.  Leaflet is the leading open-source JavaScript library for mobile-friendly interactive maps. PostGIS adds spatial capabilities to the PostgreSQL relational database. It extends PostgreSQL so it can store, query, and manipulate spatial data. GeoServer is a web server that allows maps and data in a variety of formats to be served to standard clients such as web browsers and desktop GIS programs. Data is published via Open Geospatial Consortium (OGC) standard interfaces, such as WMS and WFS.  These are all Open Source Geospatial Foundation (OSGEO) projects.

Multiple GIS support

The product Duke selected supports ESRI ArcGIS, GE Smallworld, Intergraph GTechnology, spatialNET and Google Maps. At Duke it is used to access data in a couple of GISs as well as non-GIS data in Maximo, SAP, three different outage management systems, four or five customer information systems, and can link to CAD drawings and photos and images.  Importantly for engineering users it preserves the original symbology and background maps of the different GIS systems.

Online and offline

Another key advantage of Duke's solution is that it can run equally well offline and online and is able to seamlessly combine and switch between online and offline data.

Mobile and office devices

It supports mobile as well as office devices, running on laptops, tablets and phones running iOS, Android, and Windows. The user interface adapts automatically for each device, displaying map layers, forms and other data in a way that is appropriate for each device.

Synced or direct access to databases

It provides two ways of accessing data; one is to periodically import or sync data into the system's spatial database, which uses PostGIS, and the other is to directly access the data via a web service. It supports ESRI REST Services, which are used by ArcGIS Online and ArcGIS Enterprise, and the industry standard OGC WMS and WFS web services, which are supported by all major geospatial vendors.

It does not support direct edit of spatial data, but does allow a GIS data correction requests to be routed through Maximo following Duke standard business procedure for making corrections.

It is flexible enough to support different business processes for collecting and organizing data. For example, pole inspections are conducted by circuit in what was Progress service territories and by square area in what was Duke's service territory.

Simplicity of use

Another of the key differentiators of Duke's web-based solution is that it is not targeted on GIS professionals, but on the 95% of people within Duke who can get a lot of value from a really simple Google Maps style interface onto their enterprise data.

Leading up to and during deployment Duke put on road shows, made web demos available online, utilized computer-based-training, and provided job-aid documents to make people aware of the new system.  The tool caught on surprisingly rapidly. Within five months of it being deployed there were 5000 users. Currently there are 8000 users with over 2000 users accessing it 24/7 every day.

Anticipated and unanticipated benefits

The major expected benefit that was realized by the system is that eases the integration of Duke and Progess. All GIS asset data is exposed through a single tool to designers and asset engineers. But there were some unanticipated benefits as well.  It has had a direct impact on tax calculation - it shortened the cycle for internal auditors to complete tax assessments.  It is also widely used during storms and other natural disasters. It enables crews from different area to use a familiar tool to analyze unfamiliar data in areas where they haven't been before.  It is used to provide driving directions for field crews.  Storm managers overlay outages, assets, flood plains, wind, rain, traffic  and other maps for situational assessment.  It allows manager to share staging sites during storms. It has also  proved extremely useful in locating "unmatched outages" - outages called in by first responders with just an address.

Process for choosing a vendor levels playing field for new entrants

Duke used an interesting process to select the vendor. Preliminary vendor interviews were conducted to exclude vendors that clearly didn't have a solution that could meet Duke's needs. Remote web demos provided a cheap and easy way to narrow down the list of vendors. A request for proposal (RFP) process was used to solicit technical-only proposals from product vendors. (Duke was only interested in off-the-shelf products not bespoke solutions.)  Duke picked the top five and asked them to come to Duke for a scripted demonstration. Instead of asking the vendors for a benchmark demonstrating how their product would address Duke's requirements using Duke data, they asked the vendors to demonstrate their product using their own data. They picked the top two vendors and began contract negotiations to decide on one vendor which turned out to be Ubisense MyWorld, a relatively new entrant in the geospatial world.  An important advantage of the selection process Duke used is that it made it possible for a relative newcomer to compete successfully with traditional GIS incumbents.

