Between the Poles

California legislation mandates reporting underground utility damage by excavators

In 2016 in the first of several far reaching regulations the California legislature passed SB 661 that created the California Underground Facilities Safe Excavation Board. The primary activities of the board were to develop standards and prescribe rules and regulations covering underground utilities during excavations. A number of other bills followed, the most important of which was SB 865 in 2020 which contains provisions with far reaching implications.

Reliable as-builts

First of all, SB 865 requires network operators operator to maintain records of the location of subsurface utilities.  Starting in January 1, 2023, all new subsurface installations are to be mapped using a geographic information system (GIS) and maintained as permanent records by the network operator. It is not clear from the legislation itself what the implications are for the accuracy of as-builts submitted for newly installed facilities, but optimistically it will result in regulations similar to those in Colorado and Montana which mandate accurate as-builts signed by a professional engineer or land surveyor.

Reporting damage incidents

In North America few jurisdiction require reporting of incidents of underground utility damage during construction. Another provision of SB 865 with far reaching implications makes reporting underground damage mandatory for excavators. The California Code of Regulations requires that an excavator causing damage must file a report at a regional notification centre within two hours of the incident. Emphasizing the importance of this data the bill further specifies that the regional notification centre must notify the California Underground Facilities Safe Excavation Board of the damage incident.

Recording information about incidents of underground damage is essential not only in developing an understanding of the causes of damage, but it also provides the basis for assessing the effectiveness of different programs for reducing damage. To put this in context in North America reporting of incidents of underground damage is generally not required.

In North America extensive damage data is compiled by the Common Ground Alliance (CGA) which receives over 400,000 voluntarily submitted damage reports every year. CGA issues a DIRT Report every year with valuable data on underground damage including the rate of damage prorated to construction activity. The California legislation should enhance the perceived value of collecting and compiling data on underground utility damage.

UK pilots demonstrate secure platform for sharing information about underground utility networks

In the 2017 autumn budget the Chancellor of the Exchequer announced an important initiative to increase the value of the contribution of spatial data to the UK economy. The Geospatial Commission was created in the centre of government, as an independent, expert committee. It aims to unlock the significant economic opportunities offered by geospatial data and to reinforce the UK’s geospatial expertise on the global stage.

To provide strategic oversight of the geospatial landscape in the UK, the Commission provided a  £5 million fund to support collaborative data projects across 6 Partner Bodies - Ordnance Survey, HM Land Registry, British Geological Survey, Valuation Office Agency, UK Hydrographic Office and Coal Authority who between them hold most of the governments high quality geospatial data.

Among the first projects initiated by the Geospatial Commission was a process leading to the creation of a National Underground Assets Register (NUAR).  The objective of NUAR was to create a secure means to share information about underground infrastructure mainly among local government organizations, and utility and telecom network operators.  The register is intended to show where electricity and telecom cables, and gas and water pipes are buried and is targeted on several use cases: safe digging to avoid utility strikes, on-site construction efficiency, site planning, data exchange, and improved coordination. The project started with £3.9 million allocated for two pilot projects split between Central London and North East of England.

The system for sharing underground data for both pilots was provisioned by OS on an in-country cloud platform. It is built on an OGC standard data model and supports OGC standards such as WFS for data exchange which enables most GIS products such as QGIS and ArcGIS to access it. A lot of the effort was devoted to security, privacy and protection of competitive information. For each network operator there is a terms and conditions document that defines the legal requirements for how that operator's data can be used, Queries are limited to 10,000 square meters for field user access and there are other limits to prevent multiple queries aimed at viewing an entire network. System users are network operators, local government, and contractors working for either of these.

Using an earlier prototype version of this platform in 2018 OS and Northumbrian Water undertook to create a map of underground infrastructure in Newcastle on Tyne.  The objective was to compile in collaboration with utilities, local authorities and partners a combined infrastructure map for small sample areas including water, wastewater, gas, electricity, telecoms and other underground services. The project benefited from a spirit of cooperation - all major local utilities and telecoms in Newcastle were happy to share their data and had vector data available to contribute.  The result was a database where each utility was steward of its own data which was shared through a hub. Interoperability based on OGC standards was demonstrated with three different GIS systems consuming the combined underground data through APIs. The data could be ingested into GIS systems with internet access, regardless of format and provisioned to both field engineers and planners.    As part of this experiment in creating a shared map of underground utilities, contractors were brought in to actually dig a hole so that participants could experience first hand what is involved in excavating to determine the location of buried utility assets.  OS created a web interface to enable excavation teams in the field to access the data relevant to them via a mobile device.  By the end of the week it was possible to demonstrate a working system that allowed excavators to query the map of underground utilities and use this information during excavation and construction.  At the innovation festival a data sharing agreement was developed that all parties agreed to – Northumbrian Water, Northern Gas Networks, Northern Power Grid, Openreach, Durham County Council, Newcastle and Sunderland City Councils, Cranfield Soil and Agrifood Institute, British Geological Survey and Ordnance Survey.

