Between the Poles

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.

 

A 3D base map for the globe

At the GeoIgnite virtual event Ryan Hamilton, Senior Manager 3D Products, at Maxar described a remarkable project Globe in 3D to create a 3D model for the earth's entire landmass using the petabytes of 50 cm imagery that Digital Globe (and now Maxar) has collected and is collecting from its constellation of satellites.  Multiview stereoscopy technology enables Maxar to create a 3D model for every point (pixel) on earth by combining multiple views of the same point taken at different times and with different camera locations.  The result is a textured 3D model that has better than 3 meter absolute positional accuracy, avoids the blind spots that result from a single camera view, and averages out errors that are often found in individual images.

In 2015 Digital Globe and Saab formed a partnership which created Vricon to integrate 3D technology with Digital Globe's imagery to enable the creation of an accurately georeferenced 3D model of the earth's landmass without using control points. (Maxar completed the acquisition of Vricon last year.) The process is compute intensive and is still underway. The result will be a 3D TIN model with textures on all sides for the entire landmass of the planet. The Globe in 3D is available in Vricon format, Esri i3S or Cesium 3D Tiles.

A number of derivative products are also available for specific use cases including digital terrain and digital surface models, textured buildings, classification layers identifying different land uses and vegetation, point clouds, 2D ortho images, 3D vector representations of buildings, vegetation and transportation infrastructure with coverage in dense urban, suburban and rural areas. A limitation resulting from the algorithm that combines images captured at different times is that it is not possible to assign a specific date to  the 3D model at any location. But Maxar does provide tools to enable change detection over longer periods of time.

The 3D model can be used for many use cases including estimating cut and fill for a road, railway or other linear structure, estimating tree height for vegetation management, planning 5G telecom networks and others.  But perhaps it's biggest achievement could be to provide a shared 3D base map for the entire planet. To assist in realizing this vision Maxar provides precision 3D registration (P3DR) tools to allow other data to be corrected (conflated) to the Maxar base map. P3DR is a stand-alone software solution that automatically georegisters imagery from any source to Maxar's Globe in 3D base map.

Is legacy GIS failing network infrastructure operating companies ?

I thought it would be worthwhile to include here an expanded version of my contribution to a webinar that IQGeo presented last week with a provocative title that I think goes right to the nub of the challenge that telecom and utility companies are facing in developing digital twins of their network assets.  In this new world there is an exponentially increasing volume of real-time data and less and less time to conduct analyses and make decisions.  Telecoms and utilities and finding that their legacy GISs are simply not up to the task.  The webinar aimed to shed some light on the problems that telecoms and utilities are facing with their legacy GISs.  The webinar speakers were Linda Stevens, Managing Partner at 51by1, Peter Batty, Chief Technology Officer at IQGeo, and myself. It was moderated by Steve Tongish of IQGeo.  The full webinar is available on demand here.

Geoff Zeiss opening comments

Common symptoms of the failure of legacy GIS include as-built backlogs stretching into months, inaccurate as-builts submitted by contractors, limited updates from the field, frequent underground infrastructure damage during construction, inability to connect customers to transformers or feeders, and repeats (multiple truck rolls because of inaccurate information about field assets).

As-built backlogs are the result of a construction information flow involving different organizations with different tools.  Engineering uses CAD, construction relies on paper drawings, and records management manages data in a GIS.  The major factor contributing to as-built backlogs is the decades old lack of interoperability between CAD and GIS. The result is that the GIS is inaccurate and permanently out-of-date.

During the webinar there was a poll Poll asking webinar participants how long is their average as-built update backlog time?
8 % Less than 1 week
8 % Less than 1 month
21% Less than 3 months
25% More than 3 months
38% Don’t know or not relevant

It is indicative of the problem that of those from utilities or telecoms only a quarter had a backlog of less than a week.  (There were a significant number of people participating in session who were vendors or service organizations for whom the question did not apply hence the 38% response to the last option.)

Another problem relating to as-builts is lack of quality control. Construction contractors often submit as-designeds rather than what was actually constructed.  Contractors may also red-line as-builts to indicate the difference between the design and what was constructed, but these are often sketches rather than accurately surveyed data.  The result is that sometimes it is uncertain on which side of a street a cable or pipe has been installed or whether it was actually installed on private property instead of on the public right-of-way.