Key registry created in the Netherlands to help model the subsurface

Increasing urbanization throughout the world challenges the sustainable development and resilience of cities. Despite this, the importance of the ground beneath cities is often overlooked.  A lot of information about the underground is captured during excavation and drilling. But this data is typically stored in project or private databases and is not shared. Capturing this data in a shared, open database is a first step toward developing a model of the subsurface that can be reliably used for urban planning, design, construction and maintenance.  The Netherlands has recently embarked on a national program supported by legislation and standards to do just that.

At the preconference workshop “Utilizing subsurface data for urban planning, design, construction and maintenance” organized by Geodan and TNO at the GEO|Design+BIM conference in Amsterdam, Michiel van der Meulen of the COST Sub-Urban program presented an overview of a new program supported by legislation to develop and maintain an open database of information about the subsurface in the Netherlands.

Current data about the subsurface in the Netherlands comes from borehole logs, cone penetration tests, and groundwater measuring points. Most of the currently available information about the subsurface in the Netherlands comes from boreholes drilled typically to over 100 meters using light drilling equipment as well as some boreholes to much greater depths using heavy drilling equipment. This information is available for free through the DINOloket of the TNO, Geological Survey of the Netherlands.

Based on interpolating from the existing borehole data, there are several models of the subsurface in the Netherlands developed by the Geological Survey of the Nethelands. For example, there is a model of the subsurface down to 50 meters. It has a resolution of 100 by 100 by half a meter. It was intended to assist in identifying aquifers and gas fields. This is too course for city planning purposes. What an urban planner needs is much higher resolution and that will require more data.

In 2015 a new law was passed by the States General or parliament in the Netherlands creating a Basisregistratie Ondergrond (BRO) or Key Registry for the Subsurface. The law mandates that if you excavate or drill you have to share your data (relating to soils, geotechnics, and groundwater) with the registry. In addition if when using the data in the registry you find something is incorrect you have to report it. The registry is required to be open and accessible to all citizens of the Netherlands. This new registry will require new standards for recording data and there is a major effort underway in the Netherlands to develop these standards. The implementation of the new subsurface registry will begin in 2018.

The Key Registry for the Subsurface or BRO is intended to provide transparent and accessible information about the subsurface. It is subject to the government's open data policy. In addition to providing data for urban planning, design, construction and maintenance, it is intended to include an inventory of opportunities and risks relating to subsurface, provide data for sustainable water management and to support the governments goal of moving toward a carbon neutral economy at the national regional and local levels. It is also intended to reduce the costs associated with infrastructure management for soil remediation.

The overall goal is to initiate the process of organizing an information chain that will harness geological survey and city efforts in building databases and creating the necessary standards for information production about the subsurface. But to better serve cities with the subsurface information they need, Michiel van der Meulen said that we need to rethink geological mapping, rethink urban information management, and create new alliances among organizations involved in urban development. The new BRO key registry is an important first step in the process of developing models of the subsurface that can be joined seamlessly with above surface information to create the comprehensive digital twins that will be required by the smart cities of the future.

Do you really need a GIS to do spatial analytics ?

Geospatial technology - spatial data management and analytics - is being used in many industries outside of the traditional GIS sector. Analysts and IT folks in these industries may be surprised to find that the spatial analytics and data management they require is already available from their database management system whether it is Oracle, SQL Server, PostgreSQL, or SQLite

At the GIS in the Rockies conference in Denver Dave Murray from the City of Westminster reminded us how powerful the spatial analytics in modern relational database systems are.

In the late 1980s the first implementations supporting spatial data in a relational database were Vision* on Oracle RDBMS developed by Geovision and GFIS on Db2 by IBM. Subsequently the Open Geospatial Consortium defined a simple feature specification for spatial SQL. First Oracle and then Microsoft and the open source community have added spatial SQL based on the OGC standard to relational databases. Oracle Spatial, PostGIS+PostgreSQL, Microsoft SQL Server, and Spatialite+SQLite all support spatial SQL.

