Between the Poles

Automated feature recognition helps democratize utility mapping for producing infrastructure digital twins

In jurisdictions worldwide the low quality of underground location data has been identified as a top cause of underground utility damage. Existing underground utility data, most of which is stored in network owners' records, is often inaccurate, out of date or simply missing.  In North America in a survey of 402 locate professionals better facility maps received nearly unanimous agreement as a top strategy for improving the efficiency and quality of locates.  Singapore's Digital Underground project is unequivocal about the economic and social impact of low quality underground location data stating that "virtually all stakeholders are aware that much of the available information is unreliable and that this has repeatedly led to losses of time, money and opportunities."

Challenges facing the utility mapping industry

The root cause of the low quality of underground utility data is how utility mapping is structured as an industry. The primary activities of the utility mapping industry are data collection and how we keep and share this data with other parties.

Data collection

The way we collect is slowly changing, but predominantly we're still using traditional survey and locate methods. The survey methodology we are using hasn't changed in a hundred years. It's a very time and materials based, manual and analog process. Survey processes are slow and expensive to execute and require specialized equipment and skill sets, and safety is a big concern.

Locating underground utilities is one of the biggest causes of delays in construction schedules, service disruptions are a common occurrence, safety is always a major concern. and locate operations are slow to execute. Accuracy and timeliness are major issues. Locate operations require specialized equipment and skill sets which restricts locators to a small number of industry insiders.  A major shortfall is that underground utility location data is rarely available during the crucial planning and design stages of civil engineering projects.

Democratization of utility mapping

The utility mapping industry needs some fundamental changes. We need to move away from the monopoly that exists right now. There is just a handful of companies that are able to go out and collect underground utility data. We need to democratize data collection so that many more people can collect this data, targeting a crowd sourced approach and enabling multiple creators or collectors providing this data. This distributes the responsibility for the data as well. The more distributed the responsibilities, the lower risk and the lower the cost of failures will be.

We also need to change the skill level required. With current technology, we should be able to automate much of routine locating and thus enable many more people to do it. Using inexpensive technology will lower the cost of collecting and aggregating locate data. As data becomes lower cost to capture, data retention will become less expensive which will encourage people to retain the location data they acquire rather than simply painting/marking the ground and not capturing the data digitally.

Democratization is the real future of 3D infrastructure mapping. Right now data is still very siloed. We'll always have the traditional mapping companies, survey companies, line locating companies and asset owners going out and collecting their own data. But through crowdsourcing data from routine day to day locating and mapping, infrastructure projects, and other sources, aggregating that data and distributing access to that data is how utility mapping will really come to the forefront.  It should be easy to upload data into a central register where it can be made accessible to multiple stakeholders. Compliance with Open Geospatial Consortium (OGC) standards provides a basis for full interoperability enabling open access to every single conduit, cable, pipe, or other piece of infrastructure in real time from your phone, your tablet, your laptop or other applications.

Infrastructure digital twin

We think the best paradigm for producing and capturing asset data is through collecting it into an infrastructure digital twin. Digital twins have made a lot of progress in a variety of industries. Most importantly, for the utility mapping industry, it should be noted that creating a digital twin is much easier than it was even just a few years ago. We think the time is right for an infrastructure digital twin and it can be just at the asset level for one particular company, but we believe it should be at a regional level. Cities, municipalities and regional governments around the world are beginning to develop comprehensive digital twins of all of their physical infrastructure; buildings, roads, trees, power lines, and subsurface utilities and they're pouring resources into it.

And we think that Canada and the United States should start doing the same thing. And for us in the utility industry, data collection costs are lower than they ever have been. There's clearly interest among stakeholders and governments to start dealing with infrastructure and utility mapping problems, particularly to lower the number and costs of strikes, injuries and the loss of life. We now know a lot more about how to create data from other data sets, using different types of analysis and artificial intelligence to piece disparate data sets together. Using AI machine learning in the cloud is much less inexpensive compared to just a few years ago. And nowadays one of the things that makes infrastructure digital twins readily accessible to the utility industry is the fact that storing huge volumes of data in the cloud has become very inexpensive.

What is infrastructure digital twin ? First of all, a digital twin needs to result in effective action. It's not just enough say we have a digital twin philosophy The purpose of having a digital twin is to result in effective action that improves your business. It needs to use real time and historical data, which is paramount for us in the utility mapping industry, and it's going to be motivated by outcomes. For us in the utility industry, it is to reduce and even eliminate utility strikes during construction and the associated risks of injuries and fatalities.

The infrastructure digital twin has to be stored and managed in a relational database so it can be queried, analyzed and improved on through time, and related to other data sets. Processes for collecting data and maintaining data must be simple so that we can break the barrier of restricting underground utility mapping to a specialized group of people. If we truly want to democratize and compile data from disparate data sets, we need very simple processes to get data into the database, analyzed and maintained.

An infrastructure digital twin should be captured and maintained in 3D, even though most utilities in the ground are within just a couple of meters of the top of the ground. The 3D component is vital, because part of analyzing a data set is that much ancillary data is 3D such as overhead power lines that come down the side of a pole into conduits below the ground,

Imagery that's taken looking straight down at the ground, like traditional satellite imagery, can be visualized on a map and incorporated directly into a GIS database. But other types of imagery such as oblique, 360-degree, street-side, and inspection imagery need to be treated differently.  Oriented Imagery has a range of possible applications. For example, a construction company might use it to review asset inventory photos taken by field workers at a construction site. A telecom company or electric utility would use it to examine close range inspection images of electrical and telecom towers from many different angles. Oriented imagery applications enable you to see exactly where the image is on the map as you pan or zoom in the image. There are spatial navigation tools designed to work specifically with oriented imagery. You can query oriented images using spatial and temporal filters.

Finally, it is very important to have standardized formats, including storage formats and cloud formats.  It improves our ability to inexpensively use machine learning to do associative analyses of disparate data sets to make a larger, more conflated data sets.

Automated feature extraction

Maintaining accurate, up-to-date, comprehensive data about assets, above ground and below ground, has been a challenge for utilities for decades. Now new technologies including machine learning are being applied to automate real-time maintenance of utility network information and provide a foundation for infrastructure digital twins.

