Between the Poles

Quick overview of ASCE 75-22 "As-installed" Standard

In most jurisdictions worldwide the low quality of underground infrastructure location data has been identified as a top cause of underground utility damage. Existing underground data, most of which is stored in network owners' utility records, is inaccurate, out of date or simply missing.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 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." 

Until now there has never been a standard for an “As-Installed” (often referred to as “As-Built”) utility data record for which location, positional accuracy, geometry, dimensions, type, function, ownership, materials, or operational status of a utility feature are among the data documented. The lack of a standard for utility as-installed record data has resulted in utility records that are of inconsistent data quality, content, and formats not conducive for data exchange.

ASCE 75-22 Standard

The purpose of this standard guideline is to specify essential elements for recording and exchanging data about the location, size, orientation, function, ownership and other attributes of newly installed, relocated, or otherwise exposed utility infrastructure. The benefits of such a standard are the exchange of consistent underground and above ground utility data among construction stakeholders, support for digital project design standards such as building information modelling (BIM), and reduced underground utility damage through sharing of more consistent and reliable infrastructure data.

The 75-22 standard should be used whenever subsurface utility infrastructure is exposed such as newly installed, relocated and daylighted utility infrastructure. Various techniques of modern reality capture including LiDAR and photogrammetric scanning and high accuracy GNSS should be used to record  the location of exposed infrastructure to ensure survey accuracy.

Data exchange framework

The 75-22 standard defines a data exchange framework which includes a minimum, optional, and conditional set of elements that every utility data model may include.  Utility infrastructure data stakeholders have developed different data models and management tools to handle the data. The data exchange framework identifies specific data elements to collect and manage for newly constructed or exposed utility infrastructure to facilitate a variety of functions including exchange of consistent underground and above ground utility data among construction stakeholders and reduced underground utility damage through sharing of reliable infrastructure data.

The data exchange framework is derived from standard geospatial data models and includes: feature types, geometry types and feature attributes. Every 2D geospatial data model includes at a bare minimum the spatial data types: line strings, points, and polygons.  Among the data elements defining most utility data models can be found the following data types (often with a different name): segment, device, access point, support structure, containing structure, secured utility area, encasement, marker, and tracer. Associated with these feature types are attributes that define the properties of individual features. 

ASCE 38-22 Quality Levels

A Utility Quality Level is assigned by a Professional to each subsurface Utility Feature.  The Quality Level identifies the uncertainty of a subsurface Utility Feature’s existence and actual location to that of its documented location.

Utility Quality Level A (QLA): A value assigned to that portion of a subsurface Utility Feature that is directly exposed and measured and whose location and dimensions are tied to the Project Survey Datum. The subsurface Utility Feature shall be tied to the Project Survey Datum with an accuracy of 0.1 ft (30 mm) vertical and to 0.2 ft (60 mm) horizontal for the outside limits of the Utility Feature that is exposed.This is the default quality level for newly installed utility equipment.

Utility Quality Level B (QLB): A value assigned to a subsurface Utility Feature whose existence and horizontal position is based on Geophysical Methods combined with professional judgment and whose location is tied to the Project Survey Datum.

Utility Quality Level C (QLC): A value assigned to a Utility Segment not visible at the ground surface whose estimated position is judged through correlating Utility records or similar evidence to Utility Features, visible above ground and/or underground.

Utility Quality Level D (QLD): A value assigned to Utility Feature not visible at the ground surface whose estimated position is judged through Utility records, information from others, or from visual clues such as pavement cuts, obvious trenches, or existence of service.

Positional accuracy

Utility company records comprise the vast majority of the data we have about underground utilities. Much of this data is inaccurate, out of date or simply missing (especially in the case of abandoned infrastructure and infrastructure without a known owner). Jurisdictions around the world recognizing the critical importance of accurate data are implementing policies to ensure data quality. ASCE 75 is central to these initiatives and has already been mandated in Montana and Colorado as the "as-installed" standard for new infrastructure in state right of ways.

The ASCE 75 standard includes positional accuracy for horizontal and vertical data points.

In the context of underground infrastructure location data locators, professional surveyors and engineers, network operators, and one call centres have different responsibilities in collecting and certifying underground utility location data. For example, Colorado and Montana mandate ASCE 75 compliant as-installeds be submitted  for completed public construction projects. Understanding the equipment used to gather information and the reliability of that technique in that particular project situation is the responsibility of the person putting their sign and seal on the data. Assigning quality levels compliant with ASCE 75 requires sign off by a professional engineer.

Among the attributes required by ASCE 75 are several that define data quality for different utility feature types.  The horizontal and vertical datums shall be managed and exchanged at the record level using the horizontal spatial reference and vertical spatial
reference elements in the framework for data exchange;

• Horizontal spatial reference: Coordinate system, datum, associated with the X and Y coordinates
• Vertical spatial reference: Datum and geoid model for the Z coordinate
• Horizontal accuracy: Error of horizontal coordinates at the 95% confidence level
• Vertical accuracy: Accuracy level of vertical coordinates at the 95% confidence level
• Accuracy units Units: used to express horizontal and vertical positional accuracies.

During construction or when underground utility infrastructure is subsequently exposed, the horizontal and vertical coordinate observations are obtained at each distinct feature; at horizontal bends, vertical bends, and points of deflection; and along each distinct feature with sufficient interval frequency to achieve the required Positional Accuracy Level.