Following on the innovation festival the participants formed the North East Underground Infrastructure Hub (NEUIHub) consortium in order to progress the creation of a ‘Common Infrastructure Map’ implementing a dataset that contains almost full water, gas, electricity and a great deal of telco data for the city of Sunderland.

The most recent implementation of the NUAR system was deployed in late 2019 in the North East of England and as part of the pilot in Central London. When I was in Newcastle for a meeting of the NUAR Advisory Group, we went out in the field with an iPad to compare the network information available on the NUAR pilot system with above-ground visible infrastructure, manhole and handhole covers with distinctive designs allowing the below-ground network to be identified.  While the quality of the data was what I expected from utility as-builts (PAS 128 quality level D), the remarkable part of the experience was being able to see all the underground networks, water, wastewater, electric power, gas, and telecom, in the query area together on the screen - a very rare experience anywhere in the world.

The Central London pilot is covering the area of six boroughs in Central London. While the vast majority of relevant data has already been vectorized in the Northeast of England, this is not necessarily the case in London.The Greater London Authority (GLA) will provide funding to local authorities to locate data and upgrade its format for use within the pilot platform. To enable comparison of vector and raster data, for three of the boroughs all the data will be vectorized. For the remainder various formats including raster will be used.

There are two remarkable achievements of these pilots.  The first is a secure legal framework within which municipal government organizations and network operators are able to safely and securely share their data. When a user accesses data of a network operator, there is a downloadable document associated with the data that defines the terms and conditions under which the data can be used. 

The other remarkable element is that it makes it very easy to provide information about  errors that are observed by stakeholders in the field, for example, if a contractor performing road work for a town council exposes pipes or cables that are not located where utility as-builts indicate they should be. In the NUAR pilot system these are called observations and are represented by green triangles associated with the incorrectly located features. Observations enable a worker who notices an error in the field - a wrong location, incorrect type of equipment, or other problem - to enter corrected information about the location, type of equipment, and other information from a mobile device.

Damage to underground utilities represents a significant risk to the public and is a major cause of construction project delays and budget overruns. The NUAR project represents an important step forward in enabling the sharing of information about the location and other information about underground infrastructure that will contribute to reducing the risk of damage to underground utilities during construction.

Locate issues are a major cause of underground utility damage/strikes in U.S. and U.K.

In the U.S. and the U.K. statistics reveal that about a quarter of underground utility damage and strikes is attributable to locating issues including inaccurate maps, missing data and problems with underground locating equipment.

There are over 10,000 works (construction projects) taking place every day across the UK with tens of thousands of incidents of underground utility damage during excavation occurring annually. But there are no reliable statistics on this important safety and economic issue.  A very important initiative in the UK is the the Utility Strike Avoidance Group (USAG) which has begun collecting information about underground utility strikes during construction.  USAG has just released the 2017 & 18 Utility Strike Damages Report.  For this biennial report USAG analyzed reports on 3000 incidents of underground utility damage or strikes submitted by 34 USAG members. The information submitted on each incident includes when the strike occurred, where the strike occurred, how the asset was damaged (equipment type), what the damage was, type of asset damaged, type of construction project, and what caused the damage.

In the U.S. the Common Ground Alliance (CGA) has been collecting voluntarily submitted statistics on underground utility damage since 2003.  The latest CGA DIRT report for 2018 concluded that progress in the U.S. in reducing damages has plateaued.  Total damages in the U.S. increased from 439,000 in 2017 to 509,000 in 2018, representing a 16% increase. Damages per 1,000 one call information requests increased by 11%, from 1.87 to 2.08, and damages per million dollars of construction spending (2017 constant dollars) went from 0.359 to 0.392.

Cause of underground strikes

One of the interesting analyses in the USAG report is the cause of underground utility strikes.  Respondents are requested to identify the cause of the damage from a list of over 15 possible sources.

I have simplified the number of categories to five;

1. Inadequate excavation procedures (Inattention/lack of awareness, Not following procedure, No protective systems, Clearance not maintained, Misuse of tools/equipment, Lack of supervision, Excavation practices not sufficient, Service markup not maintained,)
2. Insufficient survey practices (Insufficient survey practices (use of locating equipment), Inadequate survey)
3. Inaccurate and missing underground utility location information (Inaccuracy of plans, Assets not on relevant plans)
4. Inadequate work planning (Inadequate assessment of works, Insufficient time allowed)
5. Insufficient skills (Insufficient competency, Insufficient gang skills)

Since most of these incidents were reported by contractors, I would expect that their perspective would be reflected in the cause profile. Over 50 % of the causes identified are related to excavation practices.  The next highest cause is missing or inaccurate location information followed by insufficient survey practices.  Together these locate issues represent the second largest reported cause of strikes in both 2017 and 2018.