Folks wishing to employ geospatial techniques may find that they don't need a traditional GIS product and all the complexity involved in integrating a GIS with their database management system. They very likely will find that they can do all the geospatial processing they require using spatial extensions to standard SQL on their existing database. Finding the number of automobile accidents along a stretch of road, the census tracts a road passes through, or the burglaries in a neighbourhood are straightforward spatial SQL queries. The results can be visualized using Oracle's own visualization tools or open source applications like QGIS sitting on top of Oracle, SQL Server or PostGIS+PostgreSQL.  Dave mentioned spatial SQL courses on Udemy for those interested in learning more about spatial SQL.  Gordon Luckett's web site has a wealth of information about spatial queries, especially for integration with AutoCAD and Esri ArcMap.  Gordon also has developed courses on Lynda such as Using SQL Server with AutoCAD and Learning QGIS.

 

U.S. congressional bill to restrict access to open geospatial information

A bill has been introduced in the U.S. House of Representatives that specifically targets open access to geospatial information.  It intends to do this by nullifying a regulation under the Civil Rights Act of 1968 that provides open access to data that enabled the public to identify and monitor housing discrimination, patterns of of integration and segregation in housing.

The Civil Rights Act, signed into law by Lyndon Baines Johnson in April 1968,  contains a provision (Title VIII commonly referred to as the Fair Housing Act of 1968) that prohibited discrimination concerning the sale, rental and financing of housing based on race, religion, national origin and sex.  Among other things the Act required the U.S. Department of Housing and Urban Development (HUD) to affirmatively further the goals of fair housing. In 2015 HUD published the "final rule" to provide clear guidelines on what fair housing and equal opportunity meant in practice.  It also committed to providing the data necessary to measure compliance with the final rule.  Specifically, to aid communities in assessing fair housing, HUD committed to providing "open data to grantees and the public on patterns of integration and segregation, racially and ethnically concentrated areas of poverty, disproportionate housing needs, and disparities in access to opportunity."

On January 12 of this year a bill was introduced in the U.S. House of Representatives, HR 482, that is intended to nullify the final rule on affirmatively furthering fair housing (AFFH).  If enacted, the bill would require that "no Federal funds may be used to design, build, maintain, utilize, or provide access to a Federal database of geospatial information on community racial disparities or disparities in access to affordable housing."  Some see this not only as a first step in reducing the regulatory efficacy of the Civil Rights Act, but in restricting access to what has been open information, specifically, open geospatial information or maps of certain types.

3D model-based design is transforming construction in the Netherlands

At the GeoBIM Building and Infrastructure Conference in Amsterdam, Ingeborg Ligtenberg, Senior Project manager for Mega Projects, described how the Dutch highway authority Rijkswaterstaat (RWS) is piloting a very different approach to highway construction.  Rijkswaterstaat is responsible for the design, construction, management and maintenance of the main infrastructure facilities in the Netherlands including the national road network, waterway network and sluices. 

Background

By way of background, the highway plan-design-build process that has been used over at least the past 25 years, starts off with a complete (re)survey of the project terrain to create a basemap on which the design is based. Existing as-builts in the area are rarely if ever consulted.  The deliverables from the design process are paper drawings which are used by construction contractors to build the project.  After construction is complete construction as-builts are submitted to the owner, but these are not "sealed", meaning nobody signs off on them putting their professional reputation behind their accuracy and reliability.  Practically what this means is that in the transportation industry, as-builts are treated as unreliable information and are rarely consulted.

At SPAR 3D 2013 Ron Singh, Engineering Automation Manager/Chief of Surveys, Oregon Department of Transportation (DoT) made a startling proposal that would stand the traditional highway construction process on its head. He proposed that the DoT should specify the data it needs and this data including BIM models and LIDAR scans would be collected by the DoT from the contractors during and after a highway construction project.  The data would be saved in a geospatially-enabled database maintained by the DoT.  One of the implications of this approach is that when a new project is initiated in the same area over 80 % of the necessary survey-grade information would already be available in the DoT's database, which would make the traditional complete resurvey unnecessary - saving time and money. 