The Horizontal Accuracy and Vertical Accuracy requirements help determine the type of data collection equipment and data collection methodology. Different Positional Accuracy Levels are possible when using global navigation satellite system (GNSS) equipment and real-time kinematic (RTK) methods, post-processed mapping GNSS equipment, and mapping GNSS equipment without post processing. The combined effect of the characteristics of the positioning equipment, data collection procedures, and postprocessing support a specific statement of accuracy. For example, equipment and data collection and post-processing procedures that are designed to achieve a 3 ft Positional Accuracy Level cannot be used to certify an accuracy smaller than 3 ft.

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).

An infrastructure digital twin provides a dynamic environment for managing underground assets of a multi-utility campus

For organizations managing a campus such as educational facilities, healthcare facilities, commercial real estate, utilities, and municipalities managing public works, storm sewer systems, and parks which are characterized by having many different utilities and and other infrastructure, an infrastructure digital twin provides owners, operators and third-parties with a streamlined, cloud-based solution that maps, manages and shares a wide range of subsurface and above ground assets, including fibre, pipes, electric power cables, lighting, conduits, and other structures enabling campus asset owners to not only track their assets but to provide all the information about their assets; their history, their replacement cost and their current cost without which you cannot justify your financial demands, be it a budget request for next year or a bid bond for a major project.

Infrastructure digital twin

A major challenge for legacy asset management systems is that documents are hard to access. Problems in the field are hard to identify because it takes too long to access the relevant documents and other information in the field. It's hard to share architecture and utility drawings with contractors and other stakeholders. People are really copying large blueprints or as-builts and delivering a set of documents to a new contractor who's just been onboarded and the contractor has to spend weeks studying these documents and comparing this information to what's there, what's underground, what's above ground and matching it together.

The way we address this challenge is by creating an infrastructure digital twin which provides accessible information to all stakeholders through a subscription service. A full digital twin of your campus, is a big, costly project, but an infrastructure digital twin is simpler and much faster. It involves site verification and infrastructure mapping as a service including drone imaging, site surveying, walking the campus and verifying objects. If the customer is using some type of infrastructure detection, the accurate information from that system plugs into our system, so it remains live. We support Open Geospatial Consortium standards to ensure interoperability. We onboard all of the data and we do that for any new updates on the campus. If something is wrong in the map and it's not in the site, or in site and not in the map, or if the drone gave us a better accuracy, we detect that and make corrections. For example, customers often hire utility engineering firms to conduct subsurface utility surveys (SUE).  We load that information and match it with everything we have in the system about their underground and other assets. Full onboarding requires 40 days or less. And more importantly, all the data is owned by the client all the time.

We've loaded over four million assets for our customers. Those assets are being used by thousands of users, including facilities, teams, construction managers, engineering and architecture firms, local utilities, fire departments and others.

You can share data immediately with anyone you want. And there are no limits on the number of users in any of our systems. If you onboard a new construction company for a new project, you just provide them with a login, specify what they can access and see, and they are onboard.

Dynamic view of your assets

The system provides a dynamic view of all your assets. It's not a static image, GIS or CAD files which you update every several months. It's real time, live presentation of information coming from a database and it is integrated with all your other systems. You can drill down on every single conduit, cable, pipe, or other piece of infrastructure and query all its attributes and see the sources of the data. You have access to the data in real time from your phone, your tablet, or your laptop because it is a fully cloud solution. We are able to do that because we are compliant with the Open Geospatial Consortium (OGC) and follow OGC standards. We are using open systems in terms of representing all the attributes about every item.  For a typical campus environment, ranging from a few blocks to a college campus or hospital complex, for example, you may have 20,000 assets. All of those assets and their associated attributes are captured and live in that database. We're building IOT integration, so you will have real time data on top of their various asset layers. You can see the water flow and the temperature and the pressure from the sensors. To go back to the updates. We are building integration with augmented reality. so that all underground assets can be visualized in 3D AR.

We provide a dynamic way of having base maps. You can use Google Maps, Esri World Imagery, or OpenStreetMap or even a mix of these. You can add your own imagery. 

In addition to the dynamic view of the locations and attributes of all your assets, you also have a plan room. All your documents are there, your engineering drawings, as-builts, survey and SUE reports, legacy maps, and other sources of underground data. You can search them by date or by the contractor or surveyor who supplied this data to you. These documents are accessible to you at any time from any device.

Managing asset budgeting

More importantly, the system provides an executive summary of all your assets. For example, in this site, which we have built as an example (we cannot share real data publicly) we have included about $4 million worth of assets. FM people need to deliver projects on time, minimize disruption of services to those people who are using these facilities, and improve the sustainability of their facilities. To do this requires that you can justify your CAPEX and OPEX budget. For some capital projects, you might be able to look at the total cost for adding a new building. But for budgets for operations and maintenance, retrofitting a fiber network, changing your irrigation network, it's really difficult to do that without having access to everything about your assets, their history, their replacement cost and their current cost. Without this information you cannot justify your financial demands, be it a budget request for next year or a bid bond for a major project.

The key to managing budgets is by replacement costs. If you want to replace your electric network, you need to know how you built the electric network in the first place. The database provides access to all this information. You can break your assets down by type of structure; electric power, conduits, lighting, water, wastewater and gas pipe networks. You can see their life expectancy, aggregate them based on their expected time of expiry and then decide on remedial action. You also need to worry about the impact of potential accidents and most importantly your liabilities. If a major underground gas line is damaged, then you might face heavy liabilities because it might interrupt gas service not just to your campus but for the entire area or even an entire municipality.

Our system has an ROI calculator so you can compute your potential savings. This has helped customers secure grants because they were able to compute the information needed to submit a bid bond.