Comparison with Common Ground Alliance DIRT 2018 Report

For comparison the Common Ground Alliance, which collects data on underground utility damage incidents (strikes), includes analysis of 440,749 damages and near miss events submitted for 2018 in the latest DIRT report. 

CGA damage report submissions includes cause.  The submission form offers 25 options which the CGA groups into six root cause groupings. In 2018 the most frequent reported causes were excavating issues 31.22%, locating issues 20.79%, and notification not made 23.18%.  Excluding incidents where 811 was not notified, damages attributed to locate issues was the second most reported cause similar to the USAG findings.

Locate issues as a proportion of all incidents	Locate issues (%)
USAG 2018	27
USAG 2017	28
CGA DIRT 2018	27*
CGA DIRT 2017	18*

* Excluding damages where 811 was not notified

The profile of the stakeholder type reporting these incidents to CGA; 64% locator, 8% excavator, 17% natural gas operator, 8% telecom operator, and 4% other is quite different from the USAG report where about 90% of reports are by contractor/excavators.

Cost of excavation damage

I have blogged previously about a study at the University of Birmingham to estimate the average cost of underground utility damage which concluded that each incident entails an  estimated direct cost of £3600 per strike.  Indirect costs are estimated to amount to 29 X the direct cost which results in an estimated average true cost of each utility strike of over £100,000. Statistics for the total number of incidents of underground utility damage have not reached the level of North America where the Common Ground Alliance collects data on at least 50% of all incidents, and it is estimated that there are roughly 400,000 to 800,000 incidents per year.  There is apparently an (unsourced) estimate that the total number of incidents in the U.K. is 60,000 per annum which suggests that incidents per capita are less frequent than in North America. Based on this estimate the total annual cost of underground utility damage is the U.K. could reach £6 billion. 

Accelerating progress in open spatial data infrastructure at the municipal level in Canada

At the SDI Summit in Quebec City Gordon Plunkett offered an interesting perspective on the penetration of open spatial data infrastructure (SDI) at the municipal level in Canada.  Local governments are traditionally responsible for local services such as safety, infrastructure, environment, and recreation that directly impact citizens' daily life. But citizens are often not aware of available services. To increase citizen engagement local governments should make their services more easy to use, understandable, and relevant by providing them in visual form via web pages and web apps.

Background

Open data has been gathering momentum among governments initially driven by United States Federal Government data being in the public domain (since 1909.) The principle of free and open access to government geospatial data has been adopted by many governments including US Federal, Canada, Japan, South Africa, Brazil, California state and counties, and by the City of Vancouver and other cities.  In 2013 G8 leaders (Canada, France, Germany, Italy, Japan, Russia, United Kingdom, United States, and the European Union) signed a charter on open data.  The Ordnance Survey in the UK began releasing some of its data with open licensing as part of its OpenData initiative in 2010, but it wasn't until 2018 that the OS Mastermap was made openly available for the public and businesses to use. But being able to access prepared maps and other derived material is just a first step, governments need to provide access to raw geospatial data in commonly used Web-friendly formats. In the U.S. Data.gov was launched in late 2009.  In 2013, President Obama issued an Executive Order making open and machine readable the new default for federal government information in the United States.

In Canada the Government of Canada announced its Open Government Action Plan in 2012.  The fundamental principle was a government-wide directive making government information and data available to the public by default. Geospatial has led the development of open data in Canada.  Of the 80,000+ datasets that are available on the federal government's open data portal, 69,000+ are geospatial. Currently in addition to the federal government eleven provinces and territories and over 60 municipalities have open data portals. At the municipal level Vancouver has been in the forefront of municipal open data - Vancouver's open data portal was initiated in September 2009.  In November 2009  the City of Toronto launched Toronto.ca/open, the City of Toronto's official beta data set catalogue, which provided open access to city data.  In January 2010 the City of Edmonton opened Edmonton's open data catalogue, which made city data including geospatial data openly available.  In 2010 Ottawa created an Open Data Ottawa site to encourage digital innovation, improved service delivery, and stimulation of economic growth.

A major motivation for open data has been government transparency.  But there is also evidence that open geospatial data contributes to the economy.  In 2008  Australia and later New Zealand commissioned studies of the contribution of spatial data to the national economy that have concluded that with appropriate government policies the contribution to the GDP, estimated to be on the order of 1%, could be doubled.  In 2015 Natural Resources Canada released the Canadian Geomatics Environmental Scan and Economic Value Study that estimated the contribution of the geospatial sector to the Canadian economy to be on the order of $21 billion.

Current state of open SDIs in Canada

Interoperability is key for an effective SDI. The inability to easily share data is a serious constraint on the value that can be derived from it. Gordon presented a simple SDI interoperability maturity matrix. Most municipalities probably fit into what he called syntactic interoperability, the ability to share data using common file formats and APIs.  Some have ventured in sharing data at the semantic level, which means recognizing content through shared data models such as the Canadian Municipal Data Model (CMDM), National Road Network (NRN), and NENA standards for 9-1-1.  A few are venturing into addressing the issue of different names for the same objects in different jurisdictions or in different disciplines.