As an example of how data reuse can save time and money, the Oregon DoT's first automated machine guidance (AMG) project was in 1997.   At that time in Oregon a few forward-looking contractors had invested in AMG technology foreseeing that it would save them money.  These forward-looking contractors were winning bids by lowering their estimates just enough to beat other contractors. They found that AMG technology did save a lot of money.  But the savings were going to the contractors, not to the DoT.  To redress this, starting in early 2015, the DoT began specifying on its bid web site that projects were "AMG ready" meaning that the DoT was making available provide all the data it had which could be download to see design BIM models, mobile LiDAR, overflight data, and so on.  The effect has been to drive bids down, because there is less risk for the contractor.  As a result the DoT is now sharing in the savings.

Rijkswaterstaat pilots

At the first GeoBIM conference in Amsterdam, Jothijs van Gaalen gave some real world examples of highway construction transformation based on GIS+BIM integration including using laser scanning/LiDAR for reality capture at the beginning of a design/construction project. 

The Rijkswaterstaat (RWS) board is convinced of the benefits of BIM and BIM/geospatial integration and decided initially to roll out BIM on four design-build-finance-maintain (DBFM) projects with the intention to require BIM on all DBFM-contracts in the future.

One of these projects is a highway expansion project for the highway connecting Schiphol-Amsterdam-Almere or SAA.  The objective of the SAA project was to improve the accessibility and quality of life within the SAA corridor. This includes the improvement of the natural and social environment and economic development and employment opportunities in the northern Randstad region.  A stretch of the highway is shown before and after the highway widening.

On many highway projects RWS had experienced information flow problems due to different formats and data models.  This not only necessitated major data conversion efforts, but also inhibited collaboration.  One of the goals of the SAA project was to improve information flow which was expected to also improve the flow of asset information for reuse during the maintenance phase of the highway.

A critical part of the RWS' new approach was the Information Delivery Specification (ILS). The ILS specified the general aspects of the required information including formats, standards, structure, and expected quality (timeliness and reliability) of the information.  The ILS was a contractual obligation that also required the contractor to accept new versions of standards on a yearly basis.  The standards that were adopted to ensure a uniform flow of structured information include VISI (an open communication standard for the construction industry based on XML), National Concept Type Library (CB-NL), Object Type Library (OTL), and COINS.

Today RWS is receiving a quarterly delivery of data from the contractor compliant with the ILS.  This is a remarkable achievement that required a lot of hard work during this first project.  But now that contractors are familiar with the standards and the ILS concept, it is expected that ensuring a uniform flow of information will be easier in future projects.  The next challenge is enabling this data to be used for asset management during the maintenance phase of the project.

Reusing data on future projects

 In response to a question, Ingeborg Ligtenberg said that this new approach was still in its initial phases but that the data would likely be made available to designers and contractors for future projects.  It would be made available on an "as is" basis, which sounds similar to how the Oregon DoT makes its data available on its bid site. 

As Ron Singh has pointed out, there is tremendous value in this data, but the value is only there if in the future engineers feel confident in using the data.  If very accurate and rich data were collected during and after construction including, critically, its lineage in the form of metadata about how and when it was captured, so that a future engineering user would have no reason not to trust its provenance and reliability, then there should be nothing to prevent the data from being reused in future projects - saving time and money.

Autonomous vehicles

One of the consumers of this digital highway information may be autonomous vehicles.  In-vehicle sensing is limited in its ability to enable safe and reliable driving under all conditions.  For example, on snowy roads, lane markings may not be visible. Such driving conditions will require the autonomous vehicle to be equipped with ultra-accurate maps to augment in-vehicle sensing systems.  Pre-loaded high-precision maps would enable autonomous vehicles to overcome many of these challenges – staying within lanes when wintry conditions obscure the lane markers. This will require sophisticated mapping systems and ultra-accurate and reliable DoT data to enable vehicles to navigate under challenging conditions.