If there was an effective feedback mechanism these problems might be ameliorated over time, but getting updates from the field into the GIS in a timely fashion is a challenge for many utilities and telecoms.  I have encountered telecoms and utilities where updates from field staff did not get into the GIS at all or were seriously backlogged. Not seeing updates reflected in the GIS in a timely manner discourages field staff from reporting changes and errors.  The result is that over time the quality of GIS data degrades.

Inaccurate GIS data is responsible for 400,000 incidents of underground infrastructure damage every year in the U.S.  This represents a serious risk for workers and the public. Just a week ago there was an explosion in Murrieta, Los Angeles which left one person dead and fifteen injured.  In the last few months there have been explosions in downtown San Francisco, Durham NC, and I could go on.  It is also a multi billion dollar drag on the economy and is responsible for many construction projects running over budget and over schedule.

Missing or incomplete connectivity linking customers to transformers and feeders directly affects outage duration.  It is not uncommon for a utility not to know the correct connectivity for 15-20 % of its customers ( I would add that knowing the phase for each customer is often a bigger problem).  Not being able to identify a damaged upstream device when a customer reports an outage or being able to identify which customers are affected by a failed device directly impacts outage duration and restoration (SAIDI and CAIDI).

Inaccurate GIS data is also responsible for "repeats", where more than one crew trip is required to maintain a piece of equipment or fix a problem.  Typically it involves one trip to determine where an asset is and its details and another to return with the required equipment to resolve the problem.  This also impacts outage duration statistics.

Other symptoms include redundant data where the same information is maintained simultaneously in different GISs by different groups within a utility and difficulty in reporting accurate situation assessments of network assets to management and regulators.

Question: - Utilities devote huge resources to maintaining redundant geospatial data in different systems, much of which is inaccurate and out of date. This unreliable GIS data directly affects outage statistics, field staff efficiency, the reliability of reporting to regulators, and the ability of management to get an accurate perspective on operations. Why has this situation persisted for decades?

There are several reasons for this problem persisting as long as it has.  Among the top technical reasons is the incompatibility between CAD and GIS data. An important contributing factor is that multiple organizations and departments are involved in the workflow.  A fundamental problem is procurement practices that don't treat data as important as the physical assets themselves. An enlightened procurement policy can resolve this problem. I worked with a large Brazilian telecom that enforced a policy that contractors were only paid when as-builts were in the GIS and verified.  Another fundamental problem is that many regulators have not recognized the value of accurate, up-to-date GIS data.  I only know of a few that recognized how important this is for utility operations.  For example, about a decade ago ANEEL, the electric power regulator in Brazil, imposed a rule that all utility GISs had to be 95% accurate or better.  This was enforced by requiring frequent audits of utility GISs with penalties imposed if 95% was achieved.

Another important reason that this problem has persisted is that in some countries including the U.S. and the U.K. there is a liability structure that invariably assigns responsibility to the construction contractor for damage to underground infrastructure during construction.  This provides little motivation for telecom and utility network operators to improve the reliability of their GIS data.  In some countries such as France liability is shared between the telecom or utility operator and the contractor.  For example, if a contractor hits a utility facility that is shown accurately on utility GIS maps, it is the contractor's liability.  Conversely if a contractor hits a utility facility that placed incorrectly on utility GIS maps, it becomes the utility's liability.

Question: What is the impact of reality capture on how we create and maintain geospatial data? Can capture of network connectivity be fully automated ?

I have seen estimates of telecom and utility GIS accuracy of 70-80 % or even lower.  With recent advances in reality capture a question that utilities are asking themselves is whether it is more cost effective to conduct a complete reinventory of its network assets, to recapture the location and attributes of its field equipment, or to try to correct the existing GIS data.  Recently a very interesting pilot was carried out by Duquesne Light in western Pennsylvania.  It compared the network model derived from a standard walking inventory with a model derived from LiDAR/photo data captured with a truck and post-processed in the office.  It found that the model based on LiDAR scans was just as reliable as the walking inventory and much safer.  In addition the LiDAR scan provided significant side benefits. For example, It could be used for a joint-use audit. The LiDAR data also allowed clearances between poles and cables to buildings and structures to be accurately measured to ensure regulatory clearances and to verify third party attachment heights. 

In a related development machine learning is being applied to automatically recognize different types of telecom and utility equipment in LiDAR and photo imagery including high resolution satellite imagery and to automate inspections for maintenance issues such as corrosion, vegetation encroachment, and other issues. To support this application of AI, EPRI is collecting training data sets for utility applications of machine learning.  A startup SiteSee claims that it can inspect telecommunications towers with a UAV and automatically analyze the resulting imagery for problems with antennas. I don't think that we are quite at the stage of network connectivity being routinely determined using these techniques, but I suspect that the day is fast approaching when this will be possible.