Patterns of use for SDI include mapping and visualization, monitoring, field mobility, data management, analytics, sharing and collaboration, decision support, citizen engagement, and design and planning. 

Perhaps the top challenge for local governments, who hold the data that is most directly relevant to citizens, is making them aware of what is available and how to use is to address their every day concerns. Gordon presented some examples across Canada of interesting open data portals: Kamloops, British Columbia; Saint John, New Brunswick; Halifax Regional Municipality, Nova Scotia, which have made a lot of geospatial and other data available on open portals; St Albert, Alberta which enables citizens to report broken infrastructure and track progress in resolving issues; Regina, Saskatchewan which also makes a lot of data available on an open portal; Red River Planning District, whose site is heavily used during the annual Red River flood season; Brampton, Ontario, a poster child for open data and citizen engagement; Repentigny, Quebec, which enables users to easily determine the availability of municipal services close to where they live or intend to live; Prince Edward Island Hydro, which shows current electricity outages; and Mount Pearl, Newfoundland, whose portal include a parcel map in addition to roads and other basic map layers.

I would suggest that another data layer that should be part of every comprehensive SDI and that is missing in virtually all local, provincial, and federal SDIs is accurate maps of underground infrastructure in public right-of-ways.  Not knowing the location of underground cables and pipes has important implications for citizen safety and the reliable delivery of essential services.

In summary, local governments are traditionally responsible for local services such as safety, infrastructure, environment, and recreation that directly impact citizens' daily life. But citizens are often not aware of available services.  Local government can improve citizen engagement by making their services to more easy to use, understandable, and relevant by providing them in visual form via web pages and web apps.

Singapore's standard for location of underground utilities

Straits Times

In August 2017 the Singapore Land Authority (SLA) released a standard and specifications for the procedure and practice of utility surveying in Singapore.  The standard specifies the data that should be captured for new underground utilities including telecommunication, sewerage, water supply, electric power, gas, and drainage. It specifies that after installing utilities and prior to filling the trenches an as-built survey should be performed by a registered surveyor to capture the location of the utilities with horizontal accuracy of ±100mm and vertical accuracy of ±100mm. It specifies that the survey equipment to be used should be a total station, GNSS Real Time Kinematic (RTK), or 3D Laser Scanning.

This standard is not mandated by the Singapore Government but is intended to provide a baseline to be incorporated in construction contracts. Since 85% of land is owned by the Singapore government, if a major government agency such as the Urban Redevelopment Authority (URA) routinely incorporated this standard in its contracts as part of standard procurement practice, it would effectively become a de facto mandate. 

In addition to location, the standard also defines the attribute information to be captured.  For example, for an electric power cable, the standard specifies that x,y,z should be surveyed at 20 meter intervals for a straight line cable and more frequently for a curving cable.  In addition, height, width, number of columns, number of rows, number of ducts, number of cables and quality level should also be captured. The quality standard used in Singapore is; 1) ±100mm, 2) ±300mm, 3) ±500mm, 4) Unknown accuracy and 5) Trenchless method.  In Singapore most underground utility work is open trench, although there is some trenchless which requires gyro surveying.

The standard does not specify format or what is to be done with the as-built information.  Currently in Singapore there is nothing in place to require sharing of information about the underground like Netherland's BRO or the British National Underground Asset Registry (NUAR) project.  But there is a Digital Underground project underway to create an ecosystem for a digital twin of the underground as part of the Mapping Singapore in 3D project.  The digital underground project was initiated by the Singapore Land Authority with the the support of the Urban Redevelopment Authority.  The standard for underground utility surveying would create a basic data model for sharing information about underground utilities.

Global underground utility mapping market estimated at US$ 842 million

Markets and Markets has just released a report Underground Utility Mapping Market Global Forecast To 2024 in which it is estimated that the 2019 market size for underground utility mapping, which refers to a complete detection, processing/post-processing and visualization solution, to be US$ 842 million and growing at an annual rate of 11% (CAGR) expecting it to reach US$1.4 billion by 2024.  Currently underground utility mapping is estimated to represent over 15% of the underground utility locate market but it is forecasted to be growing significantly faster, 11% vs 6% CAGR. These estimates are stated to be conservative.

By underground mapping application M&M means an integrated hardware + software solution that detects and locates underground utilities and provides a way of processing/post-processing and visualizing the results using a map view, and increasingly a 3d map view, of underground utilities.  It relies on one of the remote sensing techniques for identifying, detecting, and labeling underground utility lines (telecommunications, storm sewers, water, natural gas, electricity, cable television, and fiber optics.)  GIS is specifically referenced because of its ability to assist with 3D data acquisition, management,  and visualization as well as image processing.  The study sited technological advances including the increasing availability of utility mapping and detecting applications and improving 3D viewing experience as important factors driving the growth of the underground mapping market.  M&M suggests that the non-technical drivers for the growing market are rising concerns over the safety of underground utilities and government initiatives for the implementation of utility mapping tools.

