Between the Poles

AI and Multi-Sensor Underground Utility Mapping

AI and Multi-Sensor Underground Utility Mapping

There are no silver bullets—no single sensor will detect everything. Multi-sensor is increasingly popular, yet the challenge lies in processing and analyzing these together. AI is stepping up to solve this.

I love this analogy: “Let’s say you want to get a 3D picture of the inside of your body today; you have an MRI, CAT Scan, and Ultrasound,” says Jeremy Suard, Co-Founder & CEO of Exodigo. “But it’s 3 devices, 3 pieces of software, and potentially 3 different doctors that have to analyze them.”

This is the challenge faced when seeking to comprehensively map underground utilities. As noted in ASCE and UESI literature, the use of multiple sensors is the best practice for catching-it-all. Ground penetrating radar (GPR) and especially multi-channel GPR (MCGPR), time-domain electromagnetic induction (TDMI), magnetic (MAG) methods, sonic, ultrasonic, etc. I love my MCGPR unit, but have realistic expectations, especially in various soils type and moisture profile environments.

Mixing multiple sensors means having expertise in interpreting/analyzing each, and then you need to put it all together in a map. I don’t care how much MCGPR gets billed as #D underground mapping, it still takes a lot of experience and skill to “read the tea leaves”. But what if analysis with other sensors could be automated to corroborate and/or fill in what the other missed. I’ll make an analogy to the integrated positioning systems that are on mobile scanning systems and some UAS: an IMU is processed along with the precise GNSS—one can help catch outliers in the other.

Processing Together With AI

Exodigo has done this for multi-sensor underground utility mapping, for ad hoc systems mounted on carts, or for some limited applications, even a drone.  “It's better than doing all of them separately. And it removes the human interpretation factor in the field,” says Suard. “Because today, even though a lot of calculations are done in the field on your device, without broader context or corroborating data, it's a lot of human interpretation. You can lose a lot of data there. A lot of data, lots of room for decisions. So, you need to be a real pro at GPR, or whatever sensor.”  With staffing shortages across AEC industries presently, let alone finding an expert in each type of sensor, such an approach can be appealing.

An example of multi-sensor processing and analysis showing the locations, quality levels, and type of detection method used. Source: Exodigo

“So, the way we do it is that the measurements in the field are very, very systematic,” says Suard. “Then all the calculations are done on the cloud computing with our AI, combining all the different signals. It can be a lot of data; like 150 GB per acre. And everything's uploaded to the cloud. The AI basically processes all of that very, very fast and efficiently, then we have quality assurance by engineers. Then we export the map you need.”

UAS can be the platform in very limited conditions, but it is still cool that is an option. “Once you've got the basics down, once you switch to an operating manner in the field that's just crisscrossed with all the sensors, then you can put the sensors on a drone,” says Suard. “The first ones to do that was actually the mining industry, by the way. We didn’t invent that, but we just put way more sensors on the drones.”

Suard explained that sensing from drones must be done very close to the ground. “So then it's  an operational question. Like, if you can you fly a drone that low, why not just use a cart or trailer? It really depends on the site, conditions, environment etc., and the application. Of course, inside of cities, we will always use the cart to be closer to the ground and make sure we catch everything.”

Services, Not Hardware

Exodigo does not want to get into the hardware business; rather, they tap into the expertise of those who develop the best sensors. “We buy sensors from the best sources; we are not interested in selling hardware. Our vision in the future is to be able also to pass the tools to anyone who wants to scan. And then it's easier to do smaller projects everywhere, we will still offer the full service if someone wants it. But once we've reached a certain point where we trust the tools in the field to be very simple, no problems, no bugs, then we can pass the scanning to everyone—your students, your interns, your new crew members—because anyone can do it.”

Typically the multiple sensors are deployed on a cart, but under certain conditions, and depending on the application, some can be deployed on a UAS. Source: Exodigo

Presently, Exodigo sends their own teams and sensor arrays out to clients sites; they have teams in Europe, and both the east and west coasts of the U.S. “We buy the best sensors we can find,” adds Suard. In examples he showed me, they have a cart with GPR and other sensors, GNSS for positioning, etc., and some low-flying UAS examples.

Case Study

What really piqued my interest in this was a formal test performed, a pilot study, of underground utility mapping in a suburban setting in Santa Rosa California. The results were published in a white paper (Exodigo will send it to you). Exodigo did a crisscross pattern on a subject street with multiple sensors on a cart. 

The verification step was to send conventional locate teams to cover the same site. From the white paper: 

“The findings of the Utility Research “SUE” were marked using CGA Best Practices standard paint markings and flags inside the white lined work area. (All of the markings were by the subcontracted locator since the area is a private mobile home park). The locator used a combination of limited mapping, field research, surface features, electro-magnetic locators, and GPR. The markings appeared orderly and consistent to sound industry practices.”