Question: Poor GIS data quality for underground assets within the utility industry can have direct and very serious consequences in injuries and lost lives, not to mention the huge financial impact in damages and fines [400 lives, thousands of injuries and a $50 billion drag on the U.S. economy arising from 400,000 cases of utility damage every year]. Can new tools for underground asset capture and the sharing of this data begin to mitigate these risks?

There are two major challenges in improving the reliability of our maps of underground utilities, remote sensing technology to detect them without excavation and sharing the information that is captured everyday about utilities exposed during construction excavation.  In the last few years there have been important advances in both areas.

One of the most versatile technologies for detecting underground utilities is ground penetrating radar (GPR).  It can detect underground objects that electromagnetic techniques can't and it is better at estimating depth than other technologies.  This makes it possible to generate 3D maps of underground utilities from GPR scans.  However, until now it has required a trained geotechnologist to interpret the radar scans. GPR arrays have typically been mounted on a pushcart making scans slow to acquire and hazardous for the operator.  In the last year or two new technologies have been developed that allow GPR scans to be acquired much faster using a vehicle.  With this approach there are no boots on the pavement making it much safer for the operator.  It is also much faster.  The most recent technology advance makes it possible to capture GPR scans using a vehicle travelling at up to 135 km/hour. Another advance is combining a GPR array with a LiDAR scanner on the same vehicle to capture below- and above-ground scans simultaneously.  A major advance is new GPR software processes multiple GPR scans to present a view of the underground that enables non-geotechnical professionals to interpret GPR scans.  These are very important advances in the detection technology and in democratizing its use.

In North America every state and most provinces have a government mandated one-call centre which requires anyone planning to excavate to call 811.  It requires all utilities and telecoms with services in the area of the excavation to paint the ground indicating the probable location of their cables and pipes. In the U.S. the locate industry is estimated to amount to billions of dollars annually.  But in general this information is not shared and the same underground assets may be located over and over again.  Recently there are initiatives at the city, regional, and national levels to share information about the underground.  The most recent is the National Underground Asset Registry (NUAR) in the U.K., supported by the Geospatial Commission, that is currently running two pilots in the Northeast of England and in central London.  In Belgium the KLIP initiative which has been running for a number of years was motivated by the 2003 gas explosion which caused multiple fatalities.  In the Netherlands a similar initiative KLIC for sharing the location of underground utilities was augmented in 2018 by a "key registry of the underground" (BRO) which mandates sharing geotechnical information discovered during drilling and construction excavation.  There has also been a pilot by the City of Chicago to share information captured during excavation with contractors.  An interesting experiment by Bentley placed low cost consumer cameras on a backhoe to photograph exposed utilities and used software to identify and georeference underground utilities exposed during excavation.  In another experiment exposed underground infrastructure together with accurately surveyed control points were photographed with smart phones and the images processed to identify and georeference underground utilities.

When viewed together the technical advances in GPR and the initiatives to share information about the underground are evidence of accelerating vendor investment in remote sensing hardware and software. In addition governments are beginning to show serious interest in finding ways to encourage the sharing of information about the location of underground assets.

Question: Why has there been so little change in the GIS industry in utilities and communications over the past twenty years? Why have there been no new entrants in this space?

A fundamental problem is that data has been valued less than the physical infrastructure itself.  The challenge of changing traditional construction practices is another.  I also believe that there has not been an awareness of the cost of unreliable GIS data among utilities and regulators. But with the growing interest in developing digital twins of network assets there is a growing awareness of the importance of accurate, real-time GIS data among regulators and utilities and telecoms.  The example of ANEEL in Brazil is evidence of how regulators can contribute to changing this.  As I have mentioned previously enlightened procurement policies can also have an important impact. Another factor that is harder to change is a liability structure relating to utility damage during construction that provides little motivation for telecoms and utilities to improve the quality of their location information about their underground assets.

Audience question: What are the principle differences between the GIS requirements for telecom and utility network operators? What can these different industries learn from each other?