The M&M research focused on technological solutions, which includes ground penetrating radar, electromagnetic induction, and others with associated software and professional services including firms providing surveying and mapping, training and education and consulting services.  The vertical industries that were investigated include oil and gas, electricity, government and public safety, and construction.  The geographic focus of the study is broad including North America, Europe, and Asia Pacific.  The analysis does not include the revenue generated by the hardware from vendors like Leica Geosystems, Sensors and Software, and GSSI, but does include their software revenue.

The major vendors providing underground utility mapping solutions that were involved in the study include Hexagon Geosystems (Leica Geosystems), Sensors & Software Inc. (Sensors & Software), Guideline Geo AB (Guideline Geo), Geophysical Survey Systems, Inc. (GSSI), and US Radar Inc. (US Radar) and services firms Plowman Craven Limited (Plowman Craven), Cardno Limited (Cardno), Utility Mapping, Maverick Inspection Ltd. (Maverick Inspection), multiVIEW Locates Inc. (multiVIEW), , Vivax-Metrotech Corp. (Vivax-Metrotech), Enviroscan, Inc. (Enviroscan), Global Detection Solution Sdn Bhd (GDS), ProStar Geocorp, Inc. (ProStar Geocorp), and Underground Services, Inc. (SoftDig).

To put this in context an analysis also by Markets and Markets estimated that the global utility locator market amounted to US$ 5.17 billion in 2017 and predicted it would grow by about 6% per annum to US$ 7.50 billion by 2023.

Read more about locating underground infrastructure

Developing high precision highway maps for autonomous vehicles

Currently autonomous vehicles with some exceptions rely on on-board sensors for the detailed aspects of navigation. But many believe that high precision maps, which contain significantly more detailed information and true-ground-absolute accuracy than current road maps, will be essential for the safe operation of autonomous vehicles. These high-precision 3D maps will be specifically for the self-driving vehicle market and include detailed inventories of all stationary physical assets related to roadways such as road lanes, road edges, shoulders, dividers, traffic signals, signage, paint markings, poles, and all other critical data needed for the safe navigation of roadways and intersections by autonomous vehicles.  Referred to as high definition (HD) maps they will record the location of physical highway assets to centimeters.

These HD maps have two applications in the autonomous vehicle market.  They can be used by autonomous vehicles in actual driving to augment on-board sensors with near real-time and highly accurate data of the highway and surrounding location where the vehicle is driving.  Secondly because self-driving vehicles would need to travel billions of miles in the physical world to demonstrate a significant improvement on safety for human drivers, simulation has become an essential part of the development of autonomous vehicles.

 At the 2016 SPAR3D conference I had a chance to chat with Ron Singh, then Chief of Surveys at the Oregon Department of Transport about high definition (HD) maps of highway systems.  High definition means accurate to centimeters and Ron believed that HD highways maps would be required to reduce the risk and costs of highway construction.  But he also believed that autonomous vehicles would require this information.  Today autonomous vehicles rely completely on on-board sensors. but many believe that for safe operation they will require other information that can only come from external HD maps of highways and roads.

At the Year in Infrastructure 2017 (YII2017) conference in Singapore, Sharad Oberoi of Sanborn gave an insightful overview of the state of the art for automated feature extraction from combined aerial and mobile scans of highways and adjacent areas. Sanborn develops high precision maps for the autonomous vehicle market.  Sanborn has developed proprietary mapping technology that leverages aerial imagery, aerial lidar data, and mobile lidar data to create standardized, high-precision 3D base-maps focusing specifically on the self-driving vehicle market.

Recently in the UK Zenzic has commissioned the Ordnance Survey (OS) to explore the geospatial data requirements for HD maps when considering the lifecycle of testing and the data interoperability requirements to enable UK plc to provide an exemplar test facility ecosystem and set the foundations for operational deployments.  HD maps for autonomous vehicles are more complex than maps used for simple driver-based navigation systems. Mapping mustbe highly accurate (better than 5cmin resolution) and needs to contain a minimum set of road information, such as lane-level geometry and information relating to street furniture.This study has been conducted with the support of software simulation companies, the Met Office and the British Standards Institution(BSI), with the aim of understanding the data requirements, gaps and sources regarding geospatial data for self-driving vehicle testing.  A report Geodata report - analysis and recommendations for self-driving vehicle testing has been released.  It addresses why self-driving vehicles require real-time high precision maps. With high-definition maps which are updated in real-time, a self-driving vehicle is able to reference the position of other road users against what it already knows to be there. It also provides a back-up in situations where its sensors are unreliable such as poor weather conditions such as snow, heavy-rain or sun reflecting off a wet-road. 