Automated feature extraction has advanced dramatically in the last few years.  This is an example where high density LiDAR and ultra high resolution imagery was used on a very low, very small diameter gas distribution install from an open trench. You can clearly see the ease with which the LiDAR produces a 3D representation of all of the local utility infrastructure.

Much of the the advancement in in automated 3D mapping have derived from the autonomous vehicle industry which have trickled down to other parts of the GIS industry.  The hardware and software are vastly easier to use and produce vastly better data. You can see from these ortho images collected from our system and the LiDAR overlaid on it. Feature extraction was used to automatically define top of pipe, top of ditch and actual joints in the pipe. Automated classification of LiDAR removes the need for someone to go out there with a Pogo stick and find the top of pipe. It makes it possible to simply drive alongside and scan with a mobile LiDAR system an open trench containing a newly installed pipe to produce a detailed 3D model with centimetre accuracy without having a person present. It is much safer - no boots on the pavement - and it drastically reduces the cost.

We prefer mobile mapping systems as opposed to handhelds because mobile mapping systems produce a tremendous amount more data and its registration tends to be vastly superior. It is much safer because it eliminates a need for someone to get out of a vehicle - so no boots on the pavement. Anytime you're still getting out, whether you're using handhold laser handhold mapping system, or a Pogo stick, you still have a person outside the vehicle walking around a street. With a mobile system you eliminate that risk, and you also drastically decrease the time required to do the collection. Here is another example where automated feature extraction was used to identify the electro fused coupling. You can see the butt joints, you can see the Ts and so forth. All this can be done very simply now with automated feature extraction.

Data registers

While surveyors may have had experience with a provincial or other types of survey register, utility location data identified during routine locate (one call) operations in general is only used to mark the ground and is rarely recorded, digitally or otherwise. Practically this means locators going back to the same location they were at just a few weeks ago to repeat the locate operation for another project. People in the locate industry are very familiar with this and the  amount of schedule and cost expense it causes. Exposing people to relocate or remap an area that was just done not too long ago is doubling their risk. Secondly, because the data is not retained or kept in easily accessible ways, we can't analyze it.  For example, we might want to confirm that we have correctly identified the type of utility, the accuracy of its location, or determine if there have been changes in its location, type, or current status since we last detected it. And because the data is not being retained and centrally managed at a global level or at a large scale level, there's no real opportunity for organic data enhancements or improvements.

The CGA Next Practices Report to Industry has made the case that a comprehensive national GIS map of buried infrastructure is among the opportunities for systemic improvement with the greatest ROI for industry. The benefits of a national register are greater safety, enhanced construction efficiency; reduced design, construction, and maintenance costs; greater regulatory compliance; improved locate efficiency; fewer project delays and budget overruns; fewer service disruptions to the public, less traffic disruptions and loss of custom and other inconveniences for the public.

Sharing maps of underground infrastructure is not totally new to Canada. For example, the City of Calgary's Joint Utility Mapping Project (JUMP) system, the ICI Society in British Columbia, the City of Toronto's Digital Map Owners Group (DMOG) and others have been sharing underground utility information for years.

The Canadian Underground Infrastructure Register (CUIR) is a new national initiative started at the beginning of the year. The CUIR initiative is intended to enhance current practices in a way that enables all stakeholder groups to benefit from the initiative; construction contractors, network owners, government transportation agencies, design engineers, subsurface utility engineering (SUE) engineers, surveyors, locators, workers in the field and the public. The CUIR initiative has been inspired by the National Underground Asset Register (NUAR) project currently being implemented in the UK. The objective of NUAR is to develop a register of location data for all underground infrastructure in England, Wales and Northern Ireland that is accessible to all network owners and government agencies.

This post is based on Joseph Hlady's (Lux Modus) talk at Subsurface Utility Mapping Strategy Forum (SUMSF).

Challenges of subsurface digital twins

The subsurface is composed up man made infrastructure; utility, transportation, and other infrastructure and the natural environment comprised of geology and geotechnical properties of the subsurface. A comprehensive subsurface digital twin requires a digital twin of underground infrastructure and a geology digital twin.

Digital twins of the underground infrastructure; utilities, communications, and transportation networks and other infrastructure, of cities, regions and nations have become a priority for many jurisdictions around the world. Over the past decade the emphasis has been on above-ground models, primarily because of the availability of technologies for efficiently capturing high accuracy location data for above ground infrastructure. However, many jurisdictions are recognizing the critical importance of below ground infrastructure and are integrating subsurface infrastructure into their digital twins.

Geological information, such 3D models of geological and geotechnical properties, and borehole databases, comprise an information product that has not yet reached its anticipated level of usage. Geological information has the potential to be useful in a wide range of broad use cases such as city planning, infrastructure construction projects, geohazard identification, environmental impact assessment and the development of water supply facilities, but to date this has not become standard best practice in these application areas. However, there is growing awareness of the importance of subsurface geology (in general but also to the technology used to detect and locate underground infrastructure) and it is expected that in the near future geology digital twins will be recognized as a foundation digital twin equally important to infrastructure digital twins.

Subsurface infrastructure digital twins

Digital models of underground infrastructure is characterized by important distinguishing features that differentiate them from above ground digital models and that need to be taken into account when maintaining and using models of underground infrastructure. These fall into the general categories of fitness for purpose, data quality, and accessibility.

Many cities, for example, Rotterdam, Helsinki, Pilsen, Athens, Rostock, Berlin and London to name a few, regions, such as Flanders, and nations such as the Netherlands, Estonia, Singapore, and the UK are developing 3D geometric models as a basis for digital twins. 3D models of above-ground infrastructure include buildings, transportation networks, parks, and other infrastructure typically captured in 2D or 3D imagery from overflights or in 2D GIS maps. But for a number of reasons underground infrastructure is often neglected, even though it is recognized that water and wastewater, energy (gas, electric power, and district heating), and communications (fibre and copper) networks provide the life blood of the city.

