Between the Poles

Advancing Underground Data Management: Insights from MUDDI, NUAR, and International Collaboration

At the 129th OGC Member Meeting in Montreal this past June, I had the opportunity to chat with Carsten Rönsdorf, the Strategic Product Manager at UK Ordnance Survey. Carsten and I discussed updating this blog, Geoff Zeiss’s Between The Poles, with the progress and initiatives Geoff would have wanted us to share.

Following up on that chat, Carsten got in touch with me this August to share some important updates.

The OGC has now published the MUDDI data model!

The MUDDI model represents a significant leap forward in the integration and management of underground data—an area of critical importance to the readers of Between The Poles. The MUDDI model provides a standardized framework that enhances interoperability across platforms, making it easier for stakeholders to collaborate on large infrastructure projects. You can explore the full details of the MUDDI model here.

Carsten also mentioned the "NUAR for Everyone" guide. This guide introduces the importance of underground data and explains how the MUDDI model can revolutionize data integration. If you're looking to understand how underground data can be effectively managed and utilized across various sectors, this guide is a must-read.

Geoff Zeiss, the founder of this blog and a giant in our field, is rightly acknowledged in the guide for his leadership. The preface in the guide beautifully states:

"Our dear friend and colleague Geoff Zeiss contributed greatly to the community and to the work which led to the MUDDI model, but sadly passed away before we reached the milestone of publication. The OGC MUDDI Standards Working Group wishes to recognize and commemorate Geoff’s contribution to this work."

You can read the guide here.

Reflecting on these developments, I can’t help but think about how much the publication of MUDDI would have pleased Geoff. Geoff was not only one of the founders of the OGC but also a passionate advocate for the better management of underground data. He dedicated his career to advancing geospatial technologies and fostering international collaboration. I still meet people in the sector who tell me how much Geoff’s leadership and support helped them.

Geoff asked me to keep this blog going before he passed away. To do that, I need your help. Like Carsten, please reach out to me to share news and updates on underground infrastructure.

This fall, we continue to honor Geoff’s memory and contribution by expanding the SUE (Subsurface Utility Engineering) program at Canada’s National Geomatics Expo to discuss underground infrastructure mapping. The program, chaired by Peter Srager, will take place in Calgary on October 29-30th. You can find more details here.

Additionally, the SUMF (Subsurface Utility Mapping Forum) that Geoff and I started continues online. If you would like to give an online talk, get in touch. The forum will be back soon: https://sumsforum.com/.

Jonathan Murphy
Founder, GoGeomatics Canada

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.

The future is fewer maps

At the first Longitude meetup in the U.K., organized by Ben Flanagan, Kenneth Field, and Chris Wesson, Ed Parsons presented his perspective on one of the directions to expect from geospatial in the future.  It was a bit counterintuitive, for consumers more geospatial data and technology, but fewer maps.

To start with he reminded us that the public has never had as much access to maps as we do now thanks primarily to Google Maps - either as standalone or embedded in other apps. What he foresees is that in future we will be seeing fewer maps or no maps at all.

The maps we have used up to now are large scale maps presenting a large scale view - probably very useful to Winston Churchill and people planning things on a large scale. But in our day to day life, we don't need maps at this scale. Instead we need to know what we will encounter in the next 15 minutes, whether that be the next exit off the highway or the next grocery store. There will be more interaction between us and the world around us - for example showing which of the nearby bars or coffee shops are very busy or less busy.

Ed gave a practical example that anyone who uses a smart phone to navigate in a place they have not been before can relate to. When you come out of a tube station or any GNSS-denied place you are always faced with a quandary - how to orient yourself to know whether to walk left or right. I mentally flip a coin and walk until my smart phone tells me if I am headed in the direction I want to go. If I am in a part of the city where GNSS is unreliable I walk to the end of the block and the street number or name usually will tell me if I am headed in the right direction or that I have to retrace my steps in the other direction.  In the future your navigation app won't rely solely on GNSS signals but will recognize buildings and give you the right direction. In fact this could all happen through an audible interface so that you needn't look at a map at all, "turn right as you leave exit 3 from the tube station".  Of course this will require a lot more spatial data about the city you are in and will require some serious geoanalytics.

What this suggests is that as consumers we will use more geospatial data and analytics, but it will become part of the technology stack and most of us won't realize how much geospatial technology we are using.