Its key conclusions are that geospatial data is fundamental for autonomous vehicles. Maps can provide an important trusted baseline where the on-boardd sensor feed are unreliable.   To provide the framework required for the testing and driving of self-driving vehicles, current mapping specifications will need to be enhanced to include relevant street-level features, with better than 5cm resolution, more extensive information about street furniture, and interoperability with other sources of data.  An authenticated, authoritative single source of geospatial data accessible to autonomous vehicles is required. Standards relating to geospatial data in the context of autonomous vehicles are required. This is on the Open Geospatial Consortium radar. Real-time updates to mapping through sensor data feeds will also be essential.  Finally it makes some recommendations about the development of data formats (LAS 1.2 or LAZ, OBJ, OpenDrive, and ESRI shapefile), data hosting, governance, minimum safe requirements and standards.

British standard PAS128 for underground utility location to be updated

Belgium underground gas line explosion during excavation 2003

As we are reminded by the recent underground utility hits in San Francisco, CA, Durham, NC, and Calgary, Alberta, not knowing the location underground utilities represents a serious risk for the public. There are 400,000 hits per year in the U.S. alone.  Standards for reporting the reliability of the locational information about underground utilities have been in place for decades but these standards do not reflect recent advances in underground utility detection technology. In the U.S. the 2003 ASCE 38-02  ,which is been used for classifying the quality of location information about underground infrastructure, is widely seen as being out of date. In France the 2012 presidential decree defines three explicit levels of cartographic accuracy for underground structures: A - less than 40 centimeters, B - 40 centimeters to 1.5 meters, and C - greater than 1.5 meters. In Canada the CSA S250 (2011) standard also specifies absolute precision with three different quality levels for exposed utilities and one for remote sensed detection. In the UK the 2014 Publicly Available Specification (PAS) 128, developed under the auspices of the British Standards Institution (BSI) and sponsored by the Institution of Civil Engineers (ICE) and others, not only includes the A,B, C, D quality levels of the U.S. standard, but extends it with explicit precision levels B1 to B4 similar in this respect to the French decree and the Canadian S250 standard.  I have just had a chance to chat with Andy Rhoades who was responsible for locating and mapping underground infrastructure at Heathrow airport for many years and is now leading the process to update PAS128.  Andy told me that this process will kickoff in a couple of weeks with a target of completion roughly six weeks after that.

Just as a reminder PAS128 defines quality levels A through D based on the type of survey used to locate underground utilities.

QLD - Desktop search techniques were used to identify existing utility data by identifying known utility owners within the survey area, requesting asset information from each of them, typically as-builts, and compiling the collected information.  Locational accuracy generally poorly known.

QLC - Requires site reconnaissance to identify above-ground visible physical features that are indicative of and help locate utilities within the survey area. Locational accuracy undefined.

QLB - For this quality level PAS requires that at least two remote sensing techniques must be used in detecting utilities.  Electro-magnetic locating (EML) and ground penetrating radar (GPR) are required, but others may be used in addition. Most EML systems only detect metal, do not have the capability of digitally recording what was detected, and can only provide rough estimates of depth.  GPR is able to provide 3D location, is not able by itself to identify the type of utility, and does not work well in certain types of soils such as clay.  Post-processing is often used with GPR to improve the interpretation of GPR data by resolving weak and intermittent signals or distinguishing multiple targets.

In addition PAS128 breaks the QLB level into several sublevels with specified absolute precision depending on the results of remote sensing.

QLB4 -  A utility cable or pipe is believed to exist but has not been detected. Locational accuracy undefined.
QLB3 - Only the horizontal location of the utility has been detected by one of the geophysical techniques used. Locational accuracy ±500 mm (XY),  Z locational accuracy undefined.
QLB2 - Horizontal and vertical location of the utility was detected by one of the geophysical techniques used. Locational accuracy for X,Y ±250 mm or ±40% of detected depth whichever is greater, for Z ±40% of detected depth.
QLB1 - Horizontal and vertical location of the utility was detected by multiple geophysical techniques. Locational accuracy for X,Y ±150 mm or ±15% of detected depth whichever is greater, for Z ±15% of detected depth.

QLA - Involves exposing the target utilities within the survey area by excavation to confirm and record the location and other attribute data.  Locational accuracy for X,Y ±50 mm, for Z ±25 mm.

In contrast with the standards in other countries PAS128 reflects the advances in underground detection technology - hardware and software - that have occurred in the last few years.  That it is now being updated suggests that PAS128 will continue to stay with the underground detection technology curve.

AR enables better grid damage assessments and improves quality of utility GIS

Maintaining accurate, up-to-date GIS data has been a challenge for utility and communications firms for decades.  Inefficient CAD/GIS workflows result in utility GIS quality degrading with time.  Now with business drivers such as implementing a smart grid, determining service availability and other applications making GIS quality a top priority at an increasing number of utilities and communications firms, new technical solutions are being applied to implement new business processes that raise the quality of utility GIS data over time.  These include CAD/GIS integration; integrated engineering design, GIS and enterprise services; extensive geospatial open source libraries  for viewing, editing and analyzing geospatial and other data; and reality capture often together with AI for inspections, distribution and transmission vegetation management, asset inventories and creating network data models. Another example of a technical advance that can contribute to improving GIS quality over time is a recent proof of concept by Duke Energy, Verizon and EPRI in which augmented reality (AR) was applied to improve the reliability of damage assessments after major events.  While successful in making damage assessments more reliable, Duke found an unexpected result that AR also improved the locational accuracy and description of assets in the GIS.