The conclusion of the study noted that:

“The Exodigo findings were consistent with of the suspected utilities marked by the locator (which appear to have been primarily based on field interpretation and induced Electro-Magnetic Locating Equipment). In addition to all the accurate locates of the traditional locator, Exodigo cited additional lines and was able to eliminate inaccurate phantom lines. (The Locator’s mismarks were later suspected to be attributed to soil features associated with previous construction). There were no lines that Exodigo missed that were found by the traditional locator.”

In the pilot example, they found twice as many utilities as the conventional locator, avoiding potentially 5 hits from missed lines. Suard says that clients almost without exception find more lines than conventional locate methods.

A Model Moving Forward

I would expect to see mapping carts hitting the market in the near future that have multiple sensors, or package options—the hardware is the easy part. The heavy lifting is processing and analyzing these huge datasets and automating the analysis. It is likely that this may never be practical to do onboard, so services such as this example will become standard for underground mapping workflows.

Gavin Schrock is a licensed surveyor and geospatial technology writer based in the Pacific Northwest. He is also a consulting editor for GoGeomatics.ca

I love this analogy: “Let’s say you want to get a 3D picture of the inside of your body today; you have an MRI, CAT Scan, and Ultrasound,” says Jeremy Suard, Co-Founder & CEO of Exodigo. “But it’s 3 devices, 3 pieces of software, and potentially 3 different doctors that have to analyze them.”

This is the challenge faced when seeking to comprehensively map underground utilities. As noted in ASCE and UESI literature, the use of multiple sensors is the best practice for catching-it-all. Ground penetrating radar (GPR) and especially multi-channel GPR (MCGPR), time-domain electromagnetic induction (TDMI), magnetic (MAG) methods, sonic, ultrasonic, etc. I love my MCGPR unit, but have realistic expectations, especially in various soils type and moisture profile environments.

Mixing multiple sensors means having expertise in interpreting/analyzing each, and then you need to put it all together in a map. I don’t care how much MCGPR gets billed as #D underground mapping, it still takes a lot of experience and skill to “read the tea leaves”. But what if analysis with other sensors could be automated to corroborate and/or fill in what the other missed. I’ll make an analogy to the integrated positioning systems that are on mobile scanning systems and some UAS: an IMU is processed along with the precise GNSS—one can help catch outliers in the other.

Processing Together With AI

Exodigo has done this for multi-sensor underground utility mapping, for ad hoc systems mounted on carts, or for some limited applications, even a drone.  “It's better than doing all of them separately. And it removes the human interpretation factor in the field,” says Suard. “Because today, even though a lot of calculations are done in the field on your device, without broader context or corroborating data, it's a lot of human interpretation. You can lose a lot of data there. A lot of data, lots of room for decisions. So, you need to be a real pro at GPR, or whatever sensor.”  With staffing shortages across AEC industries presently, let alone finding an expert in each type of sensor, such an approach can be appealing.

An example of multi-sensor processing and analysis showing the locations, quality levels, and type of detection method used. Source: Exodigo

“So, the way we do it is that the measurements in the field are very, very systematic,” says Suard. “Then all the calculations are done on the cloud computing with our AI, combining all the different signals. It can be a lot of data; like 150 GB per acre. And everything's uploaded to the cloud. The AI basically processes all of that very, very fast and efficiently, then we have quality assurance by engineers. Then we export the map you need.”

UAS can be the platform in very limited conditions, but it is still cool that is an option. “Once you've got the basics down, once you switch to an operating manner in the field that's just crisscrossed with all the sensors, then you can put the sensors on a drone,” says Suard. “The first ones to do that was actually the mining industry, by the way. We didn’t invent that, but we just put way more sensors on the drones.”

Suard explained that sensing from drones must be done very close to the ground. “So then it's  an operational question. Like, if you can you fly a drone that low, why not just use a cart or trailer? It really depends on the site, conditions, environment etc., and the application. Of course, inside of cities, we will always use the cart to be closer to the ground and make sure we catch everything.”

Services, Not Hardware

Exodigo does not want to get into the hardware business; rather, they tap into the expertise of those who develop the best sensors. “We buy sensors from the best sources; we are not interested in selling hardware. Our vision in the future is to be able also to pass the tools to anyone who wants to scan. And then it's easier to do smaller projects everywhere, we will still offer the full service if someone wants it. But once we've reached a certain point where we trust the tools in the field to be very simple, no problems, no bugs, then we can pass the scanning to everyone—your students, your interns, your new crew members—because anyone can do it.”

Typically the multiple sensors are deployed on a cart, but under certain conditions, and depending on the application, some can be deployed on a UAS. Source: Exodigo

Presently, Exodigo sends their own teams and sensor arrays out to clients sites; they have teams in Europe, and both the east and west coasts of the U.S. “We buy the best sensors we can find,” adds Suard. In examples he showed me, they have a cart with GPR and other sensors, GNSS for positioning, etc., and some low-flying UAS examples.