Mapping indoors with a smart phone

At the Spar 3D Expo and Conference 2016 in Houston, Texas, Avideh Zakhor from the University of California Berkeley showed remarkable products from her startup Indoor Reality.  I blogged previously about the technology when it was still under development.  The indoor mapping and modeling system she demonstrated was a 32 pound backpack with a collection of sensors as well as a handheld device.  The most important feature of the technology were sensors and algorithms for accurately tracking the six degrees of spatial freedom which determine the location and orientation of the backpack in a building providing the basis for generation of floor plans and Revit models (Late last year Indoor Reality released a Revit scan to BIM plug-in that automatically identifies walls and semi-automatically doors, windows and staircases to produce a basic BIM model.).  Recently Indoor Reality has released two handheld products for indoor mapping one of which simply uses an iPhone or Android phone and a subscription for cloud processing to create floor plans of multi-room indoor spaces.

The IR-100 software for the iPhone or Android phone enables you to scan a multi-room space and using cloud-processing to create floor plans in svg and dxf format, 3D mesh in ply format, and a summary report of area and volume. There is a 2D web visualization interface that enables you to see your progress as you take photos. You can also make measurements on the floor plan or on the captured images using this web interface.

The IR-200 system goes a step further enabling 3D visualization. Indoor Reality provide a light weight rig and panoramic camera that uses a smart phone to scan a multi-room space and then cloud processing to create floor plans in svg and dxf format, a 3D mesh in ply format, and a summary report of area and volume. It includes a 3D web interactive visualization interface, that enables 3D virtual walk throughs of the space that was photographed and measurement of distances on any web enabled device such as a smartphone or a laptop.

Ordnance Survey releases vector basemap optimized for national overview to street level

The Ordnance Survey has just released OS Open Zoomstack for public feedback. OS Open Zoomstack is very impressive because it is a vector basemap optimized for different scales - from a national overview to street level detail. It's available in GeoPackage, PostGIS Export File, and Vector Tiles (based on Mapbox’s open specification) and via a Vector Tile API. And it's open. Furthermore, it's a way to get your toes in the water for the upcoming Open MasterMap.

Accurately surveying utilities exposed during construction using a smartphone

The existing documentation about the location of underground utilities is poor in many countries and the costs associated with utility strikes, hitting a utility such as a fiber-optic cable, electric power cable, or a gas or water main during construction is high. Therefore, finding a way to improve our knowledge about the location of underground subsurface infrastructure is essential, but to be practical it has to be a way that does not add significantly to the cost of construction. At Geo Business 2018, Richard Bath, a surveyor at Costain, described an efficient way of capturing enough information with an ordinary smart phone to accurately survey, map and make available via the web the location of utilities encountered during a construction project.

About 4 million excavations are carried out on the UK road network each year to install or repair buried utility pipes and cables. Not knowing the location of buried assets causes practical problems that increase costs and delay projects, but more importantly, it increases the risk of injury for utility owners, contractors and road users. The problems associated with inaccurate location of buried pipes and cables are serious and are rapidly worsening due to the increasing density of underground infrastructure in major urban areas. The average direct cost of hitting an underground utility during construction ranges from £400 to £2800 depending on the type of utility. Research has found that the total cost of a utility strike in the UK is on average 29 times the direct cost.

Performing a survey of utilities exposed during a construction project using a laser scanner and total station can be cost prohibitive and as a consequence is rarely done. A research project involving Costain and Bentley has found that photogrammetry using a consumer grade smartphone results in a 3D model of comparable accuracy to a laser scan survey and is much more cost efficient. They have also found a way to make 3D models created from the pictures captured with a smartphone available over the web.

Richard described a simple workflow to capture enough information with a smart phone to accurately determine the location of underground utilities.

• Step 1 Mark ground control points (GCPs) around the area. They have to be visible in the pictures taken with the smartphone.
• Step 2 Take pictures from varying angles and heights around the exposed utilities with a smartphone.
• Step 3 Survey the GCPs with a total station, at least three are needed to accurately determine the location of the 3D model created from the pictures.
• Step 4 Upload the photos taken with the smartphone and process them and GCPs together to create a georeferenced 3D model of the exposed utilities.