Conclusion

An infrastructure digital twin provides immediately accessible information to all your assets for all stakeholders through a subscription service. It's a real time, live presentation of information coming from a database and it is integrated with all your other systems. For critical budgeting operations and maintenance, you have immediate access to everything about your assets; their history, their replacement cost and their current cost that enables you to justify your financial demands, be it a budget request for next year or a bid bond for a major project.

This post is based on Moufid Charafeddine's (BetterGIS) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

ASCE standards reduce risk and expedite public highway construction projects

What the engineer does during planning and design has a multitude of impacts: traffic disruptions to the public, safety for workers on construction sites, avoiding delays and budget overruns on projects, decreasing risk to contractors, and reducing cost to the rate paying public. It really is a matter of public welfare and that's why the ASCE 38 and 75 standards are engineering standards. What we'd like to do is enter into a world where we we have as reliable information about the subsurface as we do about the above ground and the location of underground infrastructure is well understood in the design stages.  Then we can start applying modern 3D BIM design technologies, virtual design, and new other technologies to enable the construction process to proceed as rapidly as possible with the least amount of risk and rework. And that's going to save everyone money and a lot of headaches.

Regulating the public ROW

With respect to underground utilities the key idea is to avoid, accommodate and then relocate. This will require changing the mindset of agencies such as DOTs that should be regulating work in the public right of way. Currently many DOTs allow the utility operators free rein to build and operate in the ROW. They take a hands off approach saying it is not their responsibility, it's the utilities operators/owners problem. At this point it is generally recognized that this approach no longer works because everybody is accountable when things go wrong and a gas main gets broken and a building explodes. If we can accurately locate, map and accommodate underground infrastructure during planning and design, just as we do using above ground surveys, we expedite the project by taking the risk arising from underground utilities out of the critical path. By speeding up these jobs you can really save a lot of money on major construction projects.

ASCE 38-22 for SUE Surveys

In contrast to the 38-02, the latest version of the 38-22 standard for SUE investigations is both prescriptive and a performance standard. It is prescriptive in that it specifies the minimum actions that must be undertaken to achieve (or attempt to achieve) a particular quality level. As a performance standard it describes the professional judgment that needs to be exercised to determine the appropriate timing, sequencing, location and scope of a utility investigative effort to achieve the goal of reducing utility issues during planning and design by achieving Quality Level B or A for all underground utilities. ASCE 38-22, unlike 38-02, specifies levels of positional accuracy. 

We wanted to provide better definitions so that there's greater clarity in what constitutes a SUE survey.  The SUE survey must be stamped by a professional engineer and In addition be accompanied by an engineering report that goes with the submittals to help lay out what has been found, where issues and discrepancies were encountered, and the things you need to be especially careful about. It includes our recommendations going forward on the project, what you need to consider and  where you may need to do more advanced geophysical investigations or vacuum excavations to better define the design, where you need coordinate closely with utility owners, how to prepare construction documents and plan construction. The liabilities behind this document is significant. To protect the owner, the contractor, and the engineer whoever has provided the SUE data and service must be able to document what they did sufficiently to be able to stand up in a court of law and say, "We met the standard of care."

ASCE 75-22 As-installed

We define the spatial and the attribution properties of the facilities that we want to record, so that at the end of the day, we can actually create a digital twin in a user friendly format and consistent format. We have defined minimum, optional and conditional attributes and the minimum requirements, list the information we must have to create a digital twin or very useful 3D model of our infrastructure in a standardized manner.

In Montana we have a permitting system for work in the public ROW and we can collect these attributes and an approximate alignment at the time of applying for the permit. The person filling out the permit application understands the required attributes and can plug them right in. And then when the actual work is performed, the approximate alignment's upgraded to the actual observation via the survey data. ASCE 75 has been implemented in Montana for several years now and has won both Montana and national engineering awards.

We also support absolute spatial positioning and have have a framework for data exchange by having a standard datum. At the National Oceanic Atmospheric Administration (NOAA), this is the same thing we do for nautical charts and hazards to navigation. We're just applying it to hazards to excavation. Everything is referenced to the National Spatial Referencing System. That gets rid of all the distortion that happens when you're trying to take something on a sphere and flatten it on a flat plane. You get distortions, there's different projections, state plane, county coordinates, UTM and they all distort the image. By having everything referenced to the National Spatial Referencing System, when you bring it together, it's going to go in the same spot every time. And it accounts even for plate tectonics and crustal drift ln places like California, Whenever you're turned on your GNSS system, you're tied into the National Spatial Reference System. It is being upgraded and the next version should come out in another year.

ASCE 75 specifies levels of positional accuracy. There's an accuracy table that goes with the standard, which we basically piggybacked off CSA S-250, the Canadian standard. We have CSA S-250 people on  our 75 standards committee. They also told us three things that they wish they had included with CSA S-250; make it a digital standard, secondly make it 3D and thirdly include above ground infrastructure with the standard. So we made sure we brought those into ASCE 75-22.

The Open Geospatial Consortium (OGC) has adopted both ASCE 38 and 75 as important input to their Model for Underground Data Definition Initiative (MUDDI ). The MUDDI model provides a base conceptual data model for interoperability and data exchange.  It is being adopted world wide including by the NUAR initiative in the UK, the UNUM project in New ,and other jurisdictions.

Securing sensitive information

Data stewardship includes issues of sensitive and secure information.  Here in the U.S, we're using 49 CFR Part 1520, which talks about how to manage sensitive secure information that TSA has determined to be Sensitive Security Information, as defined in § 1520.5. (This part does not apply to the maintenance, safeguarding, or disclosure of classified national security information.) It's a big issue with many utility owners. They don't want that information available to everyone. The 49 CFR Part 1520 guidance defines what information needs to be kept secure and that has helped alleviate the data protection concerns of the utility owners.