Case Study

What really piqued my interest in this was a formal test performed, a pilot study, of underground utility mapping in a suburban setting in Santa Rosa California. The results were published in a white paper (Exodigo will send it to you). Exodigo did a crisscross pattern on a subject street with multiple sensors on a cart. 

The verification step was to send conventional locate teams to cover the same site. From the white paper: 

“The findings of the Utility Research “SUE” were marked using CGA Best Practices standard paint markings and flags inside the white lined work area. (All of the markings were by the subcontracted locator since the area is a private mobile home park). The locator used a combination of limited mapping, field research, surface features, electro-magnetic locators, and GPR. The markings appeared orderly and consistent to sound industry practices.”

The conclusion of the study noted that:

“The Exodigo findings were consistent with of the suspected utilities marked by the locator (which appear to have been primarily based on field interpretation and induced Electro-Magnetic Locating Equipment). In addition to all the accurate locates of the traditional locator, Exodigo cited additional lines and was able to eliminate inaccurate phantom lines. (The Locator’s mismarks were later suspected to be attributed to soil features associated with previous construction). There were no lines that Exodigo missed that were found by the traditional locator.”

In the pilot example, they found twice as many utilities as the conventional locator, avoiding potentially 5 hits from missed lines. Suard says that clients almost without exception find more lines than conventional locate methods.

A Model Moving Forward

I would expect to see mapping carts hitting the market in the near future that have multiple sensors, or package options—the hardware is the easy part. The heavy lifting is processing and analyzing these huge datasets and automating the analysis. It is likely that this may never be practical to do onboard, so services such as this example will become standard for underground mapping workflows.

Geoff Zeiss' Memory Lives on in Urban Infrastructure Mapping Fund | Carleton FutureFunder

For over 30 years, Geoff Zeiss devoted his life to the Geospatial industry and community all over the world.

It was a life focused on the utility and construction sectors, mapping underground utility infrastructure, BIM, CAD, Geospatial interoperability, machine learning, and substation design among other interests. Zeiss was responsible for multiple large and prosperous implementations of records management software and locator-aware enterprise designs.

Zeiss was a knowledgeable, busy, productive, and inspirational individual. He was an author, speaker, creator, leader, and so much more. Holding a Doctorate from McGill University and a Bachelor’s Degree from Cornell University, Zeiss gained experience working in various positions (President, Vice President, Director) for companies such as Tydac Technologies, VISION, DPA, and Autodesk.

Zeiss was an active advisor to National Underground Asset Registry, Minnesota Geospatial Advisory Council Emergency Preparedness Committee, and Singapore Digital Underground Initiative. He wrote articles that appeared in LIDAR Magazine, was Editor for Energy and Building with Geospatial Media (2014-2016) and was involved in founding the Open Sources Geospatial Foundation from 2005-2006.

Throughout his life, Zeiss was a man dedicated to promoting the awareness and evolution of geospatial data, underground utilities, and technology. He often spoke about the importance of mapping underground utilities and infrastructure issues. Zeiss supported open standards as a director of the Open Geospatial Consortium.

With enthusiasm, Zeiss often spoke at events and forums all over the world. He made time for participating in, as well as being a founder and Conference Chair of, SUMSF. Throughout his life, Zeiss also participated in the Geospatial World Forum, Where 2.0, Location Intelligence, MundoGeo Connect, India Geospatial Forum, Middle East Spatial Geospatial Forum, Asia Geospatial Forum, and GITA events.

Zeiss was recognized for his dedication and efforts. During the years 2007 through 2009, he received Speaker Excellence Awards at GITA. He also received one of ten global technology awards from Oracle Corporation for ‘technical innovation and leadership in the use of Oracle’ in 2004. Zeiss continued his work, and at the Geospatial World Forum in 2014, he was yet again recognized for his efforts to increase the awareness of geospatial data and utility awareness. Zeiss received the Geospatial Ambassador Award.

Besides speaking, Zeiss created Between the Poles, a thought leadership blog where he advocated on issues of importance to the community. The blog addresses infrastructure issues and updates, and continues today under the care of GoGeomatics Canada, keeping Zeiss’ life mission of awareness and information moving forward.

Sadly, Geoff Zeiss passed away Sept 14, 2022. 

The Geoff Zeiss Urban Infrastructure Mapping Fund builds on decades of innovation by Dr. Zeiss in developing effective and
sustainable urban infrastructure through research and development supporting urban information systems.  The goal will be to focus on the democratization of utility mapping, creation of infrastructure digital twins, and modernization of data workflows in global construction and infrastructure management. The potential for new technologies and methods to facilitate crowd-sourced mapping and more equitable data access will end the monopoly of publicly funded private assets and improve both efficiency and safety of the construction industry. Depending on the funds raised, several exciting possibilities exist, from graduate student scholarships to post-doctoral fellowships to longer-term and endowed research positions.

Carleton University is an ideal place to house such a research fund. The Department of Geography and Environmental Studies excels at integrative scholarship, bringing together expertise in geomatics, physical and human geography, and environmental studies, while working closely with the researchers from engineering, architecture, industrial design, public policy, law, communications, and urban planning. This multi-sector, interdisciplinary approach is necessary to innovate across the complex socio-technical domain of urban information systems.

