Between the Poles

CSA S-250 one of the first national "as-installed" standards for underground infrastructure

In Canada the goal of the Canadian Standards Association (CSA) S250 Standard, first published in 2011 and revised in 2020, is to specify mapping requirements for the recording and depiction of newly installed underground utility infrastructure and related structures. It is intended to promote communication and data sharing throughout the construction lifecycle of projects and asset life.  Specifically it is intended to improve the reliability and accuracy of underground utility infrastructure mapping records and supporting data.  Accurate records provide greater safety to the public and construction workers by decreasing utility infrastructure line hits and strikes and by augmenting and enhancing damage prevention programs, such as state and provincial one call programs and initiatives by contractors, utility owners/operators and state and provincial transportation agencies. CSA S-250 has been adopted by agencies such as the Ministry of Transportation of Ontario and the Ontario transit authority Metrolinx and utility owners such as Enbridge. The best practices guide developed by the Transportation Association of Canada (TAC) recommends that "as-builts" submitted for newly installed and relocated infrastructure be compliant with CSA S-250.

The CSA S-250 standard is a precursor to and has much in common with the new U.S. "as-installed" standard ASCE 75-22 developed by the Utility Engineering and Survey Institute (UESI) and already in use in Colorado and Montana.  In particular the horizontal and vertical positional accuracy levels adopted by ASCE 75 are shared with S-250 and while not identical are very close.

CSA S-250

The CSA S-250 standard specifies 3D mapping requirements for the recording and depiction of underground utility infrastructure and related attributes at or below grade. Unlike the ASCE 75 standard it does not apply to above grade infrastructure such as poles and overhead wires.  It was one of the first standard to specify levels of positional accuracy - for both horizontal and vertical dimensions. Throughout the standard "shall", "should", and "may" are used consistently to distinguish mandatory, recommended, and optional requirements.

What's new in CSA S250:20 ?

New field locating and mapping technologies are being developed and existing technologies evolving.  The latest version of the standard has expanded the recognized technologies to include LiDAR, total station survey (TSS) and video recording for supplemental records.  The requirements for mapping records has been expanded to include all stages of operational status; proposed, existing, abandoned in place, retired, or reserved for future use. The challenge of digital base mapping is explicitly discussed, because in North America in general every utility has adopted a different base map. Clarification is provided on how information is to be collected and used to depict the location and attributes of utility infrastructure so users can be confident of its level of reliability and accuracy. The owners' responsibility for measuring and recording the location of underground utility infrastructure has been revised.  The requirements for absolute spatial positioning (i.e., horizontal and vertical datums) have also been revised. The importance of open data standards, for example the Open Geospatial Consortium's MUDDI standard, that enable data sharing between industry partners has been explicitly recognized.

The revised standard specifies that field records and data "shall" be collected contemporaneously during construction. The owner "shall" define the type and format of the field records collected.  Field records "should" be the primary source of mapping information.  Furthermore, anyone who discovers what appears to be a significant discrepancy during construction, excavation, SUE activities, and locating "should" report the discrepancy to the owner and/or source of the record in a timely manner.

Example of petroleum/gas distribution system

As a concrete example of the level of detail that is provided by the standard, for a liquid petroleum or gas pipe distribution system, the standard specifies a minimum set of properties that "shall" be captured and others that "should" be captured.

Mandatory
horizontal and vertical position and accuracy levels
cross-sectional size of pipe
material type of pipe
material of casings
pipeline coating
cathodic protection

Recommended
Valves, tees, fittings and plugs
Compressor stations, pressure reducing stations
Pipeline maximum operating pressure

Symbolization

A solid line "should" be used to depict existing infrastructure, an "x" though the solid line "should" be used to depict abandoned facilities, hatching "may" be used to depict proposed infrastructure.

This post is based on Lawrence Arcand's talk at the Canadian Underground Forum (CUF). You can listen to all the talks at CUF on the GeoIgnite CUF Youtube channel.