This process has been found to result in a 3D model of comparable accuracy to a full laser scan survey and unlike a laser scan survey it is something that anybody can do. Typically it involves taking 40-60 pictures with an ordinary smartphone. After uploading the pictures, processing them with Bentley's ContextCapture software to create the georeferenced 3D model, the resulting 3D model can be made available to others on the construction project using an online web link that allows the 3D model to be displayed along with existing 2D utility as-builts.

Deep learning enables automated extraction of building footprints and road networks from satellite imagery

Automated feature extraction from satellite imagery has made major progress in the last year. Accurate building footprints extracted from high resolution satellite imagery are available from companies such as Ecopia (which has just announced a partnership with DigitalGlobe).  Also NVIDIA has demonstrated the ability to automate detection of many road networks using sophisticated algorithms and multi-spectral high resolution imagery.

Building footprints

SpaceNet is a repository commercial satellite imagery and labeled training data being made available through Amazon Web Services at no cost to the public to foster innovation in the development of computer vision algorithms to automatically extract information from remote sensing data.  It is the result of a collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA.  Two competitions for automated building footprint extraction from high resolution satellite imagery and a third for road network extraction have been held based on SpaceNet data. 

The first SpaceNet Challenge was launched in November 2016.  Imagery of Rio de Janeiro from the WorldView 2 satellite at 50cm resolution using eight spectral bands was made publicly available on Amazon Web Services (AWS).  42 developers competed to create algorithms that extract building footprints from the imagery.  The source code of the winning algorithms was made available on the SpaceNetChallenge GitHub repository. 

The winning implementation was developed by Brazilian developer using an implementation based on random forests with brute force polygon search and did not leverage deep learning frameworks.  The organizers' conclusion based on the results of Round 1 was that automated building footprint extraction remained a challenge.

Round 2 winner. Most building footprints are precisely detected. Number on each building footprint represents the overlap between the prediction and the ground truth.

Round 2 of the SpaceNet challenge involved higher resolution 30 cm imagery from WorldView 3 for four cities across the globe. The competition challenged developers to improve performance from the first competition using the higher-resolution imagery and more geographically diverse training data samples. SpaceNet on AWS held imagery and building footprints for Las Vegas, Paris, Shanghai, and Khartoum with over 180,000 buildings181,619 footprints.  In addition additional bands were made available (panchromatic, 3-band pan-sharpened, and 8-band pan-sharpened).

Results from Round 2 winning solution: top left: Las Vegas, top right: Las Vegas, lower right: Khartoum, and lower left: Paris. Blue outlines: ground truth, green outlines: correct identifications, red outlines: false positives and yellow outlines: false negatives.

The winner of Round 2 applied a  deep neural network model developed originally for medical image segmentation called U-Net.  Successful training of deep networks requires many thousand annotated training samples.  Training was aided significantly by augmenting the imagery with OpenStreetMap data.  The winning solution used OpenStreetMap layers and Worldview-2 multispectral layers as the input of the deep neural network algorithm.  In Las Vegas, the winning solution was outstanding in outlining suburban homes, but also did well in identifying odd-shaped buildings. In Khartoum, the solution was very good in identifying the footprints of stand-alone apartment complexes, but had trouble with small buildings, with building footprints that are close together, and with horizontally large buildings.  One of the problems was inconsistent ground truth which affected training. The solutions offered by the developers in the competition were sufficiently successful that the organizers concluded that the winning algorithms achieved performance with potential for automated mapping tasks such as keeping maps up-to-date and to assist first responders during natural disasters.  The source code for the winning implementations can be found in the SpaceNetChallenge GitHub repository.

DigitalGlobe has just announced a partnership with Ecopia, which has established an automated process to create building footprints quickly and at scale by leveraging DigitalGlobe's Geospatial Big Data platform (GBDX) and advanced machine learning in combination with DigitalGlobe’s imagery library. The two companies plan to automatically extract accurate 2D building footprints globally, then refresh the datasets periodically to find and track changes over time.  These datasets would be valuable to municipal governments for permitting purposes and for first responders after disasters.   Ecopia is developing a database for all of Australia (7.6 million square kilometers) which will include every building with a roof area greater than 9 square meters.  The database will contain building footprints for about 15 million buildings. The database includes linkages to other datasets including geocoded address, property data and administrative boundaries. Ecopia intend to update the dataset regularly to be able to track changes, especially urban sprawl.