In the U.S. since the breakup of AT&T, the development of cell technologies and the rise of the internet, telecom (including cable) has become extremely competitive.  This has engendered a focus on the customer that is still rare among electric power and gas utilities, and even more so among water utilities. This has led to the explosion in smart phone capabilities and smart phone apps. It also means that telecoms are reluctant to share information about the location of their network infrastructure.  This makes them reluctant to share information about their underground networks without assurance that this no more of this information is shared than is necessary.  Electric and gas utilities are generally more cooperative and more willing to share data.  Another major technical difference is that large telecom companies have led most industries in the development of IT technologies - remember the developments at Bell Labs.  Most electric power, gas and water utilities have not had much IT capability in-house, which is why there is high level of interest among small to medium sized utilities in software as a service (SaaS).  Another technical difference is that telecom data models are more much complex than utility models.  My rule of thumb has been, telecom data models involve hundreds of classes, electric maybe a maximum of 150, and gas and water less than 50.  This is changing with the rise of the smart grid with utility networks evolving to something closer to communications networks.

This suggests that utilities can learn a lot from the IT expertise of telecoms and in particular with the growth of smart grid from the complex data models and supporting applications developed by telecom firms.  They can also learn from the consumer-oriented focus represented by smart phones and smart phone apps.  Telecoms could learn from utilities about the benefits of sharing data, especially about the underground.

Audience question: What is the role of governments to improve the accessibility and interoperability of utility

As I have mentioned there is a growing awareness of the social and economic cost of not knowing where network assets are located or their condition.  Collecting and sharing this information does not necessarily require government involvement, but there are reasons why governments should be involved.  As I mentioned this is a sensitive issue for the very competitive telecom industry.  Any policy has to protect critical competitive information.  Secondly it can't simply push all the cost off on construction firms who already suffer from very low margins.  In the U.S. an important additional reason for government involvement is the Critical Infrastructure Protection act which restricts access to location and other information about network assets.  As I have mentioned a growing number of governments have gotten or are getting involved in encouraging or mandating the sharing of information about network assets. Furthermore in some countries there is discussion underway about a national infrastructure map (U.S.) or a national digital twin (U.K., Singapore and others).

Key takeaways

• There are important technology innovations among them reality capture for above and below ground and machine learning to detect and assess network assets that are poised to dramatically change the utility operating business.
• Governments are recognizing the economic and social costs of not knowing the location of network assets and are implementing policies to encourage or require the sharing of location information about network assets.

IoT platform for integrating two decades of Hexagon's technology acquisitions announced at HxGNLIve

In May of last year Hexagon acquired a company based in Alpharetta, Georgia called Catavolt that combines internet of things (IoT), analytics and mobility. At HxGNLIve in Las Vegas Ola Rollén in his exhilarating opening keynote - beginning with incredible graphics showing every foot of a young gosling's 400 foot fall down a cliff (which happily it survived) and ending with an incredible slalom ahead of an avalanche punctuated with the skier executing a back flip - announced its new name Xalt and showed a unique example of what it is capable of.  Xalt runs in the cloud on Amazon Web Services and collects data from sensors, which could be a metrology (measuring) device, an Oracle database or SAP. It then analyzes the input stream looking for outliers and raises an alarm that causes something else to happen. It could be informing a human that there is a problem or it could be linking to another machine. In the example that Ola showed a measuring device could be seen measuring the precision of what a robot did and when the precision became worse than a threshold, it raised an alert which caused the robot to recalibrate itself.  Another example of what the Xalt platform is capable of was demonstrated on the exhibition floor where we were invited to develop an app in seven minutes that integrated data from several different sources. The development platform was a laptop and the resulting app ran on an iPad.

Xalt is an internet of things platform supporting many different sensors and types of sensors, big data analytics and different clients especially smart phones. Xalt supports multiple AI data sources including imagery, video and big data for applications such as predictive maintenance, change and anomaly detection. The platform monitors, senses and acts on all connected data inputs in real time. It accesses sensors from remote locations and utilizes the data to optimize business processes. It includes AI capabilities including deep learning with digital neural networks. From Ola's presentation it is clear it is not intended as a standalone product but rather as a platform that integrates the many and disparate devices and systems that Hexagon has acquired since Hexagon began its incredible journey in 2000 from a Swedish contract metals manufacturer and producer of valves, antennas and pressure vessels to a global geolocation and measurement, design and construction application and visualization technology company with $4.11 billion in revenue and 18,315 employees in 2017. During these nearly two decades Hexagon acquired 64 companies many with geospatial capabilities including Leica Geosystems in 2005 and Intergraph in 2010. The big question for observers of Hexagon's meteoric rise has been how was it to integrate these disparate acquisitions into a synergistic whole that is bigger than its component parts.  It is clear from Ola's keynote that Xalt will be the foundation for Hexagon's big leap to achieve this.

