Between the Poles

Severely restricted access to critical infrastructure information in the U.S.

The Critical Infrastructure Information Act of 2002 of the U.S. Congress provides a mechanism for severely restricting access to proprietary critical infrastructure information voluntarily shared by network operators with the federal government.  It prevents consumers and organizations from accessing this information under the Freedom of Information Act, by a regulatory agency, or as part of civil litigation.  What this means is that a utility, telecom, or other organization can elect to submit proprietary network information including the location of above and below ground facilities to the Department of Homeland Security's critical infrastructure information program (PCII) after which it is protected from access by anyone except certified Federal government employees and contractors.

Introduction

Information about infrastructure in the United States is regulated by an act of Congress and several presidential directives. The earliest federal directive is the 1998 Presidential Decision Directive/NSC-63 Critical Infrastructure Protection that identified the need for a private public partnership, preferably voluntary, to protect critical infrastructure.  The Critical Infrastructure Information (PCII) Act of 2002 followed very closely after September 11, 2001,   The Homeland Security Presidential Directive 7 in 2003 established a national policy for Federal departments and agencies to identify and prioritize critical infrastructure and to protect them from terrorist attacks.  The Presidential Policy Directive 21 (PPD-21), Critical Infrastructure Security and Resilience of 2013, established a national policy on critical infrastructure security and resilience as a shared responsibility among the Federal, state, local, tribal, and territorial governments and public and private owners and operators of critical infrastructure. This directive also clarifies the critical infrastructure-related responsibilities across the Federal Government.  The National Infrastructure Protection Plan (NIPP) 2013 is a plan resulting from Homeland Security Presidential Directive 7. The NIPP's goals are to protect critical infrastructure and key resources and ensure resiliency. The NIPP is based on the 1998 Presidential Decision Directive-63, which identified critical sectors of the economy and identified government agencies to each of them on sharing information and on strengthening responses to attack.

Critical Infrastructure Information Act of 2002

The U.S. Congress established standards for protecting critical infrastructure information through the Critical Infrastructure Information Act of 2002. In response, the Department of Homeland Security (DHS) created the Protected Critical Infrastructure Information (PCII) program to protect private sector infrastructure information voluntarily shared with the government for the purposes of homeland security. The 6 Code of Federal Regulations (CFR) part 29, Procedures for Handling Critical Infrastructure Information; Final Rule, published in 2006, established uniform procedures on the receipt, validation, handling, storage, marking, and use of critical information information voluntarily submitted to DHS. Information submitted by utilities, telecoms and others to the PCII is broadly protected from disclosure. This includes disclosure under the Freedom of Information Act; State, local, tribal, or territorial disclosure laws; regulation or civil litigation.  Only trained and certified U.S. government employees or contractors may use PCII. All users of PCII must have homeland security responsibilities and duties, possess a valid need-to-know, and sign a non-disclosure agreement.  Organizations that can submit proprietary information to the PCII includes privately or publicly owned companies, industry associations and State and local government officials.  Federal government agencies may not submit information to the PCII Program.  Information that is in the public domain cannot be submitted.

Presidential Decision Directive/NSC-63 1998 Critical Infrastructure Protection

Directive NSC-63 identified critical infrastructure whose physical and cyber-based systems were essential to the minimum operations of the economy and government. These included telecommunications, energy, banking and finance, transportation, water systems and emergency services, both governmental and private. Since most infrastructure is privately owned, the protection of critical infrastructure is necessarily a shared responsibility and partnership between owners, operators and the government.  The directive specifically identified interdependence between different utility, telecom and other systems as a growing challenge. Its goal was to create a public-private partnership to enable a closely coordinated effort of both the government and the private sector to avoid increasing government regulation or putting a greater burden on the private sector. To that end it stated that it is preferable that participation by owners and operators in a national infrastructure protection system be voluntary. It also emphasized that privacy rights of consumers and operators of utility, telecom, and other networks must be protected and information handled accurately, confidentially and reliably.

Homeland Security Presidential Directive 7: Critical Infrastructure Identification, Prioritization, and Protection 2003

Homeland Security Presidential Directive 7 establishes a national policy for Federal departments and agencies to identify and prioritize critical infrastructure and to protect them from terrorist attacks. It directs that federal agencies will collaborate with the private sector to encourage the development of information sharing and analysis mechanisms. Its objective is to identify, prioritize, and coordinate the protection of critical infrastructure and key resources and to facilitate sharing of information about physical and cyber threats, vulnerabilities, incidents, protective measures, and best practices.