Road networks

In November 2017 CosmiQ Works, Radiant Solutions, and NVIDIA announced a third round competition, “Road Detection and Routing Challenge” to explore automated methods for extracting map-ready road networks and routing information from high-resolution satellite imagery.  

Detecting road networks in Paris. Photo image, ground truth, and the results achieved with three deep learning algorithms

NVIDIA has released the results of several deep learning algorithms that illustrate just how difficult identifying road networks is and the sophistication of the available tools.  These include using multi-band spectral imagery to identify the material properties of the road surface itself (asphalt, gravel, packed earth) and using more sophisticated processing including conditional random fields (CRF), percolation theory, and reinforcement learning. 

Detecting road networks in Las Vegas. Photo image, ground truth, and the results achieved with three deep learning algorithms

NVIDIA utilized high performance GPU compute resources provided by the NVIDIA GPU Cloud and Amazon Web Services.

Excavation equipment equipped with cameras could map underground infrastructure

The challenge of increasing urbanization in a world where anthropogenic pressure on the environment has become threatening is driving the digitalization of urban planning and the design, construction and maintenance and operations of urban infrastructure.  3D maps of the above ground of many cities have been created, but the below ground has been neglected.  In the U.S. it is estimated that an underground utility is hit about every minute.  Underground utility conflicts and relocations are the number one cause for project delays during road construction. There is a growing recognition that the subsurface needs to be included in urban planning and construction to reduce the risks of conflicts with subsurface infrastructure and to maximize the benefits of subsurface resources. 

One relatively inexpensive way to begin mapping underground infrastructure is to record the location of infrastructure uncovered during routine maintenance and other construction excavations.  But using a traditional surveying approach would require having survey crews on standby and it would also delay the work.

An innovative pilot in Chicago led by City Digital, Cityzenith, Accenture, HBK Engineering, and the City of Chicago collects digital images of open excavations, extracts 3D information from them and shares the information. When local engineering firms and utilities excavate in streets or sidewalks, workers take a digital photo of the visible pipes and wires exposed by the excavation. The images are then scanned into the mapping platform, which extracts key data points from it: location, depth, and diameter of the pipes and other attributes of the infrastructure in the photo.

An alternative is to equip excavation equipment with cameras making it possible to record videos during excavation. Then a tool such as Bentley's ContextCapture could be used to create a 3D mesh showing the uncovered pipes and cables as well as neighbouring structures whose location is known. Together this information would enable the location of the underground infrastructure to be accurately determined

At the Year in Infrastructure 2017 (YII2017) conference In Singapore, Stéphane Côté, Augmented Reality Research Scientist at Bentley Systems, reported the results of an experiment in which he attached to four cameras, two Kodak Panorama and two Gopro cameras to an excavator. The resulting videos and images were then loaded into ContextCapture software to create a mesh. Analyzing the mesh allowed the location of underground pipes and cables that were exposed and the pipes that were laid to be located with centimeter accuracy by referencing neighbouring structures such as houses and other buildings whose locations were accurately known from other sources.  Stephane has created a video of the experiment.

If every piece of excavation equipment were equipped with low cost cameras, a lot of information about the underground could be captured during excavation and drilling with no impact on cost and scheduling. But if this data is stored in project or private databases and not shared, it will be of limited utility. Capturing this data in a shared, open database would a first step toward developing a model of the subsurface that can be reliably used for urban planning, design, construction and maintenance. The Netherlands has recently embarked on a national program supported by legislation and standards to do just that.  In 2015 a new law was passed by the States General or parliament in the Netherlands creating a Basisregistratie Ondergrond (BRO) or Key Registry for the Subsurface. The law mandates that if you excavate or drill you have to share your data with the registry. The registry is required to be open and accessible to all citizens of the Netherlands.  The combination of an open registry of underground features, as is being implemented in the Netherlands, together with excavation equipment equipped with cameras as in Stephane Cote's demonstration would be a low cost way to rapidly begin capturing reliable information about underground infrastructure.