Conclusion

With the imminent availability of the ASCE 38-22 and 75-22 standards, every underground utility segment that's depicted can be assigned Quality Levels based on ASCE 38-22 for existing infrastructure or from the ASCE 75-22 "As-installed" standard for newly installed infrastructure. For example, in Colorado both draft standards are mandated for all work in the public ROW. This enables a world where we we have as reliable information about the subsurface as we do about the above ground and the location of underground infrastructure is well understood in the design stages. Then we can start applying modern design technologies to produce better designs which enable the construction process to proceed as rapidly as possible with the least amount of risk and rework.

This post is based on Phil Meis' (UMS and ASCE) talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Standards-based subsurface data model key objective of New York's underground infrastructure resilience project

Improving disaster resilience requires accurate data about subsurface utility and other infrastructure. New York has has experienced several disasters over recent decades, foremost among them 9/11 and Hurricane Sandy. The experience of 9/11 and Hurricane Sandy showed that communities interested in addressing utility resilience currently have little access to underground infrastructure information, which is typically siloed in network owners/operators' proprietary systems, is rarely integrated with other data sets, and is inaccessible to the excavators, disaster planners, emergency responders and others who need it. The UNUM project, funded by the National Science Foundation, is designed to address the challenge of creating a single accessible map of all of New York's underground infrastructure to support emergencies and disasters such as 9/11 and Hurricane Sandy.

Underground infrastructure mapping in NYC

Over the past decades the City of New York has initiated several projects involving underground utility networks. The first project was the conversion of citywide paper maps of water mains into CAD format. The water supply map was near completion when the city began the development of the citywide base map in a GIS. The base map provided locationally accurate platform, including block, lot, street, buildings and photogrammetric imagery of the entire city. Since then most agencies have contributed their data to the base map project which is virtually all open and publicly available. The city's sewer paper maps were entered into the GIS and the water main maps were converted from CAD to GIS.  Both sewer and water main maps were integrated with the city base map, demonstrating the ability to use city's cadastral map data for underground mapping as well.

9/11 emergency

Within hours of the World Trade Center attack, an interim site was equipped to response to the emergency. The Emergency Mapping and Data Center was set up to respond to map requests. The map grid of the site was developed by the Fire Department of New York to enable first responders to navigate the site safely. In addition, maps were developed to advise citizens of street closing and outages of utilities. Maps were submitted to the media and newspapers with daily updates of these conditions for citizens. Thousands of maps were created and distributed within a six week period.The operation was nonstop for more than 18 hours a day for three months. The Deep Infrastructure Group (DIG) team digitized data of the World Trade Center site provided by infrastructure agencies and utilities. The digitized maps enabled first responders to navigate the site safely.  The combination of photogrammetry and LiDAR, revealed the location of fires burning underground. Freon becomes a poisonous gas when heated and locating the underground tanks enabled safe removal of the freon and the tanks themselves, preventing loss of life of workers on the site and residents in surrounding areas. This is a classic example of the value of knowing the location of  underground infrastructure.

The 9/11 emergency clearly demonstrated the value of compiling an integrated data set with a common base map that can be shared easily and quickly among all players. But the 9/11 experience also revealed the difficulty in achieving an integrated map with the currently available data sets and integration tools and was a primary motivation setting the directions for the UNUM project.

Unification for Underground Resilience Measures (UNUM)

To directly address the challenges experienced during these events the Unification for Underground Resilience Measures (UNUM) project was initiated to investigate subsurface data development, integration and interoperability options. Overcoming these barriers to data integration requires the design of an integrated 3D data system with robust security and data protection that is capable of integrating data from different network owners, municipal and other sources based on a standardized subsurface data model and a willingness to share data securely among all stakeholders.  One of the key goals of the the project is the development of a detailed road map for the implementation of a standards-based subsurface data model capable of integrating all of NYC's subsurface data from multiple sources into a single map.

UNUM involves two pilots in NYC neighbourhoods: Sunset Park, Brooklyn and the Grand Central Business Improvement District. These community test beds will be used to refine an infrastructure data integration strategy and to assess the vulnerabilities of utility infrastructure. The New York City pilot area are far apart in terms of demographics, diversity of their populations and their economic bases. They also differ widely in the types of underground infrastructure problems they have experienced.

Use cases addressed by UNUM

The UNUM project, which is supported by a National Science Foundation (NSF) grant, addresses emergencies and disasters that are related to underground infrastructure. Emergencies are typically characterized as localized incidents with limited damages while disasters are more wide ranging, cover a lot of area, cause a large amount of very expensive destruction. NYC has experienced two disasters in the last two decades: 9/11 and Hurricane Sandy.

There are many use cases that involve underground infrastructure. The UNUM project is focused on just two uses cases; emergency response and disaster planning and response. Other projects in NYC target other cases including excavations, excavations for new foundations, maintenance, smart cities, digital twin, and so forth.  For UNUM's use cases the data focus is on the subsurface utility networks, their features and attributes and their relationships to one another.

Quality and availability of subsurface infrastructure data in NYC

Over the past years extensive research has been carried out to determine what data is available in NYC and who is responsible for each data set.  It has been found that there is actually a fair amount of data. New York City itself, which manages the water and sewer utility systems, has very good data which is open and publicly available. In contrast for private utilities such as Con Edison, National grid and Verizon, knowledge about the structure, depth, and reliability (completeness, currency, and accuracy) of their data is uncertain because other than the through the 811 Street Markup (one call) system, there's very little sharing of this data. In general most of the underground utility and communications network data in NYC is within silos that are rarely, if ever, integrated into a single data set and are inaccessible outside of the networks owners themselves.