Broken business processes degrades GIS data quality

 Over the years the lack of CAD/GIS interoperability has cost communications and utility companies hundreds of millions of dollars trying to cleanup their GIS data and streamline their CAD+GIS workflows.  For many utilities and communications firms traditional GIS/CAD business processes, which have been the standard approach for maintaining utility GIS for the past few decades, has simply not been able to maintain cost-effective, real-time, high quality asset location and associated data.  Instead GIS data quality tends to degrade with time.

Challenge: improving the reliability of damage assessments

At Distributech 2019 Aleksandar Vukojevic of Duke Energy together with Paul Giers Duke Energy, Micah Tinklepaugh EPRI, Norm McCollough EPRI, and John Simmins (now Associate Provost for Research and Economic Development at Alfred University) reported on a proof of concept at Duke Energy the primary objective of which was to apply AR to improving the reliability and trust-worthiness of damage assessments after major disasters, but found an unexpected benefit in contributing to improving GIS data quality.

The objectives of the trial were to investigate the application of AR to reduce errors in identifying and characterizing missing and damaged equipment and as a result improve trust in the assessments by the work crews assigned to carry out the repairs required to restore power. It was envisaged that AR could improve damage assessments by visualizing before-storm images over post-storm damage thereby simplifying identifying missing or damaged assets. Currently field technicians visit the area of the outage and use paper and pencil to assess and record damage. Some of the challenges associated with this process is that field technicians, especially less younger, less experienced ones or office staff seconded to the field, may not be knowledgeable about the secondary power grid and in assessing damage to it. The result is missed and incorrect identification of missing or damages equipment. In addition paper processes are slow and error prone making ordering the required replacement equipment a major source of delay in restorating power.  The result is a lack of trust in damage assessments by field crews.

Proof of concept: Applying AR to improve reliability of damage assessments

The trial involved a safety helmet mounted RealWear HMT-1 Android computer with an eye-level display and microphone. It incorporated a GPS so that location was recorded automatically. The HMT-1 was connected via WiFi to router in a truck that connected via a Verizon wide area network to a cloud server.  The field engineer views the "before picture" based on the location data and is guided to align the high resolution, stabilized HMT-1 camera to match the same view to capture a corresponding "after picture". Voice commands and voice responses to damage assessment questions presented in the application are utilized for a fully hands-free user interface. As the engineer performs the work, data is immediately sent to the back office for evaluation and action. The images and data are used to create a list of equipment and inventory required to restore missing and damaged equipment.  The trial did not involve an actual outage but simulated a major outage.  17 different sites were involved in the exercise. 

Results of the proof of concept

Early results indicated that nearly 100% accuracy rate was achieved in damage assessments using AR.  This represents a 50 % improvement over the existing, paper-based process. This resulted in much greater trust by work crews in the damage assessments.  In addition financial benefits were estimated to be significant. The first 12 hours response spent manually recording and transferring assessment reports was sped up dramatically. It was estimated that for an outage affecting 250,000 customers over a four day period, the time to assess and restore power could be reduced by 12 hours with a cost savings of over $ 8 million. One of the big advantages is that training only took 10 minutes even for a worker with no damage assessment experience.  Assessments were delivered to the back office in near real-time, much faster than for paper reports and eliminating the "middle man" who transcribed paper reports into a computer based system reduced the number of errors.

Side benefit of using AR: Improved GIS data quality

While Duke assessed the proof-of-concept to be successful for damage assessment, they also reported an unexpected but important result of the proof of concept.  The process using AR for identifying assets helped improve the quality of utility GIS data compared to the existing paper-based process that could actually result in degrading the quality of the GIS.

In conclusion Duke was extremely happy with the proof of concept. Achieving 100% accuracy even for inexperienced assessors impressed the Duke Energy team and created much greater trust by the work crews in the assessment reports. But AR had an important side benefit - Duke found that using AR to accurately locate and identify assets had the potential to contribute to improving the reliability of asset data stored in the GIS.  This is another example of the application of technology which change business processes and result in improving the quality of GIS data over time.