Please consider marking Geoff’s legacy with a contribution in his honour to advance this important research.

 

Author: Jon Murphy

Jonathan Murphy is the founder and Managing Director of GoGeomatics Canada, the most popular communications hub for the Canadian geomatics sector. Jon is the founder and chair of GeoIgnite - Canada's national geospatial leadership conference.

Jon has created Canada’s largest professional geospatial network, with the goal of strengthening and empowering our geospatial ecosystem. Jon has had broad range of global experience in marketing and sales for geospatial businesses across a number of sectors and regions globally. He holds a bachelor's degree in Archaeology from the University of Calgary and advanced diplomas in GIS and geomatics from COGS. Jon is a Fellow of the Royal Canadian Geographical Society. In 2020 he joined the ISO/TC 211 Geographic information/Geomatics Technical Committee responsible for the ISO geographic information series of standards.

 

 

A revolutionary source of accurate underground utility location data for SUE and other surveys

Recently jurisdictions such as Colorado have mandated SUE surveys on all public civil engineering projects. Return on investment (ROI) studies of subsurface engineering utility engineering (SUE) surveys applied to highway construction projects conducted since the late 1990s have consistently revealed a large return-on-investment from conducting SUE surveys as part of highway construction projects.  Studies have found that the major benefits of SUE surveys are improved construction efficiency, greater probability of on-time and on-budget project completions, fewer and shorter utility service outages and less traffic disruption and delays for the motoring public. The objective of a SUE survey is to identify and locate all underground infrastructure in the area of the proposed project to ASCE 38-02 Quality Level B or better.

SUE surveys currently rely on on-site detection technologies such as electromagnetic (EM), ground penetrating radar (GPR), and other methods to remotely detect underground utilities.  Potholing is also routinely conducted as part of a SUE survey to confirm the identification and location of utilities that have been detected by remote sensing technologies. Using remote detection methods and potholing requires "boots on the pavement" which raises safety as a major concern when conducting a SUE survey.  EM devices are typically handheld wands and GPR is usually mounted on pushcarts. Data acquisition occurs at a walking pace.  Furthermore the reliable interpretation of GPR scans requires an experienced geotechnical practitioner. Together these factors make current technology used for SUE surveys slow, laborious processes with serious safety issues.

But a revolutionary technology innovation has been developed by 4M-Analytics that could provide SUE practitioners with a source of data without the major disadvantages of traditional remote detection methods.  It is safer because it does not require boots on the pavement, it provides accurate locations reliably, can be conducted without traffic and other disruptions, and can be generated much more rapidly than the traditional on-site remote detection methods and is available in most rural areas in the world. Intended for rural linear civil engineering projects in such as pipelines and transportation routes 4M-Analytics' technology utilizes high-resolution satellite and aerial imagery, computer vision algorithms, and training sets for machine learning developed for different biomes, including forests, grasslands, and deserts. The deliverable to the SUE practitioner is a complete, accurate and up-to-date utility conflict map showing the location of all underground utilities and pipelines intersecting a proposed civil engineering project.

SUE is rapidly growing in importance for civil engineering projects. 4M-Analytics' revolutionary approach to locating underground infrastructure, which utilizes the huge volume of aerial and satellite earth observation imagery that has been built up over the past decades together with machine learning and other AI algorithms, can provide an important source of rapidly produced, safely acquired, and reliable underground location data for SUE practitioners.

 

Using a drone to capture high precision underground utility location data up to 15 meters deep

For a long time it has been a dream of underground utility locators to deploy a drone based system for mapping underground utility infrastructure. The advantages of such a system are speed of execution - drone-based system can gather field data much quicker than traditional land-based technology. .The second advantage is safety - no boots on the pavement allows the field operator to remain safe at all times, regardless of terrain conditions. The third advantage is precision providing cm accuracy at a depth of 15 meters.

With respect to spatial positioning we support GNSS and RTK. But we can have either a fixed reference point on the ground upon which we work or have a satellite correction of our positioning. And this is how we're able to have such a high level of accuracy in our mapping.

A most important general application of Skipper NDT technology is, as opposed to traditional methods, soil conditions have no impact on our measurements, meaning that if the soil is humid, or if you have different soil types, it will have no impact on our measurements. And the proof is that we can even see through water. A specialized Skipper NDT application is river crossings, being able to detect utilities under water is a rare capability.

A third application is an out-of-straightness assessment. The Skipper NDT technology has proven to be as precise as an invasive pipeline tool to measure the mechanical deformation of pipelines.

A caveat is that we are more suited for rural areas than urban due to the regulation for drone fight, but also due to the EM noise that can be prevalent in those areas, which is very important. We do require the standard permits to fly drones in different locations which can vary from country to country. We're more of a rural-based technology applicable for both energy and utility segments. Electromagnetic noise is one of the competitive advantages that we've developed through time.  We're able to filter those EM noises out of the signal that is required in order to have the cleanest magnetic environment. We filter interferences that could be created due to high voltage lines or metallic objects in the proximity of the pipeline.