Single integrated map

The  The vision which motivated UNUM is a single integrated data set that can show all the different utility layers reliably (accurate, up to date, and complete) in 3D, together with other related GIS layers, registered to a common base map and georeferenced to a common, horizontal and vertical datum. This would reveal conflicts, interdependencies, and risks for cascading effects for NYC's utility network. Subsurface utility engineering (SUE) surveys are becoming more commonplace and data like this is becoming available for individual construction projects. This data is being captured in a GIS and compiled entire networks making it possible to see how different networks (electric, gas, water, sewer, steam, fibre,copper, etc) relate to one another.  Especially important are interdependencies such as telecom systems that require power, pumped water and wastewater systems, and virtually all utility systems that rely on digital communications because of the potential for catastrophic failure involving multiple networks.

An open standards-based model for underground infrastructure

To achieve the vision of a single map of New York's underground infrastructure will require developing a data model that encompasses all of NYC's underground infrastructure and that could form the basis for a subsurface digital twin for the city.  Bringing the relevant data together during 9/11 took weeks to do because the different data sets were not only in a variety of formats but each had a different data model. UNUM decided to take a standards-based approach and has adopted the Open Geospatial Consortium's Model for Underground Data Definition and Integration (MUDDI).  This model has the additional benefit that it is  is supported by the American Society of Civil Engineers (ASCE) 38-22 and 75-22 underground utility standards.  For NYC adopting the MUDDI model makes it possible to build on the work already done by other jurisdictions including Scotland, the Ordnance Survey of Great Britain, Denmark, Flanders, Netherlands, Singapore and others. Experience to date with the model has shown that the MUDDI model is comprehensive and able to describe all the key features that are found underground in NYC.

The illustration shows a conceptual picture of all the things that can be captured and integrated through the MUDDI model. At the centre are individual network features such as cables, pipes, valves, transformers, switches, and meters. Shown below the features it can be seen that each feature has attributes (age, material, location, dimensions, quality level, threat level, pressure, voltage, fiber capacity, etc) and lower right accessory features (access, support structure, container, serial#, etc). Underground facilities are surrounded by a natural environment (soil, geology, ground water, chemical contaminants). Together the network features, their attributes, supporting structures, and environment comprise the utility networks themselves.  Utility networks can be be represented in a GIS and related to other layers such as catchment areas, service districts, preservation districts, and so on.

New York intends to develop a detailed model (referred to as a profile) for the city's underground infrastructure based on the MUDDI conceptual model. The same conceptual model is being used in other jurisdictions. For example, in the UK the National Underground Asset Register (NUAR) project has created a UK profile for the excavation use case. Over the next few years based on this profile, all utility and communications networks in England, Wales, and Northern Ireland will be captured and integrated into a single map.

Conclusion

Improving emergency and disaster resilience requires accurate data about subsurface utility and other infrastructure. The 9/11 emergency clearly demonstrated the value of an integrated data set with a common base map that can be shared easily and quickly among all players. But the 9/11 experience also revealed the difficulty in achieving an integrated map with the currently available data sets and integration tools.The vision which motivated the UNUM project is a single, integrated and accessible map for New York City that integrates reliable location data for all of New York's underground utility layers in 3D, registered to a common base map and georeferenced to a common datum. To achieve this will require a shared data model enabling location and other data from different utilities and other sources to be integrated into a single map of New York's underground infrastructure  UNUM has taken a standards-based approach and has adopted the Open Geospatial Consortium's MUDDI model, also used by other jurisdictions such as the UK. The UNUM project is developing a road map for the creation of a shared data model encompassing all of the city's underground infrastructure based on the MUDDI model. 

This post is based on Rae Zimmerman's, Alan Leidner's and Wendy Dorf's talk on the UNUM project at the Subsurface Utility Mapping Strategy Forum (SUMSF).

Canadian Underground Forum: Today is Day 2

You can see the program for April 30 and register here.

Speaker lineup

An early innovator in underground utility mapping is Otto Ballintijn, CEO of Reduct which has developed inertial or gyro technology for mapping underground pipe networks. In his keynote he will explain the evolution of the technology including the concept, operational method, and data output of gyro mapping technology.

A number of jurisdictions around the world are developing digital twins at the urban, regional and even national level. Danny Petrecca of Locusview, an early leader in capturing and managing underground data in the gas industry, will review why as-builting still remains paper-based and discuss a digital construction solution that stitches together existing technologies to create the reliable digital twin that utilities need in the age of the smart grid.

Government has a crucial role to play in locating and mapping underground infrastructure. Gord Reynolds will present Infrastructure Ontario's vision for province wide mapping of underground infrastructure. Gordon McElravy, of buildingSMART Canada, will discuss buildingSMART's efforts in implementing the Municipal Infrastructure Council Program to encourage building information model (BIM) initiatives at the municipal level.

Carsten Roensdorf and Neil Brammall will introduce the Open Geospatial Consortium's work in progress, the MUDDI standard for exchanging location data about underground infrastructure. Lawrence Arcand, who has led Subsurface Utility Engineering (SUE) investigations on a national level since 2003, will provide an overview of the Canadian standard CSA S250:20 Mapping of underground utility infrastructure.

Leica Geosystems' IDS Georadar was one of the early innovators in ground penetrating radar. Simon Pedley, Detection Ambassador at Leica Geosystems will talk about some of the innovations in hardware and software that Leica Geosystems has developed in the last couple of years.

Canadian Underground Forum begins tomorrow

You can see the program for April 29 and 30 and register here.