There is now a consensus that modeling a city requires not only information about buildings and transportation networks, but also about the utilities and communications networks. Utility and communications network operators maintain extensive records of their assets, including the location of their assets and operating, inspection, and maintenance data. For years they have used monitoring, control and simulation software for tracking and modeling demand, network energy, water, and gas flows, voltage, and pressure and other features of the modern utility networks. Utilities are expanding this capacity and moving in the direction of digital twins that can represent the entire utility system.  Ultimately this will enable them to support digital twin use cases that extend beyond utility asset management and network simulation to incorporate models of the entire utility network including demand and the supply chain. Digital replicas of energy infrastructure  makes it possible to undertake energy network sizing and routing process analytics to help understand and appraise loads and design optimum energy networks based on actual demand, consumer services and evolving technical solutions. In the UK electricity and gas networks operators have launched a National Energy Systems Map (NESM), a proof-of-concept project that brings together network data from all of Britain’s electricity and gas network operators into a digital, whole-energy system map covering Great Britain. The objective of the pilot project is to give customers information about energy network assets, where those assets are located as well as who owns them and is intended to provide the basis for a digital twin of the UK's energy network.

5G is increasingly recognized as an essential national utility. Knowing the location of all infrastructure is critical to understanding where fiber optic cables and 5G antennae intersect with the people and places that need them. This presents a major challenge because many telcos do not have current, comprehensive data on overground and underground assets in an integrated, easily accessible and shareable format. Operators need comprehensive, accurate and current location-based information on underground fiber-optic networks in relation to all the critical services that depend on them.  This will require integrating live data from all assets in the field with the demand for services from customers.

Challenges of subsurface infrastructure

There is a fundamental difference in what jurisdictions are able to do with representations of above and below ground infrastructure. Above ground, whatever can be photographed or scanned in the public space; from the street, from an airplane or drone, or from a satellite, in 2D or 3D, is generally unrestricted in application.  It is necessary to remove recognizable people and vehicles for privacy reasons and there may be national security restrictions, but in general this data can be used without restriction to create an open, publicly available digital model or digital twin.  The accuracy of the above-ground data in digital twins is generally high - for digitally LiDAR scanned data accuracy even reaches the mm level.

Underground the situation is different. First of all, there are restrictions on access to underground infrastructure location data.  In many jurisdictions some or all of the underground infrastructure in the public right-of-way is privately owned.  Where cities own water and sewer services, this data may be open and publicly accessible, but electric power, gas, and telecom are often privately owned and private ownership imposes restrictions on public access to this data. For example, the City of Rotterdam has decided that a key requirement of its digital twin is open access to the public. But for underground infrastructure the city has had to recognize restrictions on access. About 2/3 of the underground utility data is open and accessible, but there are access restrictions on the remaining 1/3 because these represent privately owned facilities.

Another major challenge in most jurisdictions is data quality. Utility network documentation and records are not collected or maintained consistently, the level of accuracy varies, data is often out of date and for abandoned infrastructure frequently missing. Over the past decades electric, gas, water, and telecom utilities have poured millions into digitalizing the graphic design/work management and GIS/records management processes. But the decades-old as-builting process remains marred by manual paper-based processes that results in low data quality and which has ramifications for the construction industry and for society in general. One of reasons for low data quality of network documentation is that utility business models provide no direct incentives to improve their data at the scale required for a full solution. This is a complicated problem that may require government involvement through regulation, changes in industry best practices and new technologies. Jurisdictions such as Colorado, Montana, and Singapore have implemented regulations focused on improving data quality. New technologies such as reality capture with LiDAR or a handheld phone together with RTK and other technologies for accurate positioning and mobile+cloud solutions for capturing and sharing data in the field are enabling real-time high accuracy data capture during construction.

Return-on-investment studies of improving information about underground infrastructure

Return on investment (ROI) studies of the benefits of improving the mapping underground infrastructure on highway construction projects conducted since the late 1990s have consistently revealed a large return-on-investment. A U.S. Department of Transportation sponsored survey conducted by Purdue University in 1999 of quantifiable and qualitative savings estimated a total of US$4.62 in avoided costs for every US$1.00 spent on accurately locating underground infrastructure. Although qualitative savings (for example, avoided impacts on nearby homes and businesses) were not directly measurable, the researchers made the case that those savings were significant, and many times more valuable than the quantifiable savings. In 2007, the Pennsylvania Department of Transportation commissioned Pennsylvania State University to study the savings on Pennsylvania highway projects and found a return on investment of US$21.00 saved for every US$1.00 spent for SUE when elevating the quality level of subsurface utility information using SUE. This significantly higher return on investment when compared to Purdue study is the result of maturation of process and a consideration of some of the qualitative savings in the Purdue study.

Fitness-for-purpose for subsurface utility models

Straits Times

For underground utilities the Open Geospatial Consortium's MUDDI underground standards project has identified important use cases for underground infrastructure location data including planning, reducing underground utility damage during construction, construction efficiency, asset management, emergency response, and disaster planning. Each use case has specific data requirements. For example, the planning use case may not require survey-grade location accuracy, it may only be necessary to refresh the data once or twice a year, and the level of detail may be low. Fitness-for-purpose for each use case will determine the quality of the data to be fed into the digital twin, the algorithms that model urban processes, the simulations that need to be supported and the analytics and visualization technology that makes the model cognitively easy and accessible to users.

The Netherlands, Helsinki, and Singapore have developed master plans for the subsurface that support multiple use cases. As an example, a number of years ago Singapore recognized the urgency for a map of its underground utilities for a number of purposes; planning, design, construction, ownership and maintenance of underground infrastructure. The Singapore Land Authority (SLA) initiated the Digital Underground project aimed at establishing an accurate, current, and complete map of subsurface utilities in Singapore that could be used for multiple use cases.

Priority use cases for subsurface utility models

However, the priority use cases for subsurface digital models in most jurisdictions are reducing underground utility damage during construction and construction efficiency. The simple reasons for this are economic. The cost of damage to underground infrastructure during construction amounts to tens of billions of dollars every year, injuries and fatalities of workers and the public, and delays and budget overruns on civil engineering construction projects. For this use case data accuracy, currency and completeness are essential requirements. Another issue specific to underground infrastructure is integration. In general each utility and telecom has a different data model, vocabulary, symbology, base map, standards for layout, and data protection guidelines. To integrate data from multiple organizations requires harmonizing these features in a common standard agreeable to all data providers.