Two main operators have validated this application, one being GRTgaz, the incumbent French gas operator, and the other one being Veolia, the large utility company. We've done operations in Europe, US, Asia and Africa. We have collaborated with prime operators such as TotalEnergies, GRTgaz, Patronus, US-based operators such as Kinder-Morgan, as well as operators in the field of utilities, such as Veolia. It is particularly interesting for markets in Europe, where there is a regulation that obliges all operators, energy or utility, to map their entire infrastructure with the inch precision by 2032.

Hardware payload

The Skipper NDT is a four-kilogram payload enabling a high-precision pipeline mapping. It is composed of several sensors, magnetic, but also centimetric GPS and IMU, sensors that calculate the distance between the drone and the ground to provide the depth of cover, and an electronic card, which interpolates all this data. The result is an acquisition pace which is three times higher than traditional methods. The device can collect data at up to 12 kilometers per hour with a data density that is up to 1,000 times more than traditional tools with a point every centimetre, if required. In addition we perform magnetometer compensation that allow us to increase by 25 times the native resolution of our magnetometers to have very high-precision data. We can detect pipelines that are metallic, so no PVC, but we can also detect active cables, cables in which a current is being transmitted. We have been actually tested on pipes going from a diameter of 80 millimetres to 1.2 meters, meaning we can go from very small to the largest pipeline seamlessly

We are drone agnostic, meaning that the drone we are showing is the DJIM 600. But we can integrate our payload on any tool being able to carry our payload. The way we market this product is either directly with operators which know us now or to service providers that might have already have a drone able to carry out payload.

In terms of software, we apply several algorithms to the data to have the cleanest magnetic information. These include field data level control, which compensate the movement of the acquisition vector that could cause potential false positive, or at least complicate the analysis of the data. But we also we filter EM interferences that could be created due to high voltage lines or metallic objects in the proximity of the pipeline. We also have several algorithms patented, which will process this data in order to provide the operator with the cleanest and most precise information.

Operational process

Here, we describe the operational process of data gathering through the Skipper NDT technology. We've simplified the process so that operator in the field will input two points and will then deploy the drone, which will go back and forth over the area. The objective is to hover over a zone with a width of 10 meters around the pipeline location. to collect data. We will then apply different magnetic acquisition filters in order to provide a magnetic map from which we will derive a number of points that will be provided to the operator within the format that suits best their system.  In this example a seven-inch diameter,gas pipeline was measured over a distance of 207 feet over 32. The number of profiles that were done by the drone was five to cover the entire magnetic anomaly. And the acquisition time for this coverage was of six minutes. The results of this acquisition were compared against an open ditch measurement, and the two tables describe basically the performance that was obtained with the Skipper technology. We can see that laterally, we have an average precision of 5.2 inches and a standard deviation of four inches. And horizontally, meaning depth of cover, we have an average precision of 1.6 inch with a standard deviation of 2.1 inch. We see here that the data is fairly constrained, meaning that there is a high degree of certitude on the data that is provided by the Skipper NDT algorithms. This shows why it is important that we gather the entire magnetic signal. We can see that on one of the pipelines that was measured, two different magnetic interferences were gathered, both in the low frequency map and a high frequency map. And this was highlighted by our algorithms. Those kind of interferences are characteristic of a metallic object. Our analysts that reviewed the data suggested that there might be an abandoned line crossing the existing line. After further investigation, it was confirmed that an abandoned line was actually crossing the new line that was built. This constitute a serious hazard potential, and hence the data was then integrated into the global information system of the client. This is why we insist on gathering a large magnetic map that allows us to gather not only the magnetic signal of the pipeline, but also of its environment.

This post is based on Luigi Kassir's (Skipper NDT) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

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.

Product-as-a-Service:a paradigm shift in low cost access to advanced underground mapping technology

Gyro or inertial mapping provides an effective technical solution to the challenge of accurately mapping underground utility infrastructure. Gyro mapping solutions are available for any type of underground utility including water, gas, electric power, communications, ducts, and wastewater. Using this technology it is possible to map networks of pipes or ducts with diameters ranging from 29-34 mm to 90-1500 mm for distances of up to 2 km. Solutions are being offered globally by quite a large number of service providers.

Product-as-a-Service (PaaS)

Gyro mapping is perceived to be expensive because it is currently offered in a very inefficient manner with high mobilization and de-mobilization costs. Whereas the reality is that that if you provide the service efficiently gyro mapping has a price advantage over other technologies. That is why we're introducing the Product-as-a-Service (PaaS) concept which represents a true paradigm shift in how gyro mapping is offered.  