Speaker lineup

One of the early innovators in applying LiDAR to mapping underground pipelines, Joseph Hlady, President at Lux Modus in Calgary, Alberta will explain n his keynote the benefits that his technology has brought to the oil and gas pipeline industry. Another early innovator is Otto Ballintijn, CEO of Reduct who has developed inertial or gyro technology for mapping underground pipe networks. In his keynote he will explain the evolution of the technology including the concept, operational method, and data output of gyro mapping technology. A number of jurisdictions around the world are developing digital twins at the urban, regional and even national level. Alex Shalash of UEVO will discuss creating a city subsurface infrastructure map which unifies fragmented information and produces an intelligent digital twin. Danny Petrecca of Locusview, an early leader in capturing and managing underground data in the gas industry, will review why as-builting still remains paper-based and discuss a digital construction solution that stitches together existing technologies to create the reliable digital twin that utilities need in the age of the smart grid.

Our chief guest speaker is Jim Anspach, Chair of the ASCE 38 standards group, who will bring us up to date on the imminent 38-21 utility as-built standard. Carsten Roensdorf and Neil Brammall will introduce the Open Geospatial Consortium's work in progress, the MUDDI standard for exchanging location data about underground infrastructure. Lawrence Arcand, who has led Subsurface Utility Engineering (SUE) investigations on a national level since 2003, will provide an overview of the Canadian standard CSA S250:20 Mapping of underground utility infrastructure.

Rob Martindale, Program Manager - Utilities/Railroads at the Colorado Department of Transportation, will present an overview on how a mix of legislation and regulation has enabled Colorado to revolutionize the capture and sharing of accurate underground utility location data. Page Tucker, President & CEO at ProStar which implemented the mobile and cloud solution adopted by the Colorado DoT will speak about how the partnership between Colorado DoT and Prostar made this successful. One of the remarkable new technologies applied to mapping underground infrastructure was developed by 4M-Analytics. Harel Dan, Head of GIS and Remote Sensing, and Ophir Wainer, Director of North America Business Development, will explain how 4M-Analytics uses satellite and aerial imagery to map underground infrastructure. Leica Geosystems' IDS Georadar was one of the early innovators in ground penetrating radar. Simon Pedley, Detection Ambassador at Leica Geosystems will talk about some of the innovations in hardware and software that Leica Geosystems has developed in the last couple of years.

Government has a crucial role to play in locating and mapping underground infrastructure. Gord Reynolds and Alon Gat will discuss how Infrastructure Ontario partnered with Metrolinx and the Ministry of Transportation to establish the Utility Coordination Program (UCP), a new initiative which oversees the development of utility relocation standards and the supporting process to improve the location, treatment and relocation of utility infrastructure. Gordon McElravy, of buildingSMART Canada, will discuss buildingSMART's efforts in implementing the Municipal Infrastructure Council Program to encourage building information model (BIM) initiatives at the municipal level. Capping the first day of CUF Prashant Shukle, who was responsible for mapping and earth observation in the Canadian government for many years, will moderate a panel discussion on the role of government in underground infrastructure with panelists from the State of Colorado, the U.K. Government's Geospatial Commission, Public Services and Procurement Canada, and Infrastructure Ontario.

Standards for underground location quality are rapidly evolving

In the last two years quality standards for location quality of underground infrastructure in the U.S., Canada, U.K. and Australia have been revised or augmented.  This reflects the accelerating development of new technologies and evolution of existing technologies.

For example, the release of the new ASCE Standard Guideline for Recording and Exchanging Utility Infrastructure Data (also referred to as the utility as-built standard) developed under the auspices of the American Society of Civil Engineers (ASCE’s) Construction Institute(CI) and Utility Engineering and Surveying Institute (UESI) is imminent. Its purpose is to facilitate data collection, data management, and data exchange among stakeholders. It is designed to complement the widely used ASCE 38-02 Standard Guidelines for the Collection and Depiction of Existing Subsurface Utility Data.  As a reminder ASCE 38-02, published in 2002, is intended to standardize the classification of the quality of location information about existing subsurface utility networks on engineering drawings. The standard defines four quality levels based on the method used to determine the location of underground assets.

It is interesting to review global underground location quality standards and see that most of these standards have evolved in the last years which reflects the accelerating development of new technologies for detecting and locating underground infrastructure.  For example, ASCE 38-02 is not prescriptive with respect to accuracy and is generally 2D in focus.  Recent quality standards such as the soon to be released ASCE as-built standard, PAS 128, the French DICT, and Canadian CSA S-250 specify accuracy levels in 3D.

1986 – Japan, Road Administration Information Center (ROADIC)

1998 – Japan, Road Management System (ROADIS) provides de facto standard

2002 – U.S. ASCE 38-02

2006 – Malaysia Standard Guideline for Underground Utility Mapping

2011 – Canada CSA S250:11

2012 – France Presidential Decree relating to excavations near underground, overhead or underwater transmission or distribution networks

2013 – Australia AS 5488

2014 – U.K. PAS 128

2015 - Ecuador NTE INEN 2873

2017 – U.K. PAS 256

2019 – Australia Revised AS 5488.1:2019

2020 – Canada Revised CSA S250:20

2021 – U.K. Revised PAS 128

2021 – U.S. ASCE Standard Guideline for Recording and Exchanging Utility Infrastructure Data aka “Utility as-built standard”

Above and underground infrastructure comprise Rotterdam's open digital twin

The City of Rotterdam is developing a digital twin of the city.  3D vector and image data of the above- and underground infrastructure, zoning regulations and city by-laws, data streams from real-time sensors, applications and simulations will comprise Rotterdam's digital twin. It is envisaged that the digital twin will enable the city to assess the impact of proposed changes in bylaws and regulations by simulating them before they are implemented. Rotterdam, and in fact the whole of the Netherlands have adopted transparency and open data as a pillar of government and are basing 3D models on open national and international standards, intend for the 3D data to be open and accessible to the public, and intend to use open source software wherever possible.