New digital "as-installed" standard for underground and above-ground utility infrastructure

Since 2002 there has been a standard, ASCE 38, for investigating and documenting existing underground utility infrastructure.  Now in the US a new standard ASCE 75 is imminent that will define a standard way to record and exchange information about newly installed infrastructure. This means that in the future all underground infrastructure can be assigned a quality level, either under ASCE 38 or ASCE 75.

Developed by the ASCE Construction Institute (CI) and Utility Engineering and Survey Institute (UESI) the new "as-installed" standard is currently available for public comment through January 6, 2022. It has already been adopted in Montana and Colorado as the "as-constructed" standard for new infrastructure in state right of ways. Recently UESI and the Centre for Advancement of Trenchless Technologies sponsored a talk by Phil Meis, one of the authors of the ASCE 75 standard.

ASCE 75 is applicable to newly installed, repaired, relocated and exposed infrastructure, both below and above ground.  The standard specifies the minimum spatial, 3D geometry, and feature attribute data to be captured for newly installed utilities. Unlike ASCE 38-02 it defines quantitative levels of positional accuracy - for both horizontal and vertical dimensions. It defines a basic set of data that provides a foundation for utility engineering and coordination, network asset management, and the exchange of utility facility information among stakeholders.

Most importantly ASCE 75 is a digital standard, which means that it is consistent with and supports digital construction design standards and delivery practices such as building information modeling (BIM) and virtual design and construction (VDC).

What does it mean for the public right of way owner ?

It makes it possible for the public right of way owner, typically a state Department of Transportation and their designers and contractors, to require advanced 3D digital technologies such as BIM, conflict detection, and reality capture for improved utility coordination with 3rd parties including utility owners. The major benefit is reduced construction risk, time, and costs, and more on-time project completions.

What does it mean for the utility owner ?

For utility owners the benefits of the new standard dwarf the incremental cost.  Standardized recording and retrieval of utility infrastructure data and the ability to exchange information about underground and above-ground utilities among stakeholders provides for enhanced damage prevention and reduced risk to existing utility infrastructure  As a digital standard It enables for more focused and effective utility investigations and conflict assessments. In general it will provide a foundation for more effective utility asset management and more resilient and sustainable infrastructure.

Keeping utility GISs up to date using automated real-time reality capture

Maintaining accurate, up-to-date, comprehensive data about assets, above ground and below ground, has been a challenge for utility and communications firms for decades. Now new technologies including machine learning and computer vision - based feature extraction and text recognition and high accuracy location capture using GPS and augmented reality clouds are being applied to automate the real-time capture of utility network information in the field during construction, when new utilities are installed and when utilities are exposed and relocated.  When fully implemented this will automate real-time maintenance and update of utility network information and form the foundation for utility digital twins.

Background

The decades-old as-builting process remains marred by manual paper-based processes that is characterized by inefficiency that causes critical processes such as outage restoration to require more time than they should and low data quality which leads to incomplete, out of date and inaccurate underground utility records that are responsible for frequent construction delays, injuries and fatalities for workers and the public, and a drag of billions of dollars on national economies.

One of the persistent themes from the very beginning of the Between The Poles was the challenge of integrating CAD and GIS data in a common workflow (some examples; 2006, 2007, 2008 ).  Critical metrics for utility readiness for the smart grid is the as-built backlog, field update backlog and the location accuracy of the assets in utility GISs.  As-built backlogs typically stretch into months, updates from the field take months to be entered into the records database (if they are entered at all), and the recorded location of assets in the GIS can be meters and even tens of meters from their actual location.   The challenge is to replace conventional paper-based workflows with real-time digital work flows that can provide the basis for utility digital twins.

Real-time reality capture using machine learning and augmented reality clouds

New technologies that have been developed for consumer-based applications include machine learning for image and text recognition and more recently augmented reality clouds for recording accurate relative locations and distance measurements are now being applied for capturing utility network information.  This week at the Mirrored World: Role of Digital Twins in the Utilities Industry virtual event, Skye Perry and Peter Batty of SSP Innovations, discussed and demonstrated how these technologies are being applied to the efficient, real-time reality capture of information about utility network facilities in the field during construction. 