President issued Presidential Policy Directive 21 (PPD-21), Critical Infrastructure Security and Resilience 2013

This directive establishes national policy on critical infrastructure security and resilience as a shared responsibility among the Federal, state, local, tribal, and territorial governments, and public and private owners and operators of critical infrastructure.  Its objective is to enable information exchange among these organizations by Identifying baseline data and systems requirements for the Federal Government. Federal departments and agencies are directed to ensure that all existing privacy principles, policies, and procedures are implemented consistent with applicable laws and policies.  The Secretary of Homeland Security, in coordination with other Federal departments and agencies, was directed to convene a team of experts to identify baseline data and systems requirements to enable the efficient exchange of information and intelligence relevant to strengthening the security and resilience of critical infrastructure.

National Infrastructure Protection Plan 2013

The National Infrastructure Protection Plan (NIPP) 2013 was mandated by Homeland Security Presidential Directive 7. The latest version of the plan was produced in 2013.  The NIPP's goal is a mechanism for developing coordination between government and the private sector.  As the Nation’s critical infrastructure is largely owned by the private sector, managing risk to enhance security and resilience is a shared priority for industry and government.  The goal is collaborative public-private partnership that operates as a unified national effort, as opposed to a hierarchical, command-and-control structure.  The eighteen sectors identified by DHS as critical infrastructure are: chemical, commercial facilities, communications, critical manufacturing, dams, defense industrial base, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information, technology, nuclear reactors, materials, and waste, transportation systems, water and wastewater systems.  While it lauds the investment in some critical infrastructure it specifically refers to A National Academy of Sciences report that the Nation’s earlier heavy investment in the design, construction, and operation of critical infrastructure systems—water, wastewater, energy, transportation, and telecommunications—has not been matched with the funds necessary to keep these systems in good condition or to upgrade them to meet the demands of a growing population.

Information about critical infrastructure is vulnerable to unauthorized access that could affect its confidentiality, integrity, or availability. The distribution of such information to those entities that can use it for efficient and effective risk management remains a challenge. It is critical to maintain the availability of information and distribute it to those who can use and protect it properly. This entails being transparent about information-sharing practices; protecting sources and methods; and ensuring privacy and protecting civil liberties, while also enabling law enforcement investigations.  It references the PCII program, authorized by the Critical Infrastructure Information (CII) Act of 2002 and its implementing regulations (Title 6 of the Code of Federal Regulations Part 29).

The National Plan identifies certain lifeline functions that are essential to the operation of most critical infrastructure sectors. These lifeline functions include communications, energy, transportation, and water. To manage critical infrastructure risk effectively, it directs that partners must identify the assets, systems, and networks that are essential to their continued operation including interdependencies. Interdependencies may be operational (e.g., power required to operate a water pumping station) or physical (e.g., co-located infrastructure, such as water and electric lines running under a bridge span) and include infrastructure that requires accurate and precise positioning, navigation, and timing (PNT) data. PNT services are critical to the operations of multiple critical infrastructure sectors and are vital to incident response.

Geospatial + BIM interoperability essential for the development of smart cities

Integrated geospatial and BIM is increasingly being recognized as essential for full lifecycle construction and for enabling urban digital twins.  At this year's Geo Business in London the importance of integrating these technologies was asserted by several speakers.  From the perspective of developing smart cities, Hong Kong has made integrating these technologies a policy priority for its goal of becoming a smart city.

In an award winning paper at a 2011 conference organized by Britain’s Association for Geographic Information (AGI), Ann Kemp, then head of GIS at Atkins Global, the design and engineering firm, asked the question ‘BIM isn’t geospatial — or is it?’ and then argued that integration of geospatial and BIM was essential to address the challenges of the 21st century.  At that time within the UK construction company this was a radical suggestion, but since then the need to integrate geospatial and BIM has been gaining traction in the construction industry.  Among the construction vendor community it was adopted by Bentley, Hexagon, and Trimble a number of years ago and in 2017 Autodesk also jumped on board, forming an alliance with ESRI.  At this year's Geo Business event in London the importance of an integrated geospatial and BIM approach in construction was recognized by several speakers.  For example, Marek Suchocki, Autodesk argued that GIS and BIM are not separate, quite a change for Autodesk compared to a couple of years ago.