Generating real-time 3D reconstructions using a UAV equipped with a digital camera linked to a laptop

Drones are very efficient at capturing a large volume of data that often requires hours of post-processing in the cloud to create 3D reconstructions that can be used for 3D visualization and other applications.  In his talk at SPAR3D 2018 in Anaheim Michael Jones of Lockheed Martin showed how a drone with just a photo camera and access to a GPU (graphical processing unit) on a laptop can use SLAM for real-time 3D reconstruction enabling advanced visualization and 3D modeling for immediate ‘in context’ information. The ability to generate 3D point clouds and 3D imagery in real-time as a vehicle flies enables applications that need to operate real-time, for example, the ability to automate the inspection of large critical infrastructure in real-time, comparing architectural drawings to actual 3D imagery for immediate progress updates.

SLAM is a technology that uses the identification of 3D objects from a 2D or other camera to identify 3D objects which can be used for 3D reconstruction. Applications include anything requiring 3D information such as collision avoidance, inspections, or navigation in GPS-denied locations. I have blogged about a a UAV equipped with LiDAR and using SLAM for navigation can be used in areas where GPS is not available. But LiDAR is heavy and expensive and requires an expensive drone to carry it for a significant amount of time.

Modern GPUs are designed for massively parallel computation and SLAM, which is based on triangulation, can be parallelized to take advantage of this capability.  Laptops equipped for gaming include very fast GPUs. In his talk Michael demonstrated how a drone with just a photo camera connected via a wireless link to a laptop can use SLAM to create a 3D reconstruction of an area in real-time.  The software, developed in Canada, is called Hydra and is commercially available. The pro Michael hinted at work underway that would enable SLAM processing onboard the drone.

Open source geospatial application reduces copper cable theft by 70%

A highlight of the SA Geotech conference in Johannesburg was a presentation by Ryno Goosen of Suritec Geospatial.  Ryno, who was awarded the Best Paper prize at SA Geotec, gave a fascinating presentation about how he developed an open source GIS solution to fight copper cable theft.

In Canada copper cable theft costs the Canadian electricity sector an estimated $40 million annually.  In South Africa in 2014 there were 72,533 reported incidents of copper cable theft resulting in 10,736 arrests.  Copper theft incidents in the U.S. have tripled in the last 5 years.

Goosen Goosen's solution architecture was based on open source geospatial software.  He chose open source for several reasons.  First and foremost, it enabled massive scalability by providing an elastic IT infrastructure.  Secondly it provided open interoperability with other open source software as well as proprietary systems. He also emphasized the importance of using software that was "battle-tested" in both military and commercial environments. 

His solution architecture was built on Cesium, GeoServer, Boundless Desktop/QGIS, Protobuf, Cassandra, WildFly, Solr and GeoTrellis.  Using this solution he was able to combine data from different sources including ground patrol and remote sensors.  By analyzing this data geographically and temporally he was able to determine spatial and temporal patterns of copper cable theft.  This enabled optimum deployment of resources.  The results were impressive.  Over a 30-month period cable theft was reduced by 69%.

The success of Goosen's shows that open source GIS software has become a viable alternative for building complex and highly scalable solution-oriented architectures.  He also suggested that it levels the playing field for small to medium size enterprises by providing them with similar intelligence capabilities to their larger counterparts.

Are point clouds going away ?

At the SPAR3D 2017 conference in Houston, one of the interesting questions posed to a panel comprised of Greg Bentley (CEO of Bentley), Burkhard Boeckem (CTO of Hexagon Geosystems), and Shabtay Negry (Senior VP at Mantis Vision) is whether point clouds, whether from digital photography or LiDAR, will effectively disappear, being replaced by meshes for most reality modeling applications.  Meshes are mathematical constructs, typically 3D triangular networks, that are smaller and much easier to manipulate than point clouds, which are typically huge and a challenge to edit.
 
I blogged previously about a digital model comprised of meshes of downtown Philadelphia that was used to orchestrate and secure the Papal visit.  All the data was collected as photographs which were then used to create a single reality mesh using Bentley ContextCapture.  At SPAR3D there are presentations on two other projects which used a similar approach to create models of the city of Helsinki and the Cologne Cathedral.