Erratic per unit pricing, which is actually the norm in the mapping industry, is a problem that inhibits new advanced mapping technologies like gyro mapping technology from competing more effectively with traditional underground mapping methods such as GNSS. The way pricing currently works is that service providers ask a fixed rate for a day's work. The fixed cost covers mobilization, de-mobilization, the crew, the equipment, the planning, and the actual mapping. But the actual amount of mapping conducted may vary from a very small portion of the daily rate to virtually all of it. As a result a crew is often inefficiently deployed because the volume of mapping is fairly low, but it still takes a day. As an example, a service provider whose standard rate is $3000 per day is requested to come in and do a 300 foot mapping. That translates into an effective of cost of $10 per foot, which is very expensive. On the other hand if they had mapped 1,500 feet that day the average cost would have been $2 per foot.

 

Enabling the efficient delivery of gyro mapping services requires two things; reducing mobilization and demobilization rates and a pricing model that reflects the amount of subsurface mapping actually conducted.

The solution is to bring the technology closer to the job sites to reduce mobilization and de-mobilization costs and to adopt a pricing model that more accurately reflects the amount of gyro mapping that is actually performed.

The foundation of Product-as-a-Service (PaaS), is that Reduct makes its equipment available to the service provider free of charge.  On its side the service provider commits to conducting a certain amount of underground mapping using gyro technology per year. The service provider either conducts the underground mapping work itself or subcontracts a contractor, engineering firm, or surveyor to conduct the pre-construction survey work. In either case the service provider provides the Reduct gyro mapping equipment used in mapping the location of underground utilities. 

Of course this will require that a lot more service provider people (or your subcontractor's people) be trained in gyro mapping to perform this work. But you no longer need to travel half way around the world to acquire Reduct training. Reduct have set up the Reduct Academy where you can find video-enabled training packages for all of our tools, how to assemble them, operate them, process the data, and generate the 3D models.

Transparent competitive pricing

PaaS makes pricing transparent and enables gyro mapping to be offered competitively to traditional technologies. Service providers can provide prices to their customers based on the rates that Reduct charges for the PaaS service. PaaS rates are simply a balance between the value of the equipment that we provide and the feet of subsurface mapping that is committed by the service provider.

A key benefit of the Product-as-a-Service concept is that it provides for complete flexibility in your choice of equipment. PaaS allows you to select, exchange or return technology depending on your current requirements. Because the technology is provided free of charge you have complete flexibility in choosing the equipment you want to use.  Reduct hardware solutions are available right now for pretty much every type of utility including water, gas, electric power, communications, ducts, and wastewater and for a wide range of pipe and duct diameters ranging from 29-34 mm to 90-1500 mm for distances of up to 2 km.  The pipe or duct can be any material and any shape; round, rectangular or X -shaped. 

Here's some practical examples of the advantages of full flexibility. We supply you with an ABM-30 for an inch and a quarter data duct, and an ABM-90 or a DR-4 for doing larger diameter pipes - anything up to 500 millimetres. Now, if you commit to mapping a hundred thousand feet with those two units on an annual basis, then you will be charged 52 cents per foot for mapping done using this equipment. That is a flat rate, it doesn't matter which of the two units you use. There is zero investment risk because tools are exchanged and replaced as they become obsolete.

Now if your projects change and your latest job requires that you map a 210,000 foot pipe, you find you need to upgrade your ABM-90 to a DR-4. Your rate per foot drops to 27 cents because on the one hand, you have a slightly more expensive tool, but you have a lot more volume. So your rate goes down to 27 cents.

For service provider's people in the field the benefit of the PaaS model is full hardware flexibility and transparent, competitive pricing.

Cloud-based services

We've also set up cloud-based services, where your experts can monitor everything that happens to all the tools that are within your portfolio without needing to be on site. With that online service, you can see performance statistics, quality statistics of the tool such as spread and length scaling, that give a very good indication of how well the tool was pulled through the pipe and how accurate the result is likely to be.

You can centralize all the activities that are happening, reporting, quality control and other activities. You can monitor the quality of an individual tool or of an individual operator. For example, if an operator is performing less efficiently than another operator with the same equipment or if you detect data quality issues with a particular operator, you may consider giving some remedial training or monitoring specific operators more closely.

Reduct community

We have created a community of Reduct users. Within that community, we have pipeline owners, service providers, and service partners. Service partners are people and organizations with relationships to Reduct.  For example, they are a local place where you can go to exchange your tools or have them re calibrated. So every region will have service partners.The community will be a place where you can basically interact with your partners or your service providers.  If, for example, you have a very interesting reporting template for a certain customer, which is very specific, and you know other people could benefit from that, you can offer it through the community.

This sums up the new concept we call Product-as-a-Service, or PaaS that provides a way to bring gyro mapping into the mainstream technologies, just like any of the other ones.

Implications for introducing other advanced technologies

We believe that this approach has implications for subsurface mapping in general, because it provides a way for other new advanced subsurface mapping technologies to be offered into the market competitively with traditional mapping technologies.