Hong Kong has the highest population density (26,100 per km²) in the world.  In Hong Kong 50% of the construction industry is government providing government with a key role in moving the entire construction industry, both public and private, forward. The city is quite advanced in adopting BIM.  BIM is mandatory for projects over HK$30 million. The city has developed a BIM Data Repository prototype to allow BIM models of public structures to be shared among government departments and with the public.  BIM has been accepted for General Building Plan submission (permitting) and all of this has been done in collaboration with the Construction Industry Council (CIC).  Finally a study of BIM/GIS integration has been completed.

In his inspiring talk at Geo Business Thomas Chan, Director of Lands, Hong Kong SAR Government, asserted geospatial has to go hand in hand with BIM in creating the common spatial data infrastructure (CSDI) that will underpin Hong Kong as a smart city. As a basis for moving toward a smart city Hong Kong has adopted common spatial data infrastructure,  3D GIS city model, and open data as public policies.  The city has already initiated a pilot open CSDI data portal with over 70 data types available.   But the BIM-GIS integration study found that it is currently difficult to bring BIM models into GIS.  Thomas Chan mentioned the OGC standards CityGML and IndoorGML as essential smart city standards.  Research has shown these standards have some incompatibilities with buildingSmart BIM standards. He is strongly encouraging the geospatial and BIM communities to collaborate to ensure that BIM models submitted to the city can be easily brought into the city's CSDI.

I would suggest that the most important takeaway from this is that in addition to the now widely recognized benefits of geospatial+BIM integration for the construction industry, it is also essential for creating a common spatial data basis for smart cities.  If we cannot resolve AEC+geospatial interoperability now through collaborative work between the two communities, we will continue to be plagued by this issue for decades to come much as we have been by CAD/GIS incompatibility for the past 25 years.

Improving and sharing geospatial information about the subsurface in England

NEDO estimates that about 50% of time over-runs on commercial building projects are caused by unforeseen ground conditions.* There are two aspects to the problems associated with the subsurface.  One is geotechnics, soil, ground water, and geology conditions.  The other is the location of underground infrastructure, utilities, telecommunications and other cables and pipes. There are growing government, private sector and standards initiatives in both of these areas in the U.K. to improve the information and sharing of information about the subsurface.

Social driver: Public safety

Public safety is an important social driver for improving knowledge about the underground.  I remember attending a Pipeline Industries Guild (PIG) conference in Birmingham, U.K. in 2005 where the focus of the entire event was to motivate the pipeline operators industry in England to voluntarily participate in a call-before-you-dig centre that would reduce the risk of damage to underground infrastructure during excavation.  There had been a near major disaster in Birmingham (Hunton Hill) when a gas company crew had attempted what was supposed to be a routine tap into a gas main to connect a new customer.  Three drill bits later, the crew knocked off.  Fortunately an ESSO contractor just happened to pass by and recognized that the gas crew had been attempting to tap into a high pressure fuel line.  At the conference the section of pipe with dimple caused by the gas crew was shown.  A number of experts described the the extent of the disaster that would have followed if the crew had tapped into the line.  The 2003 gas explosion in Belgium would have paled by comparison.  In just the last month in the Americas, there has been an explosion in downtown San Francisco, another one in Durham, North Carolina, one in downtown Sao Paulo, and an evacuation of a neighbourhood in Calgary, Canada when a 6" gas main was ruptured during excavation.

Economic driver: construction productivity

UK construction productivity compared to other sectors

The major problem facing the construction industry in advanced economies is productivity which has plateaued or even declined over the past 40 years. With the growth of private investment in infrastructure, improved productivity has become urgent. McKinsey has made the case that BIM and geospatial are key technologies in transforming construction.  The U.K. has been a leader in the digitalization of the construction industry, mandating that from 2016 all public construction in the U.K. must use a BIM Level 2 process.  The subsurface has been for the most part ignored in the application of digital technology to construction, but there are signs now that this is changing in the U.K., as people recognize the drag on construction projects and the broader national economy that this represents.  Improving the reliability of and sharing location information about the underground can be seen as 'low hanging fruit' in the quest to improve construction productivity. 