Geospatial data bill before Congress revised to its original intent

The Geospatial Data Act (GDA) of 2017, which was introduced earlier this year in the Senate (S.1253) and a companion bill in the House (H.R.3522), has some history. It was originally proposed in 2015 by Orrin Hatch, a Republican Senator from Utah, and sponsored by five Republicans and five Democrats. The 2015 bill consisted of 10 sections and basically codified and strengthened federal government practices relating to geospatial data. For example, it proposed that the chair of the Federal Geographic Data Committee (FGDC) be the Director of the Office Management and Budget (OMB), an agency that has statutory authority and can set standards and enforce practices for all government departments.  This would provide FGDC with the authority to make other agencies follow existing common sense rules. The bill established a clear vision, assigned responsibility, provided authority and ensured oversight of Federal activities by Congress. These improvements were intended to help ensure that the U.S. is able to build a robust National Spatial Data Infrastructure (NSDI).  The 2015 bill enjoyed wide support in the geospatial community throughout the U.S. and was generally viewed as a positive step forward.

However the 2015 bill was blocked in the Senate Committee on Commerce, Science, and Transportation. It has been suggested that MAPPS, an organization representing some engineering and surveying firms that is attempting to restrict procurement of geospatial services by extending the Brooks Act to cover the geospatial sector, was instrumental in getting the 2015 bill blocked in committee. As a result the 2015 bill was rewritten with two additional sections that broadly extended the definition of survey data and mandated rules (Brooks Act) for procurement of geospatial services by the federal government. 

The bill has now been revised back to its original intent.  The text of the revised House bill (H.R.4395) is on congress.gov and it does not include Section 11 and 12 which were intended to apply the Brooks Act to geospatial data and services and were not viewed positively by the broad gesopatial community.  The text of the revised Senate bill (S.2128) is not yet available.

Geospatial data bill before Congress would stifle the geospatial sector in the U.S.

At a GITA-sponsored webinar yesterday John Wertman, responsible for government relations at the American Association of Geographers (AAG), gave an insightful presentation about the Geospatial Data Act (GDA) of 2017 (S.1253) which has been introduced in the Senate. (Representatives Bruce Westerman (R-AR) and Seth Moulton (D-MA) introduced a companion bill in the House as H.R.3522.) This bill has some history. It was originally proposed in 2015 by Orrin Hatch, a Republican Senator from Utah, and sponsored by five Republicans and five Democrats. The 2015 bill consisted of 10 sections and basically codified and strengthened federal government practices relating to geospatial data. For example, it proposed that the chair of the Federal Geographic Data Committee (FGDC) be the Director of the Office Management and Budget (OMB), an agency that has statutory authority and can set standards and enforce practices for all government departments.  This would provide FGDC with the authority to make other agencies follow existing common sense rules. The bill established a clear vision, assigned responsibility, provided authority and ensured oversight of Federal activities by Congress. These improvements were intended to help ensure that the U.S. is able to build a robust National Spatial Data Infrastructure (NSDI).  According to Wertman the 2015 bill enjoyed wide support in the geospatial community throughout the U.S. and was generally viewed as a positive step forward.

However the 2015 bill was blocked in the Senate Committee on Commerce, Science, and Transportation. It has been suggested that MAPPS, an organization representing some engineering and surveying firms that is attempting to restrict procurement of geospatial services by extending the Brooks Act to cover the geospatial sector, was instrumental in getting the 2015 bill blocked in committee. As a result the 2015 bill was rewritten with two additional sections that broadly extended the definition of survey data and mandated rules (Brooks Act) for procurement of geospatial services by the federal government.  According to MAPPS it worked closely with Senator Hatch and others, particularly NSGIC, on this legislation. 

According to Wertman the 2017 bill would mean that many government contracts would no longer be available to GIS shops and academic institutions. The bill would effectively exclude everyone but licensed architects, engineers, and surveyors from federal government contracts for GIS and mapping services of all kinds, not just those services traditionally provided by surveyors. The bill as currently formulated would also have implications for state government procurement because they often implement statutes similar to the federal government. For those who believe that a free and open geospatial sector has encouraged the high level of innovation that has characterized the sector this is not viewed as a positive step. The AAG argues that the bill will limit competition, innovation and free-market approaches for a crucial high-growth information technology sector of the U.S.economy and would cripple the current vibrant GIS industry, damage U.S. geographic information science, research capacity, and shackle government agencies, which depend on the existing GIS and mapping workforce.  Wertman said that the AAG views the 2015 bill as a positive step forward for the U.S. geospatial community but is opposed to the 2017 bill because of the restrictive procurement rules that were added to the bill.