Another fascinating example is the USS Arizona digital project which combined underwater lidar, sonar, aerial imaging, and existing photography and surveys to produce a survey-grade 3D model of the USS Arizona in the form of a mesh using Autodesk Remake.  Surveys were conducted in 2014 and 2016.  Just recently the two digital mesh models of the ship were compared to create a difference mesh to show areas where there has been change in the two years since 2014.

At SPAR3D there was a consensus among the panelists that meshes are replacing point clouds for most applications. Point clouds are just too big and have too little intelligence. They will remain as an intermediate data type because most scanners generate point clouds.  Replacing point clouds for many applications meshes will grow in importance in the future.  But realizing their full potential will require interactive, editable meshes that allow small bits to be changed or replaced without having to regenerate the entire mesh (which is often the case with triangulated surfaces).  Easy to use analytic tools like those used to create the difference map of the Arizona will also be required.  The panelists thought that with the appropriate edit and analytic tools people will be able to manipulate surfaces easily and may not  even be aware of what the form of the underlying data structures is.  Smaller files will also make cloud processing easier because in the future huge point cloud files won't have to be uploaded to the cloud.

Construction industry poised for rapid digitization

The CG/LA Infrastructure Leadership Forum in Montreal has provided a fascinating glimpse into what is happening around the world in infrastructure from a technology, financing, and business model perspective. . From 50,000 feet the big item is that the construction industry may be at the tipping point with respect to digitization.

PPP

Probably the highlight of the event is the success of PPP projects, especially in Canada.  About one quarter of major infrastructure projects in Canada are PPP.  In the U.S. the PPP model works well in some jurisdictions, for example, the Port Authority of New New York and New Jersey.  In some states PPP is illegal and even at the federal taxation level until recently there were tax regulations that hindered the adoption of the PPP model.  The presentations made clear that there are multiple PPP models.   Several projects in Latin America were looking at PPP alternatives that were better suited to their jurisdictions.  A conference attendee from Nigeria was clearly very interested in which of the PPP models were appropriate in his country.

Investment in infrastructure

Big numbers were on virtually every speaker's lips.  The Canadian Federal government intends to invest $180 billion over ten years in infrastructure, the Quebec government $87 billion, President Trump threw out $1 trillion over five years in a state of the union speech a few days ago, Boston Consulting Group has estimated that there is a spending gap of $1 trillion annually, and the ASCE estmates that US infrastructure requires an investment of $4 million just to make up for the lack of maintenance over the past decades. Some speakers made a convincing case that these estimates are overblown. Whatever the numbers there was a consensus that there is a very large need for investment in infrastructure. 

But infrastructure, while among the top ten priorities for Americans, is not up there with jobs and the economy.  The Boston Consulting Group estimates that there are 15.4 million jobs in the infrastructure industry.  On average these are better paying than the average blue collar job.  The BCG modeling suggests that $ 1 trillion invested in infrastructure could get this up to 19 to 20 million jobs.  To get attention infrastructure has to be about jobs.

The good news is that there was a consensus that there is a lot of money looking for infrastructure projects. One speaker estimated ten times as much money as projects to invest in.  The holdups appear to be:
1) Permitting - Few are willing to commit money to projects that don't have the requisite permitting in place.  In the U.S. that process requires on average 9.5 years, but can take up to 15 years.
2) Social licence - These days it is not enough to simply get the state, federal, presidential, US Army Corps of Engineers and other regulatory permits.  It is necessary to get a social licence which means actively seeking buyin from the community.  For example, the Plains and Eastern Tennessee transmission line project has been supported by the Sierra Club and other environmental organizations as well as local landowners and other stakeholders.  Sometimes opposition comes from unexpected sources.  Clean Line's several big transmission projects have sometimes been filed against by the local utilities companies whose service territory the line crosses.
3) Business case - Investors are looking for a low risk investment with a good return.  That means that benefits to various stakeholders have to be identified and stakeholders brought into the project to spread the risk and reduce it for any one participant.

A lot of this money is private from domestic and international sources.  Japan and Saudi Arabia were both mentioned as sources of money for infrastructure investment.  Canadian pension funds are active in investing in infrastructure internationally.  To attract private funding infrastructure projects will have to generate returns, and that will drive investment in technology to improve productivity.

U.S. infrastructure market

Another highlight of the conference was Dan Rule's presentation on the Trump Transition Team's recommendation for infrastructure development in the next four years, which I blogged about previously.