Sharing subsurface geotechnical information

One of the problems is that every construction project involves considerable effort to discover subsurface geotechnical conditions and the location of underground infrastructure in the area to be excavated, but a large proportion of the data generated by the UK ground engineering industry is used for one project and not shared.  In the Netherlands the problem is being addressed by the Key Registry for the Subsurface (BRO) which came into force in January, 2018. The BRO registry consists of 26 data types, which will become mandatory in installments over five years and all of which include location. On 1 January 2018,it become mandatory to report the first three data types, geotechnical surveys (CPT), groundwater monitoring wells and soil drilling sample profiles. On 26 June 2018, this data became publicly available via the Dutch open data portal PDOK.

In the UK the Dig to Share project, supported by Atkins, British Geological Survey (BGS) and Morgan Sindall, is addressing this problem. Its aim is to develop a fully digital workflow, which is accessible to the whole industry, to upload and access data from the BGS web-based system. This will be developed on top of the existing system hosted and maintained by BGS. 

I should add that in the domain of geotechnics Keynetix is a UK-based provider of cloud-based software for capturing, visualizing, modeling, and sharing of geotechnical data. Bentley Systems has just announced the acquisition of Keynetix which adds to Bentley's products for the subsurface including borehole report management (gINT) and geotechnical analysis applications (PLAXIS and SoilVision) with which Bentley is targeting enabling a digital twin of the underground. 

Sharing information about the location of underground infrastructure

The Geospatial Commission has just announced its intention to create an Underground Assets Register.  The intention is to show where electricity and telecom cables, and gas and water pipes are buried to help prevent both accidents and disruption to the economy.  The project will start with £3.9 million pilot projects split between London and the North East.

Pipes and cables beneath a road crossing in North East England - Thanks to Holger Kessler for sharing.

Related to this is the North East Underground Infrastructure Hub (NEUIH) , the Ordnance Survey's and Northumbrian Water's project to create a ‘Common Infrastructure Map’ in Newcastle.  The current phase of the initiative is called ‘Sunderland sand box’ and implements a dataset that contains almost full water, gas, electricity and a great deal of telco data for the city of Sunderland.

Collecting underground utility damage statistics

The U.K. has not had access to good statistics on underground utility strikes similar to the Common Ground Alliance in North America.  Since 2014 the Utility Strike Avoidance Group (USAG) has begun collecting statistics on underground utility damage. USAG currently operates on a voluntary basis with no direct funding other than the support offered by member organizations.  Its 2015/2016 report includes over 2700 utility strikes across the UK during 2015 & 16 from 32 participants.  For comparison in the U.S. there were 316,422 incidents of damage to underground infrastructure reported to the Common Ground Alliance in 2017.  According to the Japan Construction Industry Association (Edson Enohi personal communication), in all of Japan in 2016 there were 134 incidents.

One-call centre for England

England does not have legislation mandating a one-call centre as just about every state and province in North America does.  This puts the onus on excavators to track down and contact utilities and others who may have infrastructure in the area of the planned excavation.  To fill the gap a private company LinesearchbeforeUdig (LSBUD) offers something similar to a North American one-call centre. I had the opportunity to chat with Richard Broome, Managing Director of LSBUD.  LSBUD offers a voluntary free to use service which anyone planning excavation can use to check for underground utility assets that LSBUD's 75 members (network operators) have in the area of the planned excavation.

The way this works has some similarities with a North American one-call centre.  The excavator provides details of the planned excavation to an online system.  The system immediately responds with a list of LSBUD members that have assets near where the excavation is planned.  Subsequently each member sends plans of their infrastructure assets in the area of the planned excavation.

LSBUD is owned by Fisher German LLP and PelicanCorp, an Australian company that runs one-call centres in Indiana, Kentucky, Alberta, British Columbia, New Zealand, Australia, Ireland, Singapore in addition to the UK. The assets that LSBUD's member manage include hundreds of thousands of kilometers of underground and overhead electricity, gas, high pressure fuel, water and fibre optic pipes and cables.  LSBUD is supported by its members which include all fuel transmission companies, 60-70% of electric power network operators, and some water and telecom operators in England. The benefits that the members derive from LSBUD are improved safety and reduced utility damage costs. The public also benefits from fewer outages.  The LSBUD service processes over 2.5 million inquiries per annum.