Peter Batty demonstrated functionality being developed for a new product SSP Vision that supports the semi-automated capture of information about newly installed electric network facilities.  At an actual construction site Peter demonstrated automated feature recognition of electric and telecom pedestals, automated capture of attribute information from cabinets, scanning bar and QR codes and text for work order number, serial number, manufacturer and other attribute information, and using augmented reality + GPS to record the location of equipment and linear features such as a cable from the pedestal to the transformer.  All of this information was captured and pushed up in real-time and was available in seconds in the utility GIS.

The key features that are being developed for SSP Vision are automatically recognizing equipment such as pedestals and cabinets, automatically detecting and capturing attributes from bar codes, QR codes and text recognition using computer vision and machine learning. The recording of accurate locations is enabled using augmented reality clouds and GPS.  Augmented reality clouds add much greater relative accuracy including distance measurements and work both outdoors and indoors.  At this point properties like feature type can be specified manually if automated feature recognition is unable to detect the type of feature or if a new feature is being entered as part of a new design being carried out using AR in the field. AR also enables capturing linear features with support for snapping to features such as pedestals and cabinets.

Foundation for keeping a utility digital twin up to date

This is truly ground-breaking and shows how these technologies can be applied now in a semi-automated fashion to dramatically improve the efficiency of capturing location and attribute information about utility facilities during construction. The longer term vision is that the capture of this information could be fully automated by simply mounting a camera on a construction crew member's hard hat and feature recognition and attribute identification and capture fully automated requiring no manual input from the crew. This will automate the real-time maintenance and update of utility network information and form the foundation for utility digital twins.

Announcing Subsurface Utility Mapping Strategy Forum (SUMSF), Mar 22-23, 2022

The Subsurface Utility Mapping Strategy Forum (SUMSF), Mar 22-23, 2022 is a virtual conference that brings together individuals and organizations to share information about and contribute to improving the mapping of underground infrastructure. This is a highly specialized executive-level audience from organizations in both the private and public sector. The priority use cases are reducing damage to underground utilities during construction and the development of subsurface digital twins. The focus will be on innovative strategies involving policies, legislation, regulation, new technologies, business best practices, and training and education for improving mapping of underground utilities.

https://sumsforum.com/

Identifying areas of high risk vegetation encroachment on power lines from satellite imagery

Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation. Vegetation encroachment initiated the massive North American Northeastern outage of 2003. With the growing incidence of extreme events managing vegetation near power lines is becoming increasingly crucial in tackling potential fire hazards, securing infrastructure increasing resilience. The traditional approach to identifying areas of high-risk vegetation encroachment involves helicopters and ground crews and is time consuming and costly. Drones have been used to reduce the cost of data collection, but it is still a costly process. Now an innovative startup is using satellite imagery together with deep learning to identify high risk areas of vegetation encroachment. By applying machine learning algorithms to high resolution satellite imagery, it is possible to distinguish vegetation from other objects, differentiate trees from grass and bushes and identify trees at risk of encroachment. Comparing a drone-based approach with the satellite imagery methodology reveals that the latter approach is highly accurate in identifying high risk areas and significantly less costly than a drone-based process.

Background

Power line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilience. They are generally performed by manned helicopters or by a ground crew. Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a power line (fall ins) or trees that are encroaching on power lines (grow ins). These inspections are mandated by NERC in North America and are not optional. Beyond visual line of sight (BVLOS) drones have reduced the cost of collecting line inspection data. Now a new company is reducing the cost of identifying trees at risk by analyzing high resolution satellite imagery to identify high-risk areas, thus reducing the total length of power line that requires more detailed inspection with drones, helicopters, or ground crews.