Thus current geometric models of underground infrastructure will require important changes to enable them to be used as a basis for a subsurface digital twin. Digital twins are living models and need to reflect the changes in real time of the underlying assets. The key is to integrate live data from all subsurface assets and ensure that the geospatial systems modelling these networks share a common standard and are a ‘living document’ constantly drawing on current data and intelligence from the field. Databases of real time, high precision underground infrastructure data compliant with an agreed on standard are required. This requirement and new technology advances are driving fundamental changes to how data about underground infrastructure is captured and maintained.

New satellite constellation will capture 30 cm imagery with up to 15 revisits per day

I remember back in August 2014, when the first images from WorldView-3 were being downloaded. WorldView-3 was the first one-foot-resolution commercial satellite. It operated at an altitude of 617 km, flew in a sun synchronous orbit and could collect up to 680,000 km² per day.  It had 31 cm panchromatic resolution, could capture 680,000 km² per day, and had an average revisit time of less than a day.  In 2021 MAXAR is planning to launch the WorldView Legion constellation, six satellites with 30 cm resolution, better than 5 meter absolute accuracy, able to capture 5 million km² every day, and with up to 15 revisits per day.

At GeoIgnite Online 2020 Kumar Navulur, Chris Formeller, and Kathryn Seitz presented an overview of the specifications and capabilities of the Legion constellation. The total land mass of the Earth is 148.9 million km2.  The new constellation will be able to capture 30 cm imagery of over 3 % of that every day.  Over much of the lower 48 states in the U.S. it will be able to revisit the same location 15 times per day. Over Canada it will be closer to 8 revisits per day.  Some of the satellites will fly sun synchronous orbits and others mid-inclination orbits.  What this means practically is that it will be possible to capture imagery at different local times of the day, unlike with sun synchronous orbits where imagery is captured at the same local time every day. For many customers imagery will be available within 24 hours of capture.

A killer application for satellite imagery collected daily for the entire earth

At the GeoIgnite conference in Ottawa, Wade Larsen of Urthecast described what may be a killer application for satellite imagery captured daily. Farmers in the US over-irrigate by 25% per year. A recent study has demonstrated that monitoring vegetation growth on a daily basis can save farmers $50 000 in water per year. With 260,000 pivots operating in the US, this amounts to billions of dollars of savings every year.

Urthecast has been known for having two cameras (Iris and Theia) on the International Space Station and two Deimos earth observation satellites. Last year Urthecast announced the acquisition of Geosys Technology, a software company that processes earth observation imagery for agricultural applications.  Their latest initiative is called UrtheDaily with the objective of capturing nine bands of imagery from six satellites covering the Earth's entire landmass every day. The nine bands are identical to Sentinel-2's visual and near-infrared bands. The cameras are cross-calibrated with Sentinel-2 to assure data compatibility. The six cameras enable a daily revisit for higher latitudes and somewhat less for tropical areas. It captures imagery at the same time, from the same altitude every day.  About 150 terabytes will be acquired and downloaded every day. The imagery has five meter resolution (GSD) and represents scientific grade data quality that will be available on the cloud for user access within hours of acquisition. UrtheDaily is designed to support machine-learning and AI ready geo-analytics applications on a global scale. The UrtheDaily platform will provide the ability to discover data, run user algorithms, and deliver data to customers.

UrtheCast reports that it has already signed contracts primarily in the agriculture sector. An example of a potential killer app in agriculture is monitoring irrigation. Farmers in the U.S. overwater by 25 %. A four year in Texas by HydroBio which assessed the value of frequent monitoring of plant health and growth using satellite data concluded that to be effective this required high-frequency (daily) monitoring with highly calibrated sensors. With its resolution UrtheDaily will be able to monitor vegetation for individual pivots on a daily basis in order to optimize water utilization. It is estimated that this could save farmers $50,000 per pivot per year. (A pivot typically irrigates 120 acres.) There are 260,000 pivot irrigation systems in the U.S. which translates into the potential to save farmers billions of dollars per year.

To date UrtheCast reports that it has signed $350 million worth of contracts in the U.S., Russia, India and other countries. It plans to launch six satellites in 2021 and become operational in 2022.

Bringing reliable near real-time GIS within reach of every utility

Workflows for ensuring utility GIS quality are fundamentally broken and have been for decades.  Integrating CAD and GIS into an efficient workflow has been a challenge and it has simply not been a priority in the utility industry and in some parts of the communications sector to ensure real-time, high quality asset GIS data. Recently business drivers for high quality GIS data such as implementing a smart grid, estimating service availability and other applications have made GIS quality a top priority.  And now non-traditional geospatial companies are offering geospatial-based technologies that are revolutionizing the capture and maintenance of GIS information about utility and communications assets that can provide a foundation for the real-time network digital twin that is essential in the era of the smart grid and our increasing dependence on communications and utility networks. 

Broken business processes degrades GIS data quality

Maintaining accurate, up-to-date asset data has been a challenge for utility and communications firms for decades.  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.  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 the utility's GIS.  It is not uncommon for as-built backlogs to stretch into months even years, for updates from the field to take months to be entered into the records database if they are entered at all, and for the location of assets as recorded in the GIS to be city blocks from its actual location.   The challenge is to fashion workflows that include both engineering and geospatial data and applications into an efficient data flow from planning through design and construction to operations and maintenance with feedback loops that ensures an accurate, near real-time GIS.  For many utilities and communications firms traditional GIS, which has been the standard platform for utility GIS for the past few decades, has simply not delivered cost-effective, real-time, high quality asset location and associated data.