There are risks of sharing information for both commercial (competitive) or security purposes. To manage this balance, the Member always controls who accesses their data. Members always controls who accesses their data. Users can get very high level information about a Member’s network through the LSBUD oce call process, but members are in full control of the access rights to their detailed information. They also have a live view of those requesting and receiving their information together with a detailed audit trail.

Much of the liability is on the contractor to locate the assets on site. In terms of the accuracy/ validity of the data, the asset owner carries that themselves with relevant disclaimers. The asset owner determines both the Area of Interest (AOI) on the service, as well as any guidance/literature/disclaimers that are sent with their responses.

Coordinating street works

One area that would directly benefit from shared location information about the subsurface is street works. In 2004 the UK Parliament passed the Traffic Management Act, which gave highway authorities greater power to manage traffic disruption caused by 2.5 million roadworks that take place in England each year.  In particular it requires excavators to apply for a permit (as Bern has done) for road work providing information about the location and timing of the proposed road work.  Late last year the Department for Transport announced an investment of £10 million in Street Manager, a digital planning service that will generate real time data about street construction.  It will be free for technology companies and app developers to use. This will allow services like Waze (whose vision is to eliminate traffic congestion) and startups to build app that help motorists avoid congestion caused by road construction.

The Traffic Management Act also enables local councils to charge a fee for permitting road work.  One way of using this power is referred to as lane rental.  Pilots have been carried out in several location in England and have been found to reduce congestion by half on busy roads.  For this to be used effectively requires accurate locate information about underground utilities and communications infrastructure.

Standard relating to the underground

A process to update the 2014 Publicly Available Specification (PAS) 128, has just been initiated.  PAS 128, developed under the auspices of the British Standards Institution (BSI) and sponsored by the Institution of Civil Engineers (ICE) and others, is one of the few that reflects the advances in underground detection technology - hardware and software - that have occurred in the last few years.  That it is now being updated suggests that PAS128 will continue to stay current with the underground detection technology curve.

The Open Geospatial Consortium MUDDI initiative to develop a standard for sharing information about the subsurface, including both geotechnics and underground infrastructure, is supported by the Ordnance Survey and includes several U.K. and INSPIRE standards.

Together these government, private sector and standards initiatives reflect the accelerating recognition in the UK of the importance of sharing information about subsurface geotechnics and infrastructure for public safety and construction productivity.

Reference

* N.M.H. Alhalaby and I.L.Whyte, The impact of ground risks in construction on project finance in Risk and reliability in ground engineering, Edited by B.O.Skipp, Proceedings of the conference organised by the Institution of Civil Engineers, London, 1993.

"A statistical review of projects revealed that 50% of commercial buildings and 37% of industrial buildings experienced delay due to unforeseen ground conditions. Reports from NEDO state that the most frequent origin of overruns and long delays (greater than 10 weeks) are unforeseen obstacles in the ground."

NEDO Faster building for industry. National Economic Development Office London 1983

NEDO Faster building for commerce. National Economic Development Office London 1988

Using publicly available environmental data in a spatial analysis of low birth weight babies

A spatial analysis of possible environmental factors contributing to the increase in the number of underweight babies born in Canada was presented by Charlene Nielsen at GeoAlberta 2018. Termed low birth weight at term (LBWT), this is the second most important cause of infant mortality in Canada and costs the medical systems hundreds of millions of dollars every year. It is also linked to childhood and adult diseases. Charlene's hypothesis was that environmental factors including chemical pollution, the man made environment, and socioeconomic level were contributing factors to low birth weight babies in Canada.  She used spatial analytics to study the problem which she used as the basis for her PhD thesis at the University of Alberta.

Using open, publicly available databases Charlene related the spatial distribution of low birth weight babies to geographical proximity to air-based chemical emissions or to land-based pollutants related to electrical power lines, dumps and solid waste sites, gas stations, mine sites and tailings, oil and gas well pads, transformer stations, and crop lands.

One of the publicly available data sources for chemical emissions that she used is the National Pollutant Release Inventory which has been maintained by Environment and Climate Change Canada since 1993.  It currently monitors the emissions of 324 chemical substances.