Evolving machine learning models at Spacept

A Swedish company Spacept has developed a vegetation management solution that applies machine learning models to multi-band satellite imagery to automatically identify areas of high risk for vegetation encroachment on above-ground transmission and distribution lines anywhere on the planet. Spacept develops its own in-house training data for machine learning models which enables follow-up ground inspections of areas of high risk to develop pruning and other response programs. The training data is constantly enhanced with feedback from new projects for continuous improvement of the models.

Satellite-based vegetation encroachment risk assessment

The key business benefit of Spacept’s satellite imagery-based solution is automating the process for identifying the high-risk areas that require ground-based inspection, whether helicopter, drone-based, or ground crew, thus significantly lowering the cost of vegetation encroachment inspections. Spacept’s satellite-based vegetation encroachment technology makes it possible for power grid operators to monitor vegetation encroachment over their entire infrastructure in a cost-effective manner. Spacept uses the most recent, highest resolution (0.50 – 0.30 cm) satellite imagery commercially available which satisfies the requirements for vegetation encroachment detection. Spacept's approach makes it possible to monitor hundreds of thousands of kilometers of power lines every year anywhere in the world.

Workflow

The workflow of identifying and downloading satellite imagery, applying machine learning for feature identification, and the assignment of risk categories to individual trees is currently a semi-automated process. The longer-term objective is to reduce manual intervention and achieve an end-to-end automated workflow. The current workflow proceeds from a geographical layout of the power line to be inspected in the form of a standard GIS file. The most recent available high resolution satellite images satisfying certain criteria are identified and downloaded from data providers. The criteria typically include panchromatic and multi-spectral bands, resolution of at least 50 cm, vegetation capture with leaf on, and minimal cloud coverage. The downloaded images are fed to the current vegetation model which automatically extracts features and distinguishes trees from other man-made and natural objects. One of the important capabilities of the model is that it is very efficient in differentiating trees from bushes. After the automated processing, the imagery and labelling are reviewed by experienced assessors who review each image and make corrections to the labelling for problem areas, for example, for cases of visually distinguishable bushes or edges of trees. After completion of the labelling step, the distance between each tree and the power line is calculated and a risk category is assigned using risk categories provided by the client. The final deliverable is a GIS file containing the location and the corresponding risk categories assigned to the trees.

In-field case study

To assess the potential business benefits of satellite-based analysis Spacept partnered with infrastructure health management platform Sterblue to apply their satellite-based power line vegetation management solution for thousands of kilometres of a French power grid operator grid. Spacept applied their algorithms to selected satellite imagery to generate a risk file for the targeted segment of the grid. Sterblue assigned emergency (risk) levels based on the distance from the power line using a classification of emergency levels provided by the grid operator. Sterblue then carried out in detail inspections of at-risk areas with its drone inspection fleet. A ground inspection team was sent to verify the locations of the trees physically and collect ground truth. A total of 283 locations were identified. The vegetation at each location was manually validated and was compared with the satellite predictions.

The most important conclusion on the business benefit of the satellite imagery-based analysis is that it was able to identify all the trees that the subsequent ground inspection identified as at risk. In a real-world scenario, applying the satellite imagery analysis as a first step in a power line inspection would dramatically reduce the area and trees that would require ground-based inspection using helicopter or drone-based inspection. As a result of this process, Sterblue reduced its operating costs by 80%.

A more detailed analysis of the satellite data attempted to classify trees into detailed emergency levels U0 to U3. Comparing this analysis with Sterblue's classification was used to create a "confusion matrix" showing instances of agreement and disagreement between the two analyses. It was found that there was good agreement between the satellite and ground-based inspections and analyses.

Conclusion

Several companies have recognized that earth observation imagery is of limited value without the application of analytics to interpret the data. For example, Spacept is applying analytics to the huge amount of earth observation data to provide a valuable high risk vegetation encroachment detection service that is safer, faster, and less costly than ground-based approaches to transmission and distribution line vegetation monitoring.