Development of an open spatially-enabled BIM query language

Introduction

Building Information Models are comprehensive digital representations of buildings which include 3D geometry and associated data. They are widely used for designing, engineering, and constructing buildings (vertical BIM) and infrastructure (horizontal BIM). Owners are beginning to see value in BIM models and data for operating buildings. A BIM model includes not just the 3D geometry of all building elements and the enclosed spaces, but also semantic information representing both the type of the elements, the relationships between them (defined by the data model) and data associated with the geometric elements.  With BIM models being mandated in countries like the UK, Finland and Singapore, and in France and Russia in the near future,  and the increasing adoption of BIM by the private sector in the U.S. and other countries, the volume of BIM data is growing rapidly.  By merging models from different disciplines into common virtual models, the amount of information stored in such multidomain repositories quickly becomes large and complex even for average projects.  Centralized or distributed repositories enable the creation, integration and maintenance of large quantities of data from various stakeholders involved in the planning and building process and the wide range of specialized software tools used by these stakeholders.  Many ways of creating, manipulating and visualizing BIM and CAD models have been developed including CAD, Solibri, Navisworks, GTPPM, mvdXML, PQML, EQL, SQL, XSLT/XQuery, SPARQL, Gremlin, Linq, SDAI, and generic programming languages.  However, the development of a general, intuitive query language for BIM models including geometric and related information remains a challenge.

The information needs of individual stakeholders in different parts of the construction process are very different. For example, a structural engineer does not require all the information available in the overall building model such as the detailed specifications of a suspended ceiling. This makes the extraction of partial model subsets or domain specific views on large models necessary.  In addition regulations, energy performance modeling, and building maintenance require finding specific facilities among the building assets. 

In the last few years research has focussed on the development of an open, domain-specific query language for BIM.   Most of the this research has focussed on querying, updating and deleting data stored in Industry Foundation Classes (IFC) models.  The Industry Foundation Classes (IFC) is a vendor neutral and open model that defines more than 650 entities and a few thousand attributes.  Most industry software BIM tools  are able to export IFC models, though there is typically some information loss. The bimserver.org platform is an open repository of IFC models and already provides a Java API to extract partial building information models from a repository.

Open query languages

Many existing database applications use the Structured Query Language (SQL), an ANSI/ISO standard with spatial extensions standardized by the Open Geospatial Consortium (OGC), as the standard language. SQL makes it possible to create, read, update and delete records in a Relational Database Management System (RDBMS).  But the applicability of SQL in a BIM context is limited.  SQL is based on operations on data stored in tables.  Application to IFC, as a BIM example, would require storing 600 entities along with their thousands of attributes in individual tables.  Severe performance and scalability issues are associated with using SQL for BIM.  In addition the complexity of IFC-based queries would rapidly become too complex.

The W3C Data Activity has defined a collection of technologies comprising a “web of data” where information is easily shared and reused across applications.  Key parts of this technology stack are the RDF (Resource Description Framework) data model, the OWL Web Ontology Language and the SPARQL protocol and RDF query language which has been standardized by the W3C.  The Resource Description Framework (RDF) is a directed, labeled graph data format for representing information on the Web.  RDF and SPARQL  enables interoperability among distributed heterogeneous information systems making possible queries that span several repositories and link information from different sources.  Specifically in a BIM context it enables ad hoc queries of sub models that reside in repositories maintained by the different stakeholders in a building project.

Spatial queries

BIM data models can be simple or complex.  A complex data model includes logical relationships between building elements, such as windows and walls, walls and storeys, and so on.  These relationships are created at design time.  A simple model may not include any explicit relationships. For example, there may not be any way to determine which windows are associated with a wall except visually.  Spatial queries enables relationships to be determined after the creation of the BIM model.  For example, a spatial query can find all the windows in a wall, all the walls of a storey, or all the HVAC and plumbing facilities in a storey.

The efficient implementation of spatial BIM queries requires 3D spatial indexing such as octree and multi-dimensional r-tree indexes and algorithms.  It has been known for a long time that spatial indexing is a key technology enabling efficient GIS systems.  Very early on in the development of spatial indexes it was realized that these are domain specific.  Several GIS systems support an extended spatial SQL based on quadtree or r-tree indexes.  These systems evolved historically from a 2D geometric representation approach.  However, the functionality for processing 3D geometry in many GIS systems is restricted and the performance of the available tools is unsuitable for complex building data sets.  Therefore, it has been argued that a specialized tool is required to handle queries against elaborated BIM models containing vast numbers of elements and detailed geometry representations.  Such a query language enables the spatial analysis of BIM models and the extraction of partial models that fulfill certain spatial constraints. Among other features, it includes operators that reflect the topological relationships between 3D spatial objects including "within", "contain", "touch", "overlap", "disjoint" and "equal" in 3D space.  Both octrees and multi-dimensional r-tree indexes have been investigated as a basis for an efficient BIM query language.