 

This post is based on Otto Ballintijn's (Reduct) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

 

Real time reality capture during construction key to digitizing the entire construction workflow

Utility construction is booming globally. Almost half a trillion annual investment in capital across all the main verticals, electric, natural gas and telecom has resulted in capital investment with construction with as high as 4% growth. On top of this in the U.S. there is the recent $1.2 trillion bipartisan infrastructure bill.

Utilities are facing business drivers that are motivating them to improve the productivity of their business processes and improve data quality especially the locational accuracy of assets to provide a near real-time digital twin of their assets.  These drivers include an aging work force, the replacement of older workers by younger less experienced workers, a reliable near real-time GIS as the foundation for a smart grid, and a rapidly evolving utility business model that changes utility priorities.

Current legacy workflows

The current construction workflow for utilities is a highly inefficient, manual process a lot of which hasn't changed in 40 years. Actual construction, the middle and key part of this workflow, has remained a paper based process that has remained mostly unchanged for over 40 years now. Construction crews typically use paper job packets to locate the job site, and document any as-built changes to the job. They use the paper job packets, work order sketch, or any other piece of paper in the folder to record any required attribute information about the assets being installed. As construction activities increase and newer downstream analysis systems are brought online, such as an outage management system (OMS) system or an advanced distribution management system (ADMS), the data collection requirements grow, adding additional burdens on the construction crews. These crews work in difficult of conditions on difficult, time consuming tasks, but are still asked to record all of the required information on paper. The paperwork is prone to delays in processing at each stage of the workflow cycle. This antiquated paper-based collection of work order completion data results in mounds of paperwork from the field with all the attendant issues of hand written notes. The GIS analysts receiving these paper work packets have to try to sift out reliable data from these paper records. The result is that the data that actually gets into the GIS often is missing, inaccurate, or out of date.  Backlogs of months are not uncommon.

Real time data collection enables digitizing the entire construction workflow

Locusview has developed a digital construction management platform that digitizes the entire workflow process. It is designed with the construction crews needs in mind, matches their workflows, and simplifies their data collection tasks. Work order data is collected via a mobile application using smart forms, high-accuracy GPS, and barcode scanners. Data can be immediately accessed by the back office staff, in real time. This two-way communication can be used to resolve design changes that require due to field conditions, thus reducing invoice and billing delays.

Locusview Mobile integrated with high-accuracy GNSS receivers and barcode scanners eliminates manual data entry. Data forms are displayed based on pre-configured industry-specific data model rules using complex conditionality alongside smart forms.  Simultaneously back office managers use Locusview Web to gain real time field visibility, enabling project progress tracking, real time validations, contractor auditing, and smart alerts, ensuring compliance with safety, regulatory requirements and work processes since all stakeholders, field crews, contractors, supervisors, project managers, and inspectors are collaborating on the same job using a single platform. This fully digitized process reduces the time and effort typically required of construction crews to document their field activities, expedites and standardizes field data collection, and improves data integrity creating the foundation for a high fidelity digital twin.

A key metric we track is the as-built backlog. That's the time it takes to enter as-built data into GIS after completion of construction. That's only one part of closeout, but it's an important part.  Our customers are reporting a 70% reduction in their as-built backlogs. They are also reporting 20 % less time spent in the field. Furthermore they are seeing 50% cost savings in data collection because they're enabling field crews, people that are already out in the field, to collect this high quality data with high-accuracy GNSS devices and barcode scanners avoiding the necessity of hiring outside survey crews. This translates into better capex efficiency, putting more assets in the ground per dollar of CAPEX spend and Increased customer service, something very important for competitive industries as telecom.

This simplified data collection process is designed to fully integrate with downstream utility applications as well. The job closeout process using Locusview has been reduced from 8 months to 3 days. As a concrete example a very manual piece of the closeout process, the material reconciliation piece, has been reduced from 7 hours to 12 minutes.

Real time data collection benefits all stakeholders

Many stakeholders are involved in the the complete construction work order cycle; contractors built the job, GIS analysts updated their maps based on as-builts submitted by the construction crew, operations imports the GIS data and operates their Outage Management System (OMS), planning and engineering use GIS and other asset information to plan for future growth. The work management system uses the data to initiate, manage and closeout the work order process for each job.

In addition there are a number of "forgotten" user personas that use and need accurate data from the field. The finance and corporate accounting groups rely on data in the system of record to be highly accurate and reflect the most current state of the utilities assets. Utilities go to the bond and stock markets on a regular basis to borrow funds to build and maintain their utility systems. And potential lenders or investors rely on the accurate valuation of the company. Interest rates charged by those investors correspond directly to the health of the utility and impact the cost of funds. If the work order update processes are delayed by 2 to 6 months, then the system of record is misleading and the lenders or investors may question the financial health of the utility. In addition, delayed closeout of work in the work management system will result in increased cost of funds charges to the work order via extended construction work in progress (CWIP) and allowance for funds used during construction (AFUDC) charges that are on all of the open work orders. CWIP and AFUDC is a common practice for accounting for the cost of funds in the United States and it's a concept that's gaining momentum here in Canada as well. Also many local governments apply property tax assessments on utilities for the utility assets within their municipality. If the system of record is not up to date, then the property tax assessments will also be incorrect. Contract and management organizations also rely on the accurate and timely reporting of work completed by contractor crews, as well as, closing out of the invoicing process. Delays in documenting completed work in the field will result in delayed invoicing by the contractors for the services that they've provided to the utility.