A key workflow found at all utilities and communications firms is the field maintenance process.  In many utilities this is a broken feedback loop which results in the quality of GIS data degrading over time.  When a field crew performs maintenance on network infrastructure, which includes routine and emergency maintenance, or encounters equipment incorrectly located or described in the GIS, the changes or the corrections to the GIS data from the field need to get back into the GIS quickly.  Too frequently this is still a paper-based process typically involving redlining as-builts, which get to sent to records for updating in the GIS.  The backlog associated with field updates can stretch into months.  The slow turnaround of field updates discourage field staff from reporting corrections and changes further exacerbating the GIS quality problem.  The result of this and other broken workflows such as the as-buillt workflow is that not only is GIS data incomplete, inaccurate and out-of-date, but that its quality degrades with time.

Business drivers for accurate, real-time GIS

For communications companies a major driver for an accurate and up-to-date GIS is providing rapid availability of service estimates to prospective customers.  I remember a large U.S. telecom that because it could not rely on the information in its GIS had to resurvey all the locations of its fiber terminations in order to respond to potential ADSL customers indicating whether they were close enough to a fiber termination for the service.

For electric power utilities a powerful business driver that has emerged in the last few years is the smart grid.  Digitalizing the grid has made the role of geospatial data and technology strategic for utilities. As a Navigant Research report stated the smart grid is all about situation awareness and effective anticipation of and response to events that might disrupt the performance of the power grid. Since spatial data underlies everything an electric utility does, GIS is the only foundational view that can potentially link every operational activity of an electric utility including design and construction, asset management, workforce management, and outage management as well as supervisory control and data acquisition (SCADA), distribution management systems (DMSs), renewables, and strategy planning. As John Simmins of EPRI has reiterated on a number of occasions a prerequisite to implementing a smart grid is cleaning up GIS data and developing business processes (feedback processes) to ensure that it remains accurate and up-to-date.

Technology enablers: Open standards, open source geospatial and handhelds

Recent technical advances have made it possible to implement cost-efficient processes for ensuring that utility asset data including GIS data is accurate and up-to-date.  There are now many widely adopted geospatial standards; Simple Features for SQL, WMS, WFS, GML, KML, and InfraGML to name just a few from the OGC that were not available two decades ago.  Secondly, since the founding of the Open Source Geospatial Foundation (OSGEO) in 2006 there has been rapid development of open source geospatial projects including PostGIS, GeoServer, MapServer, Leaflet and others.  This open source code-base has enabled entrepreneurs to rapidly develop low cost geospatial applications because the cost of maintaining most of the code is shared across a broad developer community.  Thirdly the advent of the handheld touch screen computer and phone provides a low cost universally accessible hardware platform for field staff to access geospatial and other information stored in a utility's databases.  With the appropriate software smart phones and tablets make it possible for field staff to see all of the information about an asset in the field at the touch of a finger.  For example, I blogged about how Duke Energy field staff are using an open source based mobile application which provides a single, common easy-to-use web tool for viewing geospatial asset data stored in GE Smallworld, ESRI ArcGIS, Intergraph GTechnology, spatialNET and Google Maps as well as non-GIS data in Maximo, SAP, three different outage management systems, four or five customer information systems, CAD drawings and photos and images.

Shifting the paradigm: GIS data maintenance from the field

The paradigm shift is empowering field staff to take responsibility for the quality of data in the utility GIS. Since they have direct knowledge about the location and condition of utility infrastructure, the key to revolutionizing data maintenance and enabling near real-time, accurate GIS and other data is "cutting out the middle man" and enabling field staff to maintain the utility GIS and other data in the field as they are working. Enabling field folks to do this without adding significantly to their workload (and over time reducing their workload as the quality of the GIS improves) requires a simple "touch and go" user interface which minimizes typing, responsive applications running on handhelds, and workflows that enable field staff to see their changes with a minimum delay measured in hours or days at the maxium.  Of course, operational data can be protected from problematic updates using data versioning technology (long transactions) which provides a rapid way for records folks to review and identify potentially problematic updates.

At Distributech 2019 a new entrant IQGeo emerged into the utility and communications space and announced an alternative to traditional GIS.  myWorld Capture is a mobile solution that prioritizes direct data maintenance from the field.  Building on an open source geospatial platform that supports data versioning and interfaces to legacy utility/communications GISs such as ArcGIS and Smallworld, myWorld provides tools that make it possible to develop and maintain a high quality near real-time GIS with the information that is required for a complete network asset data model including equipment location, descriptions, connectivity, condition, and status. 

The key to ensuring that the data is high quality and up-to-date is empowering field workers to make updates at the source as the work happens and to automate the data maintenance workflow as much as possible.  This approach can eliminate backlogs in field updates, encourage field staff to take responsibility for ensuring the quality of the utility GIS and enable an effective feedback loop that improves GIS data quality over time.   With the promise of near real-time updates directly from the field this approach provides a key foundation for the digital twin of the grid that integrates sensors reporting in real-time with simulation and analytics that makes it possible to create a accurate real-time situation assessment and to model the electrical state of the grid and possible future states based on reported or forecast outages and planned expansions and other changes to the electric network.

Operationalizing the new paradigm

How does this work in real world practice? I had a chance to chat with Dick Rohm, Director of Engineering at Cable One about how they are progressing toward their goal of no backlogs for field updates or for as-builts.  The business driver for this initiative, which they have been working on for the past several years, is service availability - being able to tell a customer immediately whether a fiber service is available at the customer's location or whether it will require an addition to the network.

Cable One provides commercial fiber services to businesses as well as cable services in 21 states, primarily in small cities and big towns.  Sales success requires being able to respond rapidly to potential customers requests for business fiber service, and say yes we already have the fiber capacity in your neighbourhood or no we have to build out a fiber line to support you.  Improving the quality of the data in their GIS is an important business driver for Cable One.  

By prioritizing as-built and update workflows Cable One has managed to ensure that as-builts and updates are in the GIS quickly, typically within 2-3 business days.  To achieve this, they first changed the as-built process, making the design process paperless about five years ago. Their engineering GIS allows engineers to use AutoCAD to design fiber and RF networks directly into GIS .  For construction, contractors are provided with AutoCAD drawings (DWG) on thumb drives and it is up to them if they wish to print them.  For small jobs, contractors can work completely electronically. For large jobs, contractors still use paper, but they print the DWG files themselves. Contractors must register with Cable One's Project Central which is used to track all projects. When paper as-builts are returned, they are spot-checked, and the GIS is updated based on any redlines. A project will not be closed out (and contractors paid) until redlines are in the GIS.  The GIS is used by Marketing and Sales as an added application to myWorld.