Her study found that air-released or land-based pollutants may be more important depending on geographical location. There was a greater correlation of low birth weight babies with the environmental factors she studied in Ontario, Quebec, Alberta and British Columbia. While this study found that  environmental factors did contribute to low birth weight babies, these are not the only factors. This study was particularly interesting because it used the geographical distribution of low weight births and the spatial distribution of environmental factors to try to determine some of the factors contributing to low weight births. Secondly it used publicly available environmental data which demonstrates that this type of spatial analysis can be easily applied in other studies.

US 3D elevation project as a model for a national infrastructure map

A number of years ago an initiative was funded under the ARRA economic stimulus package to initiate what was then called LiDAR for the Nation.  This has resulted in a USGS initiative called 3D Elevation Program (3DEP) whose goal over an eight year period is to develop a 3D elevation map with horizontal resolution of a meter and vertical resolution of 10 cm for the entire U.S. and territories.  This entails LiDAR scanning the contiguous continental U.S. and Hawaii and interferometric synthetic radar over Alaska.  It also includes bathymetric data for the U.S. coastline.  To date 48% of the U.S. has been covered or scanning is underway.  An inventory of these projects is available.

At GIS in the Rockies this year Carol Lydic of the USGS gave an overview of this program and the most recent initiatives to broaden the partnerships that are instrumental in creating the national 3D elevation map.  The program is based on partnerships with federal, state, local, and tribal government agencies, academic institutions and the private sector.

In 2012 a study was completed that estimated the economic value to 27 different sectors of accurate 3D data.  The top five included in this study (National Enhanced Elevation Assessment) are

• Flood risk management                                  $295m to  $502m
• Infrastructure and construction management $206m to  $942m
• Natural resources conservation                     $159m to  $335m
• Agriculture and precision farming                  $122m to $2,011m
• Water supply and quality                                  $85m to  $156m

The total economic benefits for all sectors was estimated to be between $1.8B to $13B.   I find this to be a very conservative estimate. 

A number of years ago I remember hearing about an outstanding program by the state of North Carolina in cooperation with federal agencies that involved a statewide LiDAR scan and the creation of a database of all structures with a footprint of over 800 square feet that allowed estimates to be made of the economic impact of any type of flood anywhere in the state or of a rise in sea level along North Carolina's coastline.  This is of inestimable value in assessing any mitigation project.

In the infrastructure and construction sector, one of the largest contributors to the cost of a civil construction project such as a highway or a railway, is the amount of earth that has to be moved which can be derived from a 3D terrain model.  Having a reliable digital terrain model is of great value to any civil project for assessing alternative right of ways during the planning stage and could also be used during the design and construction phases especially if supplemented by information about the soil and geological structure of the subsurface and location of underground utilities.

I found the 3DEP project particularly interesting because it represents a potential model for creating a national infrastructure map including underground infrastructure.  Federal organizations that could be involved include USGS, National Map, US DOT, FERC, and NERC.  Partners might include NARUC (National Association of Regulatory Utility Commissioners), CGA (Common Ground Alliance), state and municipal DOTs, AGC (Associated General Contractors of America), utility and telecom network operators, general contractors, tribal government agencies, and academic institutions.

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.

Dutch portal sharing information about subsurface goes live

Increasing urbanization throughout the world challenges the sustainable development and resilience of cities.  To address this challenge cities, regions and nations are developing digital twins of their infrastructure.  However, the importance of the ground beneath cities is often overlooked. 

Every day information about the underground is captured during excavation and drilling, but this data is rarely shared and is often recaptured over and over again. In the Netherlands one source of subsurface data is derived from borehole logs, cone penetration tests, and groundwater measuring points. It has been estimated that over the past 120 years the Netherlands has spent € 20 billion collecting this type of data about the subsurface.

Recently the Netherlands has embarked on a national program supported by legislation and standards to expand the collection and sharing of data about the subsurface. In 2015 a new law was passed by the States General in the Netherlands which created the Basisregistratie Ondergrond (BRO) or Key Registry for the Subsurface which is open and accessible to all citizens of the Netherlands. The law mandates that if you excavate or drill you have to share your data with the BRO registry. In addition if when using the data in the registry you find something is incorrect you have to report it.

To fund this effort Rijkswaterstaat, which is the Dutch highway authority and is roughly equivalent to Highways England or the Federal Highway Administration (FHWA) in the U.S., has allocated an € 80 million.