Conclusion

The key is real-time reality capture, that gets you the benefit of driving a digital twin into utilities by capturing the most accurate, high-fidelity data at the most opportune time during construction, not afterwards where you're mapping a scar. Furthermore it's a unified solution standardizing construction data collection real time across utility and contractor crews.

 

This post is based on Danny Petrecca's and Robert Schultz's (Locusview) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

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).

Eliminating the as-built backlog by automating field utility data capture

The as-built update backlog is the time it takes for as-builts submitted from the field after completion of construction for newly installed network equipment to get entered into your utility GIS.  It is generally measured in months which is appalling today when we have real-time or near real-time data about so many aspects of the real world from traffic to weather.

Field sourcing

The key to solving this problem is to enable crowdsourcing of updates from the people who actually do the construction, the field workers, so construction crews can make those updates where and when the work happens. In order to do that, we really need data capture tools that are much faster and simpler, in other words, more automated than today's solutions.

There are a number of enabling technologies that can change the way that we approach this problem; computer vision, machine learning, augmented reality, voice interaction, and reality capture can all play a role. A lot of these things are being driven by other industries outside the geospatial technology industry. Part of the challenge in bringing together a solution for the as-builting process is looking broadly at these different types of technologies and where they're applied and bringing them together into a solution for utilities. You need to enable the people doing the construction to capture accurate location and attribute data quickly and efficiently. Anyone should be able to use the system with minimal or no training and it should use inexpensive commodity equipment.

Automating feature recognition

As an example, we're in a new subdivision construction site and we're going to do some as-built data capture of some electric equipment using an iPad. We point our mobile device at a piece of utility equipment and the application automates the process of detecting what the equipment is. The user just taps the screen to add the feature, an electrical pedestal. Next, we point our mobile device at the neighbouring electric meter. And again, it's automatically recognized and we tap to confirm that we want to add it.  We can then scan the meter to add more detailed attributes according to rules that we've set up.

Now we want to add a cable. If the trench was still open so the cable was visible, we could automatically recognize it like we did with the other features, but since it's hidden, we select it from a list and then just tap to add points along the path of the cable. As we walk, you can see the cumulative distance of the cable path being shown at the top. Lastly, we'll add an electric cabinet in the same way and scan its attributes, including the transformer number and manufacturer on the front side. Then the KVA from the yellow 50 on the side. We zoom in more closely to get additional attributes like the serial number, work order number from the smaller label.

Once we have completed scanning the various objects, the data gets pushed automatically to the GIS with no user intervention needed. We can see the features that we just captured on a map with their attributes plus photos. The newly captured data about the equipment is immediately available in any GIS client. For example, we can see the newly captured equipment data on a handheld mobile device or it can be viewed on any web browser. The data can then be pushed on from the GIS to real-time systems like an ADMs outage management system or network management system.

The app is able to automatically recognize features and attributes using machine learning based image recognition, to tell what type of feature it is, read barcodes and QR codes, and use text recognition to capture detailed attributes. The additional attribute information also reinforces the feature recognition in some cases. YYou can also use voice recognition as a backup if there's a scenario where you have to correct something that was misrecognized or something that can't be identified correctly.

Lastly is an example of capturing very detailed attributes quickly. We captured 22 attributes in just over a second from an electric meter.

Location accuracy

Location accuracy is particularly important for underground assets. AR gives much greater relative accuracy than consumer GPS and phones do. It measures accurate distances and angles in 3D for recording of relative asset layout. In a range of tests we found AR is accurate to about 1%. As a simple example. we measured a 10 foot distance next to a tape measure. We've added a virtual pipe and you can see the measured distance in the augmented reality app is 10 inches and it's within an inch of the 10 foot mark or within about 1%. Tests of up to about 100 feet have found the same 1% accuracy. AR provides much better accuracy than you could get with anything except the very highest precision GPS.

Visual positioning system

Apple's GeoAnchors is their implementation of the AR Cloud. The idea is that as you're moving your mobile phone around among 3D objects captured in a street view scene, it uses the camera view to match what the camera is seeing against the highly accurate street view type imagery, which Apple calls look around imagery and that refines the position.

Conclusion

Field sourcing of data is key.. You need to enable the people doing the construction to capture the data, and these new technologies really open up some very exciting new ways that we can provide systems to the construction workers to enable them to capture accurate location data. We foresee that visual positioning systems are very likely going to supersede high precision GPS in a lot of scenarios.

This post is based on Peter Batty's (SSP Innovations) talk at the Subsurface Utility Mapping Strategy Forum (SUMSF).