For updates from the field the initial idea was to use a web extension to their GIS, but they found that it was too cumbersome and slow.  It was replaced by myWorld on a handheld working with Google or Bing Maps which provided front end for viewing all engineering geospatial and associated data in the field. The myWorld solution only requires about an hour training time using a video. 

Based on the success of the myWorld implementation, user acceptance testing of myWorld Capture is currently underway. myWorld Capture enables field staff to update GIS and other data as they work.  Ease-of-use and rapid response are its key features. It makes it possible for field validators to do most of their work by tapping and drag and drop with very little typing.  Field engineering staff have to do more typing but even for them it is minimal.

The objective of the myWorld Capture implementation is 24-hour turnaround for updates from the field. It could even be less, but it is currently limited by the process of posting updates to the GIS and to the MyWorld database, which occur every night.  myWorld Capture will roll out to all field staff by July 1 of this year.
Dick Rohm emphasized that the key to making a transformative project, such as this at Cable One, successful is changing thought processes in the field. Data needs to be treated as just as important as physical assets.  When something changes or an error is found, the priority for field staff is to update it.  To encourage field staff to make changes in the GIS, tools need to be provided on handhelds that are fast and easy to use. Update processes must be fast so that their changes can be seen by field staff quickly in the GIS. Over time, field folks will increasingly rely on the GIS because they will see corrections that they make from the field appear in the database within 24 hours or sooner.  As data quality improves as a result of this feedback loop, field staff will find that they can rely on the GIS for locating and accessing up-to-date information about assets.

Conclusion

Prioritizing updates from the field to reduce the field update backlog to 24 hours or better together with an as-built backlog on the order of two to three days is a remarkable achievement.  It shows that with a business perspective that prioritizes a near real-time, reliable digital twin of utility and communications networks and geospatial technology supporting open standards and based on opensource and implemented for handhelds in the field, a cost-effective solution to the decades old GIS data quality challenge is within the grasp of any utility or communications firm.

Calculating forest loss using open source deep learning tools and publicly available training data

Deep learning algorithms have been developed by academia and as a result the code for the most part is open source but the successful training of deep networks requires thousands of labeled training samples and at the present time this training data is typically not open. In their presentation at this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis Jason Brown and Courtney Whalen, data scientists at astraea, showed how deep learning using publicly available labeled data for training was applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil.

Chris Holmes of Planet Labs, in his insightful talk at FOSS4GNA in St Louis about the application of deep learning to geospatial data, identified a challenge in making this technology open. The deep learning algorithms have been developed by academia and as a result the code for the most part is open source. For example, a deep neural network model developed originally for medical image segmentation called U-Net is open source and has been applied to identifying building footprints. Successful training of deep networks requires thousands of labeled training samples. Labeled data involves people on the ground manually ground-truthing land use types and other features so that the deep learning algorithms can learn what to recognize. At the present time this training data is typically not open source. In this presentation by Jason Brown and Courtney Whalen, both data scientists at astraea, deep learning using publicly available labeled data for training was used to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil.

This is computationally intensive and a distributed engine was used. The computation engine used open source components. Spark is a top level Apache project which enables distributed processing for global scale computation. RasterFrames is a free and open source toolkit allowing scientists, data scientists, and software developers to process and analyze geo

Forest cover in Mato Grosso in 2002

patial-temporal raster data with the same flexibility and ease as any other data type in Spark DataFrames. This is a LocationTech raster project and is built on GeoTrellis. Using this software each year required 6 to 7 hours of computation using 48 cores.

The imagery that was used was captured by the MODIS satellite for the years 2001 through 2017. MODIS monitors the reflection back from ground cover for several bands including red, green, blue, short wave infrared and near infrared. Its cameras have a spatial resolution of 500 by 500 meters and a revisit rate of one to two days. From the bands that it captures the normalized difference vegetation index (NDVI) can be calculated. From the data monthly means and yearly aggregates can be calculated.

Forest cover in Mato Grosso in 2011

The training data used came from the System for Terrrestrial Ecosystem Parameterization (STEP) which has 2000 manually labeled sites covering 17 different land cover types including five forest types scattered across all continents. The model was trained on MODIS 2012 data. 80% of the data was used for training. After training was completed the remaining 20% was used to test the model.

Comparison of rate of forest loss with Global Forest Watch for 2001 to 2016

After training and testing the first application was to Mato Grosso in central Brazil, a large state that has seen a lot of deforestation. The rate of deforestation tracked the rate estimated independently by the Global Forest Watch for the years 2001 to 2017. The major feature, the slowing down of the rate of deforestation in 2011 probably as a result of increased enforcement by the state government, is very clearly discernible.

The successful application of the deep learning technology in Mato Grosso has encouraged astraea to aim at applying this approach globally. They also intend to use satellite data with higher resolution and to handle seasonal differences better.

About STEP

The System for Terrestrial Ecosystem Parameterization (STEP) is a model for deriving vegetation and land surface parameters from remote sensing data for use in remote sensing-based classification of land cover, ecosystems, and vegetation types. The model defines parameters that relate to important ecological and biogeophysical parameters and that can be reliably measured or inferred from remote sensing, collateral, and field plot data. STEP is maintained as a database of training polygons drawn on high spatial resolution imagery that can be extracted with GIS to produce a global land cover classification. STEP is periodically reviewed to filter out inconsistent sites and augmented to fill gaps in biogeographical coverage. The database was originally created to follow the International Geosphere-Biosphere Programme (IGBP) land cover legend but it has since evolved to support any number of additional classifications.

Open developer-friendly standard for querying satellite imagery

One of the things that is required to make the vast quantity of satellite imagery easily searchable is a common way to query satellite data.  The SpatioTemporal Asset Catalog, known as STAC, is an open specification that came about when fourteen different organizations came together to increase the interoperability of searching for satellite imagery. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis, Matt Hanson, of developmentSEED, gave a technical overview of the STAC standard and described one of the first implementations.  The context for Matt's STAC presentation was provided by Chris Holmes' keynote in the morning.