Interestingly what Trump decides to do with respect to infrastructure may not be all that important. In the U.S. the federal government is not by any measure the biggest player in the infrastructure market - it only represents about 10% of the market. The big player is local governments. Local government has enormous power to choose projects, raise money and borrow money. This local autonomy is baked into the Constitution. Local governments decide on infrastructure projects and then float (tax deductible) bond issues to finance them.  The muni bond market is $3.2 trillion and is the second largest debt market.  There is good news here as well. The majority of the bond issues at the local level to fund infrastructure projects are successful in raising the necessary money through rates.State governments are also big players in the infrastructure market.  One speaker recommended to vendors and consultants looking for opportunities in the U.S. to look for municipalities who had recently successfully floated bond issues or states that had increased the gas tax or counties that has implemented a sales tax. In some states the state government and local governments are collaborating to fix all bridges in need of rehabilitation rather than inefficient one-offs by individual municipalities and counties. 

Large infrastructure projects

There are some very innovative large infrastructure projects around the world that were profiled at the conference.  I was very impressed that the long-haul transmission business appears to be coming to life in the U.S.  This business is not being driven by the incumbent power utilities, but by new players like Clean Line.  Geospatial technology underpins transmission line siting, transportation, and increasingly horizontal construction as drones, scanners, 3D visualization and other digital technology transforms the construction industry.

Technology

From a technology perspective just about every speaker referred to the lack of progress on productivity in the construction industry. Engineering and construction are second from the bottom on the IT adoption chart. But Wipro, Booz Allen Hamilton, Aconex,  Jacobs, and other vendors and analyst McKinsey believe that the construction industry is poised for the digitization revolution.  Vendors are investing heavily in technology including artificial intelligence, digital modeling such as BIM and full lifecycle BIM or BIM++ as one speaker referred to it, machine learning, drones, neural networks, virtual reality, big data, analytics, automation and hyperautomation.

GIS has been widely used by utilities for years for automated mapping/facilities management, back office records management, asset management, transmission line siting, and more recently for design and construction, energy conservation, vegetation management, mobile workforce management (MWFM), and outage management (OMS). Now, utilities are integrating GIS with automated meter infrastructure (AMI) and supervisory control and data acquisition (SCADA) systems. Intelligent design has crossed over from the office to the field in utilities, also enabled by the capabilities of GIS. Geospatial-related analytics (spatial analytics) is seen as one of the key aspects of success for electric utility operations in the smart grid era. Looking for patterns and correlations between different land, weather, terrain, assets, and other types of geodata will be increasingly important for utilities. Power-related analytics with geospatial components include network fault tracing, load flow analysis, Volt/VAR analysis, real-time disaster situational awareness, condition-based maintenance, and vegetation management.

As an example, PrecisionHawk is not only selling drone services but the geoanalytics to extract useful, actionable information. One of their targets is the construction industry and they intend to fit their solutions into existing workflows.  For example, a drone can collect aerial imagery of a stockpile, and the software provided by PrecisionHawk enables the volume of the stockpile to be calculated from the imagery without the user having to know much about drones, imagery, meshes or algorithms for calculating stockpile volumes.  PrecisionHawk also have an FAA permit for beyond-line-of-sight (BVLOS) drone flights. (I already blogged about the pilot flights that Xcel Energy also has an FAA permit for.) This suggests the next step will be drone data collection and geospatial analytical applications based on this data for linear construction projects.

Utility industry vendors urged to adopt data model standard

Software comes and goes, but a utility's data model persists. A shared electric power data model enables electric power utilities to move away from incompatible point solutions toward tightly integrated solutions which are essential for real-time monitoring, situational assessment and decision making.  Based on today's smart devices and sensors and utility-wide communications network, decision making in a real-time, big data world requires tight integration between applications.  To realize the full potential of the smart grid, utility industry vendors need to move rapidly to adopt data model standards, that's the recommendation of Geoff Cameron of AGSI. which has developed a platform based on a utility data model standard that provides real-time access to critical infrastructure, customer and operational information. 

 

Traditional electric utilities are typically departmentalized and rely on manual, paper-based processes. Sharing knowledge is relatively slow, cumbersome, and reactive in nature and characterized by redundant data, many incompatible point solutions and non-real-time.  Facilities are inspected periodically, data is accumulated and analyzed, a report is written and sometime in the future do something is done.  Today's intelligent electronic devices, bidirectional communications networks, and automation provide the basis for dramatically improving a utility's SAIDI and SAIFI outage statistics.  It also provides a foundation for transactive energy.  What is required to realize the full potential of the smart grid is a platform that enables real-time integrated applications.