Many cities have 3D models of their aboveground infrastructure; buildings, transportation networks, parks, and other infrastructure typically captured in 2D or 3D imagery from overflights or in 2D GIS maps.  Imagery is not very smart and extracting 3D vector objects such as building footprints, heights, profiles and transportation networks requires sophisticated post-processing.  Even more importantly, underground infrastructure is usually neglected, even though it is recognized that water and wastewater, energy (gas, electric power, and district heating),  and communications (fibre and copper) networks provide the life blood of the city.

Rotterdam has been developing its 3D model, which underpins the digital twin, for several years.  Currently it contains street furniture such as street lights, trees and underground parts of street furniture, a large scale basemap, a complete map of underground networks of cables and pipes, buildings, and non-physical content such as zoning regulations and  bylaws.  The 3D model is kept in sync with data repositories maintained by city departments and others.  In the future it will also include real-time data streams from a variety of sensors. 

The 3D model is open, based on open standards and many of the applications that have been developed for it are open source projects. For example, visualizations rely on Cesium, an open source initiative which has just announced support for visualizing underground utilities, mines, and geological layers. The rationale for the data being based on open standards is that an standards-based environment creates an open data commons where developers can devote their time to developing innovative applications rather than spending time converting data from one format to another.

The Dutch 3D standard is an integration of CityGML Application Domain Extension (ADE) and the existing national IMGeo standard for large-scale representations of roads, water bodies, and land use. The Dutch Kadaster, Geonovum, the Netherlands Geodetic Commission and the Dutch Ministry of Infrastructure and Environment initiated a "3D Pilot NL" to develop a national 3D standard.  Existing intnernational standards including CityGML(Open Geospatial Consortium), IFC (buildingSMART), and others were investigated. The findings of the pilot  resulted in a national 3D standard implemented as a CityGML Application Domain Extension (ADE) that integrates the latest version of the existing Dutch national standard IMGeo 2.0. 

Current applications (virtualcitySYSTEMS, ESRI, IMAGEM and Unity) use the 3D model as input for analytics and visualization. One can imagine many use cases for the data in the Rotterdam 3D model. Among these three use cases have been identified as low-hanging fruit; climate adaptation, viewsheds and energy performance of buildings. 

Much of Rotterdam is below sea level and managing rain water accumulation during heavy rainfalls is a major issue. which affects building basements, subway entrances, low-lying roads, electrical transformers, and other infrastructure.  To address this problem, the concept of "water squares" has been developed and the 3D model used to determine the optimum location for these..  Water squares are open areas where water can safely be allowed to accumulate during heavy rainfalls to avoid overstressing storm sewers. 

In another application of the 3D model data, building data can be filtered and streamed to energy performance applications that provide insights into how buildings perform.

Open standards and open data also enables the sharing of data among cities in the Netherlands.  The Dutch Kadaster has developed an open 3D map of the whole of the Netherlands, with scales ranging from 1:500 to 1:25,000 in collaboration with academic, private sector, and public sector partners.  All data in the 3D map is built on the basis of open standards.  The map was created by combining existing 2D data with the Elevation Model of the Netherlands resulting from a LiDAR survey which covers the whole of the Netherlands with a resolution of eight elevation points per square meter.

Open data and open standards also relate to a critical issue that is of growing importance. Many governments have adopted transparency and open data as a cornerstone of democratic policy.  But often digital twins for smart cities are being developed by private-sector firms or by public-private initiatives.  As William Davies points out in Nervous States, "the IT infrastructure used to build e-geovernment projects or "smart cities" comes from the commercial sector, and the companies that provide it often retain control over the data that results." 

For example, recently a joint Alphabet/Google/Sidewalk Labs and City of Toronto initiative to develop a smart city on Toronto's waterfront foundered and was cancelled.  One of the reasons, it is argued, is that a technology giant such as Google (or Facebook, Amazon, ...) is not a vendor in the traditional sense, but a "surveillance capitalist" that collects and analyzes behavioural data captured from users for its own economic imperatives.  In the context of a smart city the opportunities for data collection from citizens and from systems such as water and wastewater, transit, waste removal, traffic management, and others are immense.  If this data is appropriated and becomes proprietary, it puts a private entity in a position to supplant democratic government, which doesn't have access to this huge volume of data and the analytics which extracts information from it.  Relying on open standards and ensuring that data is open and accessible to the public are essential to avoid citizens' and municipal data becoming proprietary.  Furthermore, using open source software and, I would add, open training data for machine learning applications helps avoid the problem of the analytics also becoming proprietary.

Based on a Webinar by Christian Veldhuis, Advisor City of Rotterdam, Rick Klooster, CCO, FUTURE INSIGHT and Antje Kunze, Director Sales & Marketing, virtualcitySYSTEMS.

Australian water utility provides open access to water and wastewater network location data

Open data has become a goal for many governments, but is relatively rare among utility and telecom network operators and owners.  In Australia several water and wastewater network operators have made the location of their underground networks open and publicly available.  I had a chance to chat with Charles Moscato, Spatial Information Officer at Yarra Valley Water (YVW) about  their implementation of an open data portal which allows planners, designers, contractors and others to access and download raw geospatial data showing the location of YVW's underground facilities.