Chris Holmes and others have been working on a standard for searching satellite imagery. Currently when a user wants to search for all the imagery in their area and time of interest they can’t make just one search — they have to use different tools and connect to API’s that are similar but all slightly different. The STAC specification aims to make that much easier, by providing common metadata and API mechanics to search and access geospatial data. This and other standards are essential for opening up satellite data to processing and visualization.  This is one of a new breed of standards that are designed to be developer-friendly to encourage the open source community and others to get involved with the Open Geospatial Consortium (OGC) to further interoperability.

The STAC standard is minimalist, but balances that with extensibility.  Basically it is comprised of metadata, catalog, and API.  The core of STAC metadata is very simple, with only three mandatory fields; ID, geometry (for example, a bounding box in a defined projection), and date and time.  The standard supports extensions, of which the earth observation standard "eo" has been defined at this time.  Future extensions could include point cloud, mosaic and video extensions.  The eo extension includes parameters such as camera viewing angle, resolution (dist between pixels), percent cloud cover, and band descriptions such as RGB, SWIR, band frequency and band accuracy.

The STAC standard and cloud optimized GeoTiff (COG) files makes it possible to search and stream imagery. COGs are GeoTiff files optimized for the cloud, for example, by streaming them from Amazon Web Services S3.  Planet, Google Earth, and QGIS already support COGs and there are other open source tools such as COG-Explorer that support them as well.

Sat-api is developmentSEED's partial implementation of STAC.  It currently supports Landsat-8 and Sentinel-2 imagery.  The API allows these imagery libraries to be queried by any of the metadata fields such as date and time, location (by defining a polygon), bands, viewing angle, and so on. It returns GeoJSON.  Matt provided some example queries.

 

Find all Landsat-8 scenes from 2017 with 0-20% cloud cover

https://sat-api.developmentseed.org/search/stac?datetime=2017&collection=landsat-8&eo:cloud_cover=0/20

Find all scenes from Dec 31, 2016 through Jan  1, 2018

https://sat-api.developmentseed.org/search/stac?datetime=2016-12-31/2018-01-01

The next developments that developmentSEED is planning for the STAC standard is to add more data CBERS and MODIS and to develop some applications based on the API.

An important role for national geospatial agencies: mapping underground infrastructure

Since Google Earth's release in June 2005, the rise of crowdsourced OpenStreetMap, the deluge of remote-sensed imagery from private satellite companies Digital Globe, Planet Labs, UrtheCast, and others, and private geospatial data companies like HERE, national mapping agencies and related government organizations involved with geospatial data and technology have found that the traditional role of the government mapping agency is no longer viable.  It has been suggested that national mapping agencies are an anachronism that are no longer required since the private sector is flooding the world with huge volumes of timely, frequent, high resolution geospatial data.   As a result all national mapping agencies have had to reasess their role in the geospatial ecosystem.   At the GeoBuiz conference in Hyderabad, a panel discussion moderated by Karthik Ramamurthy, Regional Director, IPSOS Consulting included Dorine Burmanje, Chair of the Executive Board of the Dutch Kadaster, Dr. Virginia Burkett, Chief Scientist at the US Geological Survey, Nigel Clifford, CEO of the UK Ordnance Survey, and Major General Girish Kumar, Surveyor General of the Survey of India provides a fascinating perspective on how different national mapping agencies are coping with the post Google Map world.

Dutch Kadaster

One of the roles for national mapping agencies which Dorine Burmanje mentioned about which there appears to be a consensus is as custodians of authoritative data. This includes property boundaries (referred to as parcel files in North America), data used to monitor pollution and climate change, and other data that is closely tied to monitoring and measuring national objectives and priorities. Governments have also an important role in making data, whether collected by themselves or more typically by the private sector, available to the public for free.  This is important because it contributed to the public good.  She identified a further key role that a government mapping agency can fill - as a custodian of data about the underground including underground infrastructure. The Netherlands is just embarking on implementing legislation passed by the States General in 2015 and the Dutch experience could be come a model to help the rest of the world address the pressing problem of cost-effectively geolocating underground infrastructure.

US Geological Survey

President Trump's fiscal year 2018 budget requested a 15 percent funding cut for the U.S. Geological Survey and proposes deep cuts for all mission areas.  This is not the first time the USGS has faced large cuts in its funding. 

At GeoBuiz Virginia Burkett gave two very intriguing examples of how the USGS is finding new and interesting roles for itself.  The USGS continues to develop with NASA the Landsat series of satellites.   Landsat 7 and 8 are currently operational, and two more, Landsat 9 and 10, are in development to replace them.  The USGS is finding new ways to reuse the existing Landsat data that currently stretches back to 1972.  Using data cube technology the USGS is able to track individual (cloud free) pixels over time, currently back to Landsat 4, but ultimately to Landsat 1. She gave an example of a pixel in a forested area of the US which recorded the evolution of the forest since 1982 including a fire and the slow recovery after the fire.

She also described how the USGS is supporting citizen science using crowd harvesting - mining social media of evidence of earth tremours and quakes to be able to provide earlier warning - within 29 seconds - than is possible using only seismological analysis.

Ordnance Survey

The Ordnance Survey (OS) is the world's oldest national mapping agency - having begun in Napoleonic times as a military mapping agency. Since then the OS has weathered several disruptions including the industrial revolution, the digital revolution, and the current disruption of the tradtional national mapping agncy business model. Currently the OS is effectively a private, for profit company, albeit with a single shareholder, the UK Government.  As CEO not only is Nigel Clifford responsible for national mapping objectives, but he has to generate a financial return to Parliament.  One role he sees for national mapping agencies is becoming custodians for national registers - of addresses, property boundaries and other core national data. Nations also require new data sets including detailed street furniture and vegetation. IGN, the French national mapping agency, is currently piloting a data model for doing this called PCRS 2.0.  Nigel also sees national mapping agencies as undertaking projects that are considered too risky by the private sector but for which the social payoff may be large.  He emphasized that national mapping agencies have to follow a fine line since they should not be perceived as competing with the private sector.  He also mentioned a role for national mapping agencies in providing data for startup incubation, for example, the Geovation hubs in the UK.