 

At Distributech 2017 Dan Guatto, COO and VP of Engineering and Operations, described how Burlington Hydro (BHI), a distribution utility with 65,500 customers, has evolved from a point solution environment to an integrated, secure, and real-time utility.  As is typical of many distribution utilities, BHI was facing increased technology sophistication, the need integrated business workflows (operations, engineering, customer engagement, financial) to improve efficiency.  BHI had to address siloed legacy systems and business processes.  Many industry solution offerings fixed individual problems but were expensive and did not take into account integration and long term data management.  Many “enterprise” offerings in the marketplace were proprietary black box solutions with little flexibility that made BHI feel that it was surrendering control of its data and processes.  One of the major problems BHI identified was the lack of O.T. / I.T. convergence for true enterprise efficiency.   Solutions required quite expensive capital outlay and deployment.  Projects became too big and and bogged down and were unable to address fast paced industry change.  In Ontario some of the larger utilities made significant technology investments, but at the end of the day these projects were extremely expensive, disjointed and largely underutilized (or even abandoned). BHI was also concerned about the infancy of smart grid technology and rapid future changes and didn't want to be tied to an inflexible system as the speed of industry changes accelerated.  BHI was convinced that a sound system architecture had to be based on electric industry standards and was aware that AGSI had been working with EPRI on a real-time platform based on Common Information Model (CIM) and Open Geospatial Consortium (OGC) standards.

 

Over the past decade open electric power data model standards have been developed for transmission, distribution, smart substations and other electric power functions.  Worldwide the Common Information Model (CIM) has become the standard for transmission.  CIM is also widely used outside of North America for distribution.  In North America the Multispeak standard is the most widely used distribution data model standard, although CIM is used by larger utilities.  These data models are mature and cover virtually all aspects of electric power utility operations.

CIM was originally developed for standardizing electric utility information exchange.  It was designed by electrical engineers, adopted by the IEC and supported by EPRI and multiple electric utility partners, vendors and organizations.  The IEC, EPRI and partners continue to extend the model to cover asset health, station design, work, smart grid integration, IoT Integration, and network operating protocols.  CIM is able to support all aspects of electrical operations (OMS/DMS), electric analysis / smart grid, distributed energy (DER), work orders, crew management, and assets.  AGSI has implemented the CIM Core, Topology, Wires, & Assets object models in a relational database (Oracle Spatial).  Its implementation represents a real-time network model of the CIM electric system data model and provides a centralized view and interaction with the network in real-time.

 

Today this technology connects and integrates engineering, SCADA, AMI, GIS, load analysis, other smart grid deployments such as EV charging, customer, and asset databases into a single accessible platform and enabled BHI to evolve from a point solution environment to an integrated, secure, and real-time utility.  Real-time data unification provides a basis for transparent workflows and a centralized reliable source of truth enables advanced analytics for optimized investment planning.  An open architecture based on industry standards provides the flexibility to adapt to rapid changes in technology as the smart grid evolves. Real-time information sharing enables faster decision making and increased customer satisfaction via real-time situational awareness about outages and planned work.  Don Guatto believes that it provides the foundation for transactive energy which is looming on every utility's horizon.

Big geospatial data workshop

The rapid increase of sensing and mobile devices is an unstoppable information source that is transforming human lives in incredible ways. Every two days the human race is now generating as much data as was generated from the dawn of humanity through the year 2003. Big Data is driven by the real time collection, analyses, and visualization of vast amounts of the information most of which has a geospatial or location component. The data was measured somewhere or it describes events at some location. Processing this enormous and varied stream of geospatial information with new big data techniques will bring new benefits to many applications in our world.

The Location Powers: Big Data workshop will bring together leading developers of big geospatial data systems. Participants will not only hear the latest developments but will contribute to the collective discussion advancing the use of big data computing techniques applied to geospatial data with results in many applications.

A public webinar will be held on Wed 3 November at 10:00EDT to share the Big Data Whitepaper and outcomes of the Big Data Summit

Tuesday 20th September

Meeting Location:
University of Central Florida Executive Development Center
36 West Pine Street
Orlando, FL, 32801 USA

#LPBigData