Machine learning cost-efficiently creates high quality utility GIS data for smart grid applications

Many utilities' GIS contains only a small part of what is required for managing a smart grid.  Often even the data in the GIS is too unreliable to use for smart grid applications.  For example, utility GISs may not include which customers are connected to which transformer, or if they do include this information it is incomplete or incorrect.  Many utility GISs cannot identify the phase(s) each customer is using and they may not include network topology - what is connected to what.  Other missing data may includes street lights, distributed energy generation such as rooftop PV panels, the type and condition of poles, joint use of poles, and encroachment.  To correct these problems typically requires a foot survey which is expensive.  For example, a pole inventory costs at least $20 per pole.  At the Open Geospatial Consortium (OGC) Energy Summit at EPRI in Charlotte, John Simmins of EPRI described the research he is leading at EPRI to use modern technology including machine learning to reduce this cost to pennies per pole using available imagery such as Google Streetview.

I began working in the utility and telecom sector in 1993, specifically in developing software for records management (called network documentation outside of North America), and at that time the magnitude of the AEC+geospatial cultural mismatch was just beginning to be appreciated by utilities and telecoms. Planners tend to use GIS tools, engineers and designers CAD tools, construction folks in the field paper CAD drawings, and asset managers FM, GIS or integrated tools. Fashioning AEC and geospatial data into an efficient data flow from planning through design and construction to operations and maintenance represented a challenge that remains a problem for utilities.  For many utilities the backlog of as-builts and updates waiting to be entered into the GIS stretches into months, with the result that GIS data is permanently out of date inhibiting management and field staff from relying on this data operationally.  The Between The Poles blog is over ten years old and one of the persistent themes from its very beginning in 2006 was the challenge of integrating CAD and GIS data in a common workflow (some examples; 2006, 2007, 2008 ).

There are signs that modern technology is beginning to offer a solution to the problem of developing and maintaining an accurate digital model of electric power grids.

I wrote recently about a utility in western Pennsylvania that intends to implement an Advanced Distribution Systems ( ), but realized that to this requires a network data model. Duquesne Light is typical of many North American small to medium electric power utilities.  It does not have an outage management system or an electrical data model which means it is difficult to connect customers to transformers and to determine upstream devices when customers report power problems.  Currently Duquesne Light's circuit maps are AutoCAD drawings.  Duquesne Light is moving to modernize its grid and plans to deploy an Advanced Distribution Management Systems (ADMS) including outage management (OMS), DMS and DSCADA.  But the ADMS will require developing a complete distribution electrical data model and to that in the traditional way can be expensive.

To quickly, safely and cost efficiently develop a network data model, Duquesne Light looked at several alternatives including reality capture technology.  A pickup truck was equipped with with side mounted photo and IR cameras and LiDAR laser scanners. The reality capture survey could be completed rapidly and safely, with no boots on the pavement  Actual development of the data model was completed in the office without the necessity of going to the field for quality checks because the imagery provided all the data that was required to create and verify the data model.  Duquesne Light found several significant advantages of the reality capture approach.  The first was asset accuracy - both location and asset attributes.  In addition the captured imagery could also be used for other purposes.  For example, it could be used for a joint-use audit.

But this is still basically a manual process requiring a human being to scan images.  At the Energy Summit John Simmins described his research using neural network technology to automatically identify poles and attached equipment from available imagery such as Google Streetview.  Normally applying neural networks requires large quantities of training data.  To reduce this requirement, John has evaluated "biologically inspired", forward pass, neural networks.  In this approach the algorithm first looks (foveates) for vertical things that have the characteristics of a pole, and then for the characteristics of equipment attached to the pole, for example, cross members, street lights, transformers and large cables.  From this analysis is possible to determine pole location, height and width, cross member position on the pole and projected length, street light support strut position and length, position of transformer and large cable connection locations.  The ultimate goal of the research is in addition to automatically detecting each element of equipment to identify the type, size and manufacturer of each piece.

This is not a simple problem and there is no silver bullet.  Using a standard neural network approach requires large volumes of training data.  For poles it requires images of poles with different types of damage under different lighting conditions and in different types of weather.  An Australian company SiteSee claims to be able to do this for communications towers and transmission pylons - identifying the type and manufacturer of each antenna or insulator on a tower or pylon and comparing it to the manufacturer's specification to detect signs of corrosion and other types of damage.  When what you are looking for has a distinctive geometry that differentiates it from the surroundings, a biologically inspired approach may provide a way of identifying equipment without large volumes of training data.  Another area where several different approaches have been applied to automate feature extraction is electric power transmission lines for vegetation management.

Creating a digital twin of an electric grid is essential for developing and maintaining a smart grid.  Utilities have been wrestling with this challenge for decades and in many cases their GIS data is still not much closer to what is required.  If it would be possible to use the technology that John Simmins is investigating or other technology to reduce the cost of improving the quality of their GIS data, it would accelerate the adoption of smart grid technology at many utilities.  In his presentation at the Energy Summit Jared Green described his research in using smart meter and AMI network data to map secondaries from the transformer to the home, identify sources of unmetered load, and use impedance data from smart devices to correct utility GIS data.  Given the history of the challenge to develop and maintain accurate GIS data at many utilities, these approaches represent a beacon of hope which may enable utilities to cost effectively develop a digital twin of their networks that is an essential prerequisite for a  smart grid.