The Key Registry for the Subsurface (BRO) came into force in January, 2018. The BRO registry forms part of the System of Basic Registrations of the Netherlands  which includes location and other data on addresses, buildings, topography and cadastral data. The BRO registry consists of 26 data types, which will become mandatory in installments over five years and all of which include location. On 1 January 2018,it become mandatory to report the first three data types, geotechnical surveys (CPT), groundwater monitoring wells and soil drilling sample profiles. On 26 June 2018, this data became publicly available via the Dutch open data portal PDOK.  Work is underway to implement data models for the remaining data types including geotechnics, soils, and groundwater. Manmade underground infrastructure, apart from mining, is not part of the BRO. All 26 will become mandatory by 2022.

Manmade infrastructure is a separate program steered by national regulation on minimizing excavation damage.  There is a separate portal where constructors put in their planned excavation area and are reported with a joined set of harmonized utility data from all utility companies. Harmonization is done by aligning with the INSPIRE utility specifications. The information model cables and pipes (IMKL) is a Dutch standard data model for the exchange of data about underground infrastructure and is founded on the INSPIRE model for cables and pipes, in which location is specified in the Open Geospatial Consortium (OGC) GML format.

Application of geospatial deep learning to real world problems accelerating

Deep learning is increasingly finding practical applications using geospatial data, specifically, satellite imagery. At HxGNLIve in Las Vegas, Wim Bozelie, Technology Director at Imagem, described some very practical cases in which machine learning, specifically, convolutional neural networks (CNN) were successfully applied to real world problems including detecting blocked canals and differentiating different types of roofs.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded. The basic process to create these models is to find or create some training data, some data to test the model, and then play with the parameters to optimize the model for the test data. There is a long-running competition Imagenet Large-scale Visual Recognition Challenge (ILSVRC) which provides training and test data for evaluating algorithms for object detection and image classification at large scale. A motivation for this public challenge is to allow researchers to compare progress in detection across a wider variety of objects by providing publicly available, relatively expensive data. The validation and test data consist of 150,000 photographs, collected from flickr and other sources of imagery, which have been hand labeled with the presence or absence of 1000 object categories. The object categories are very detailed for the types of objects found on facebook, flicker and othe social media. Some examples,

dial telephone, cell phone, airliner, warplane, airship, balloon, space shuttle,aircraft carrier, Tibetan mastiff, Doberman pinscher, warthog, Arabian camel, dromedary, golden retriever, border collie, boxer, english springer, malamute, malemute, whippet, weimaraner, husky, brittany spaniel, lion, beaver, irish setter...

This training data is not much use for geographic data where typical applications of satellite imagery are for landuse and agricultural identification. Wim talked specifically about a type of deep learning called convolutional neural networks (CNN). For example, remote sensed imagery from a satellite can be used to create a model that will differentiate between corn and potatoes, using factors that can be calculated from satellite imagery such as normalized difference vegetation index (NDVI) (min, max, mean), texture (min, max, mean), vegetation height, and geometric factors such as orientation.  I blogged about how open source code and publicly available training data has been applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil. Wim reported a study in the Netherlands using CNN to identify blocked waterways using overflight imagery. An approach that did not use machine learning was tried first, but it found many false positives and missed some real instances of blocked waterways and the success rate was about 50 %. When machine learning was applied with reliable training data, a success rate to 97 % was achieved.

An important application of satellite imagery is identifying built structures. I have blogged about a competition to use satellite imagery to identify buildings and transportation networks. The winner applied a deep neural network model developed originally for medical image segmentation called U-Net.  Reliable training data was provided by the competition organizers. The winning solution used OpenStreetMap layers and high resolution Worldview multispectral layers as the input of the deep neural network algorithm.  Using the same data NVIDIA demonstrated the ability to automate detection of many road networks using deep learning algorithms and multi-spectral high resolution imagery.  Wim described another application of CNN and imagery in the Netherlands to differentiate residential roofs with dormer windows from those without and discussed how different aspects of the model, such as convolution layers, can be manipulated to improve results.