The immediate challenge the OS is facing is that Parliament has recently created a Geospatial Commission tasked with determining the role of geospatial data in developing the national economy.  55% of OS revenue derives from selling their premier data set, the OS Mastermap, to government and private companies. The Geospatial Commission is proposing making Mastermap data available at no cost to small business as an economic development stimulus for the SMB sector. Since this initiative would directly impact OS revenues, either large customers would have to pay proportionately more to make up for the short fall or the OS has to find alternative revenue sources.

Survey of India

The Survey of India has been around for 150 years. Incredibly until 2005 the national datum remained the Everest datum - named for the Surveyor General responsible for the first survey of India. General Girish Kumar was unambiguous in recognizing that the availability of huge volumes of private data has changed the national mapping agency business model.  These are clearly challenging times for the Survey of India.  His view is similar to the Ordnance Survey - national mapping agencies cannot and should not compete with the private sector.  He still sees a role for national mapping agencies collecting data in rural areas that are financially unattractive for the private sector.  He also sees that national mapping agencies have a role in setting standards for data quality to ensure that the private sector is collecting data reliably and to prevent "fake data".  National mapping agencies can also provide training to support capacity building so citizens are able to understand how different types of geospatial data should be used.  General Kumar singled out a new role that he suggested only a national mapping agency can take on - mapping underground infrastructure.  He revealed that the Survey of India has already mapped the underground infrastructure of Delhi.

New roles for national mapping agencies

These are clearly challenging times for national mapping agencies, but new roles are emerging for these agencies. There is a consensus that an increasingly large proportion of geospatial data is sourced from the private sector and from crowdsourced data such as OpenStreetMap.  There is also an consensus that government agencies have a a role as the custodian of authoritative data including registers of national base data and data for monitoring and measuring national objectives and priorities such as air and water quality and climate change.  While government agencies are, with some exceptions, no longer in the business of collecting data themselves, they need to quality control data that gets the government imprimatur.  Speakers also said that government mapping agencies have a role in ensuring that nationally significant geospatial data which may come from from many sources including private commercial sources is made open and available to the public for the benefit of society at large.

Mapping underground infrastructure

Two speakers identified as a new role for which national mapping agencies are well suited - mapping and being the custodian for underground infrastructure. Increasing urbanization throughout the world challenges the sustainable development and resilience of cities.  The importance of the ground beneath cities is often overlooked. In many parts of the world the location of underground infrastructure including electric power, water and wastewater, gas, district heating, and other networks is unknown or poorly known.  Information about the underground is captured during excavation and drilling, but this data is typically stored in project or private databases and is not shared.  Government may be uniquely qualified for mapping and maintaining information about national infrastructure, much of which has a national security dimension.  For example, in the U.S. access to infrastructure data including location is restricted under Protected Critical Infrastructure Information (PCII) Program.  There are successful examples around the world where municipal and regional governments have helped enable a shared underground utility network database. 

Capturing this data in a shared, open database is 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. This law mandates that if you excavate or drill you have to share your data with the registry. In addition if when using the data in the registry you find something is incorrect you have to report it. The registry is required to be open and accessible to all citizens of the Netherlands. This new registry will require new standards for recording data and there is a major effort underway in the Netherlands to develop these standards. The implementation of the new subsurface registry will begin this year.  Furthermore at the Geobuiz conference I was encouraged to hear from the Surveyor General of India, Major General Girish Kumar, that the Survey of India has mapped the underground infrastructure of Delhi.  After the panel discussion General Kumar said that information about the Delhi underground mapping project will be forthcoming.

Geospatial technology captures wall instability and collapse in an open pit mine

One of the most exciting presentations at the SA Geotech conference in Johannesburg was a presentation by Jason Richards of Maptek.  He talked about using geospatial technology to map in space and time the instability and ultimate collapse of a wall of the Kanmantoo Copper Mine in South Australia.  This is an open pit copper mine that was in operation 1970-1976 then closed but has reopened after significant additional deposits of copper and gold were confirmed.   The copper and gold deposits are mined using well established open-pit excavation techniques including drilling, exploring, blasting, loading and hauling. Two operational pits were dug at the mine site to a depth of 270m for extracting the reserves.  The larger pit (Giant Pit) spans up to 1.2km long and 600m wide. Hydraulic excavators and highway trucks are used to transport the ore to the processing plant.  The roads used by the trucks wind along the walls of the mine.  The surrounding rock is brittle and liable to fracture.  Collapse of a wall has occurred in the past and is a serious hazard for the trucks carrying the ore out of the mine for processing.

Unexpected geological conditions occur in the east wall of the Kanmantoo Giant Pit resulting in threatening wall movements.  A Maptek Sentry system was put in place to map wall movements to monitor the stability of the wall.  The Sentry system combines laser scanning and photography and can monitor multiple areas within a scene in real time without the need for targets or reference points. Movement can be detected down to 1 millimeter per hour. Heatmaps colored by displacement or velocity provide an overview of movement in real time.  Animations of time changes can be used to show morphology of terrain changes over time. Time lapse capability allows users to see changes and use this information to predict future movement.

In the Giant Pit a Sentry system was set up to monitor a 550 meter wide section of the brittle east wall of the Giant Pit at Kanmantoo.  The wall was scanned initially every 23 minutes, but after significant continuing movement was detected scans of a more focussed area were made every 7 minutes.  Movements were detected that were directly related to blasting with sharp jumps in displacement that usually stopped moving after an hour or so.  But after one blasting sequence a 364 tonne block initially jumped 30mm with the blast.  Two hours later it accelerated again, ultimately failing 4 hours after the initial blast.  The Maptek system was able to capture the movement, raise an alarm and visualize the movement with heat maps and video.  Jason showed a time lapse video of the movement and ultimate collapse of the wall which is one of most impressive videos of the progress of a geological event I have seen.

This is another example of how geospatial technology, in this case laser scanners combined with photography with real-time geoprocessing, is able to provide solutions in a wide range of industries.

Open source geospatial application reduces copper cable theft by 70%

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

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

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

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

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