Background

Open data has been gathering momentum among governments.  An important impetus was the United States Federal Government where data has been in the public domain since 1909. The principle of free and open access to government geospatial data has been adopted by many governments including US Federal, Canada, Japan, South Africa, Brazil, California state and counties, and by municipal governments.  In 2013 G8 leaders (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, United States, and the European Union) signed a charter on open data.  The Ordnance Survey in the UK began releasing some of its data with open licensing as part of its OpenData initiative in 2010, but it wasn't until 2018 that the OS Mastermap was made openly available for the public and businesses to use.There is even an open data site showing underground natural gas flow and gathering lines in Colorado.

But being able to access prepared maps and other derived material is just a first step, governments need to provide access to raw geospatial data in commonly used Web-friendly formats. In the U.S. Data.gov was launched in late 2009.  In 2013, President Obama issued an Executive Order making open and machine readable the new default for federal government information in the United States.  At the municipal level North Shore City, NZ and Vancouver, Canada have been in the forefront of municipal open data.  By 2008 North Shore City had made its water, waste water and storm water network infrastructure publicly available on the web.  Vancouver's open data portal was initiated in September 2009.

A major motivation for open data has been government transparency.  But there is also evidence that open geospatial data contributes to the economy.  In 2008  Australia and later New Zealand commissioned studies of the contribution of spatial data to the national economy that have concluded that with appropriate government policies (such as open geospatial data) the contribution to the GDP, estimated to be on the order of 1%, could be doubled.

Open access to utility network location data

In Australia 12 local governments and water utilities have made the location of their water network open to the public.  The City of Gold Coast was the first in 2014. 

Yarra Valley Water serves over 30% of Victoria's population with water and wastewater services including 764,000 residential and 57,000 business customers. Historically the groups who needed access to the location information about Yarra Valley Water's underground network were (1) planners, surveyors, designers and other utilities who needed to plan around, build near or to connect to water and wastewater assets and (2) excavators and construction contractors who needed to avoid damaging underground utilities during excavation.

The process used by Yarra Valley Water to extract and provide this data was arduous, time consuming and slow.  20-30 request were processed per week requiring one full time person.  The turnaround time was typically about 10 days.

Business case for open data

In addition to a broad initiative at all levels of government to improve government transparency by making government data open, specific business benefits were identified.  The first objective was to make the process of providing accurate location and other data to designers and contractors more efficient freeing up one FTE for more valuable tasks.  Secondly automating the process would improve internal workflows for capturing, updating and publishing data.  Thirdly, replacing slow paper-based processes for data visualization in the field.  Another benefit, whose importance for improving data quality cannot be overestimated, is the ability for users of the data to report discrepancies. Another benefit is the avoidance of potential litigation when data was found to be incorrect.  From a business risk perspective an on-line system simply made access more efficient to data that was already publicly available via the Dial-before-you-dig (DBYD) service.

Data quality

In the mid 80’s to early 90’s the location of underground water and wastewater assets was digitized from paper drawings at various scales.  In Metropolitan Melbourne wastewater assets at 1 : 500 and water at 1 : 1000. In rural Melbourne both wastewater and water were captured at 1:25,000.  Attribute information was manually keyed in which was an error prone process.  The challenge was to find a way to convey to users the reliability of the data.

In 2018 Yarra Valley Water implemented the Australian quality standard AS 5488 - 2013 which provides users with a measure of the quality of location data enabling them to assess risk.  The AS 5488 quality levels are quite different from the U.S. ASCE 38-02 quality standard and the U.K. PAS 128 standard. The AS 5488 -2013 Quality Levels specify an absolute or relative accuracy.

1. Absolute subsurface feature location ±50 mm horizontally and vertically

2. Relative subsurface feature location ±300 mm horizontally and ±500 mm vertically

3. Relative spatial interpretation of position ±300 mm horizontally with indicative depths

4. Unverified and approximate position, roughly ±1 to 10 metres horizontally

About 75 % of Yarra Valley Water asset data was assessed a “D” Data Quality Level, this is either older data (2000s and earlier) or data with no association to a field survey.  After the 2000s the quality of asset location data is assessed at Quality Level C.  Typically this involves a site inspection to determine the location of visible above ground features and inferring the location of connecting pipes.

Open online access to network location information

In 2016 Yarra Valley Water decided to implement an on-line web application that contains up-to-date locations of current and proposed sewer, water and non-drinking water infrastructure in one easy-to-use map.  The site is updated daily with the latest available information and is open to all Australians.  The site was inexpensive to implement because the technology to do this has been around for a long time and was already used internally within Yarra Valley Water. The site provides a web map allowing users to browse the data.  Implementing this site freed up one FTE that had been dedicated to responding to requests for information.

But the most important feature is that once a user finds an area of interest, they can download raw geospatial vector data using the Open Geospatial Consortium (OGC) Web Feature Service (WFS) standard.  This capability allows users to extract data when they need it, whether for planning, design or construction. This capability has appealed to designers, in particular, because it allows them to integrate the data into their CAD or BIM design tool. The significant benefits that result from knowing the location of underground utilities prior to design are well known.

Currently field staff and customers can report errors, but this process is currently arduous and the capability is used infrequently.  There is an initiative underway to replace this with a very simple way to report data errors such as incorrect location, wrong material, or inaccurate pipe diameter.  In the future it may be possible for vetted customers to make corrections to the data directly.

This is an example of the direct business benefits of open data for network operators and to the wider construction community.  It illustrates that implementing an open web portal is inexpensive and can have immediate tangible business benefits.  But perhaps more importantly it provides significant benefits for a number of use cases including utility damage during construction, emergency response, and an essential part of smart cities.