The essential key to effective application of deep learning is good training and test data - ground-truthed data that involves, for example, someone on the ground identifying whether the fields seen in the imagery are corn or potatoes or something else. There are  publicly available training data sets that can be used to train satellite imagery CNN models. For example, I blogged about the System for Terrrestrial Ecosystem Parameterization (STEP) dataset which has 2000 manually labeled sites covering 17 different land cover types scattered across all continents. Wim described a large publicly available training dataset for Sentinel 2 imagery, which was developed by Eurosat. It contains 30,000 polygons of landuse training data for ten classes of landuse: annual crop, forest, herbaceous vegetation, highway, industrial, pastures, permanent crop, residential, river and sea or lake. In an experiment Wim took 500 training polygons from this dataset for three types of landuse: built-up, pastures and vegetation, and water, to build a CNN model. He applied the resulting model to 25 test areas. Of the 25 test areas the model correctly identified 23, but two were incorrect.

AI has often suffered from inflated expectations, complex code, heavy processing requirements and not very practical applications. There is a growing body of practical applications in the geospatial domain that shows that as a result of the immense processing power available in the cloud machine learning is able to generate fairly easily practical and useful results.  As Chris Holmes pointed out in his talk at FOSS4GNA, the challenge now is to develop reliable and publicly available training and test datasets to enable deep learning models to be created for a broader range of applications using the huge volumes of geospatial data that are now available.

UK Mastermap to be made freely available

As part of the Prime Minister’s London Tech Week roundtable, the UK Government has announced that key parts of the OS MasterMap will be made openly available for the public and businesses to use. OS MasterMap is the definitive source of highly-detailed geographic data of Great Britain.  The government estimates that this will boost the UK economy by at least £130m each year, as innovative companies and startups use the data.  The release of OS MasterMap data is one of the first projects to be delivered by the new Geospatial Commission which I blogged about recently.

This has been a long journey.  Many have argued for years that opening Mastermap would benefit the UK economy.   A study released in February, 2008 estimated the benefits to the UK economy if spatial data were available at no cost instead of the current relatively high cost as compared to the US, for example.  The study estimated that the gross  benefits to the UK economy would be around £168 million a year while a government subsidy to the OS of something between £12 million and £85 million would be required. Overall this implied a net benefit to the UK economy of between £83 to £156 million.  There are risks associated with making the Ordnance Survey, the semi-commercial organization responsible for Mastermap a purely government agency, witness the USGS in the U.S., but there is no doubt that open data has benefited the U.S. economy and other economies.

Low cost sensor enables citizen scientists to measure and share urban air pollution data over the web

The World Health Organization (WHO) estimates that more than seven million deaths every year are linked to air pollution exposure from household and ambient (outdoor) air pollution. But in spite of these statistics the average city has only one or two air quality monitoring devices which are very expensive costing about $1.5 million each. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis Steve Liang, a professor at the University of Calagary and CEO of SensorUp, described a way that citizen scientists can contribute to measuring air pollution using a low cost board and CPU and share via open source geospatial web software to map the resulting measured air pollution in real time.

Based on the latest results from WHO air pollution is now the world’s largest single environmental health risk, linked to 12% of all global deaths. Around 4.3 million deaths every year are attributed to exposure to household (indoor) air pollution, from heating, cooking and lighting using solid fuels. Around 3.7 million deaths every year are linked to outdoor air pollution , including exposure to fine particulate matter from fuel combustion from vehicles and from power plants, industry and biomass burning.

Steve described the sensors, which are typically built by interested people in workshops led by staff from SensorUp, a startup that Steve leads. The devices consist of a sensor that measures PM2.5 (particles less than 2.5 microns in size), temperature and humidity, a CPU and Wifi. Each device costs less than $100. After building the device, each participant in the workshop brings it home and installs it outside within wifi range of their internet router. The sensor reports ambient PM2.5, temperature and humidity every 5 minutes to a central server maintained by SensorUp. It uses the Open Geospatial Consortium (OGC) standard SensorThings API which provides an open and unified way to interconnect Internet of Things (IoT) devices, data, and applications over the Web. SensorUp SensorThings platform is the most advanced OGC SensorThings API implementation.

A web application built on the open source geospatial software Leaflet allows users to view the data on a map in real time, investigate air pollution historically and compare different cities. The data is open and accessible to anyone.  To date about 500 people across Canada have built their own devices and are sharing measured PM2.5. The real-time feed from these devices is mapped here.