Open BIM query language

A BIM query language, dubbed BIMQL, has been proposed as an extendable, open, domain specific query language for BIM models. It can be used to select and update partial aspects in building information models.  A limited implementation of the language has been built on top of the bimserver.org platform.  It is not complete, in particular it does not support spatial queries, but has provided a useful vantage point for future research, discussion and development on the development for end-user interfaces to complex BIM models as well as the composition of service-oriented architectures.

Unlike traditional data modeling approaches which are limited by the scope of their underlying schemas, RDF and related technologies can provide an open and common environment for sharing, integrating and linking data from different domains and data sources.  In comparison with domain-specific query languages, SPARQL is especially applicable for scenarios when data from multiple sources are needed.  Some important scenarios include

• All building objects should be classified according to the local building code
• Where are the locations of companies which produce the materials used in walls placed in the hall ?

The interesting thing about these scenarios is that they require the BIM geometry and data plus other external data. This is the type of query that the W3C semantic web technologies are designed to support.

In the AEC domain, a typical use case is data integrity validation against BIM standards or building code requirements.  A Dutch example is the Dutch BIM standard requires that all building elements should be associated with appropriate building storey.  Provided that the building model is represented in the standardized ifcOWL, it is possible to check this spatial containment relationship using off-the-shelf SPARQL implementation.

Currently SPARQL does not contain the vocabularies or functions required by many use cases in the construction domain.  Recently researchers have proposed extending SPARQL with a set of domain-specific functions for the construction world. There are precedents for this strategy.  In 2011 the Open Geospatial Consortium (OGC) released GeoSPARQL, a geographic query language for RDF data, which extended SPARQL with geospatial functions that enabled spatial queries.  The advantages of this approach is that not only are the extended AEC functions compatible with existing SPARQL environments, but they can also be used to query building data combined with data from other fields.

But standard SPARQL can only crawl the data graph according to the structure of the BIM data model.  There are many use cases that depend on geometric relationships that cannot be handled by off-the-shelf SPARQL. To address this SPARQL has been extended to include spatial operators "touches", "intersects", "disjoints", and "contains" and well as other domain-specific functions.

An example of a simple spatial query using a spatial extension is finding all walls which contain or adjoin doors.  This requires the spatial function "touches" to find doors that are in or near walls.

As in the case of geospatial data, additional domain-specific functions are required to provide the necessary functionality.  For example, functions were added for different ways of measuring distance (spt: defines the spatial extensions namespace)

• spt:distance is a function to return the shortest distance between two products in 3D space.
• spt:distanceZ is a function to return the shortest vertical distance between two products.
• spt:distanceXY shortest distance between the projections of two products on XY plane

Another example is finding all windows that are close (less than 300 units) to a column.

Implementation and testing

As a proof of concept, the extensions were implemented
based on the open source Jena ARQ query engine.  An objective was to try to  minimize low level coding to make the function implementations more portable and more transparent for public reviews.

As a practical example, a case study of building code compliance checking using the International Building Code 705.8.4 (IBC 2006) was tested successfully using a BIM model of a building with 4 floors.

The researchers report that the main limitations of the open SPARQL-based approach they have proposed relate to the way it was implemented.  The implementation is not completely portable and performance could be improved, by integrating existing geometric libraries, for example.   Future research will address these issues and investigate more use cases to extend functions for specific domains and to combine more data from different sources in the construction industry.

Sources

Querying Linked Building Data Using SPARQL with Functional Extensions, 2016, Chi Zhang and Jakob Beetz

Processing of Topological BIM Queries using Boundary Representation Based Methods, Simon Daum and André Borrmann, Advanced Engineering Informatics, Volume 28, Issue 4, October 2014, Pages 272–286

BIMQL – An open query language for building information models, W. Mazairac and J. Beetz, Adv. Eng. Informat. (2013)

Topological analysis of 3D building models using a spatial query language, André Borrmann and Ernst Rank, Advanced Engineering Informatics 23(4):370-385 · October 2009

Thanks to Léon van Berlo of TNO for pointing me to this research.