xyHt - November 2023 by Diversions Publications, Inc.

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BIM SURVEYING GNSS ARTIFICIAL INTELLIGENCE

INSIDE 12 Mapping Air Quality 17 Inside the GNSS Rover 21 AI and GIS

CONTENTS

NOVEMBER 2023 xyHt [ISSN 2373-7018 (print), ISSN 2373-7735 (online), CPC CPM No. 41437548] is free upon request to qualified subscribers in the United States. The Canadian subscription rate is US $20/year. The International subscription rate is US $40/ year. Periodicals postage paid at Frederick, MD and additional post offices. xyHt is published 10 months a year by xyHt LLC, 6 N. East Street, Frederick, MD 21701. POSTMASTER: Send changes of address to: xyHt Subscriptions, 6 N. East Street, Frederick, MD 21701. Send Canadian changes of address to: Box 697 STN A, Windsor, ON N9A 6N4, Canada. For advertising, editorial, or other information, write to xyHt LLC, Inc. or call 301-662-8171.

Looking Forward By Jeff Thoreson

6 LOCATED

FEATURES

ADVANCES IN TOPOBATHYMETRIC LIDAR BETTER ROADS THROUGH LIDAR

What's Inside Your GNSS Rover (Part 3)

AI POWERED MAPS NEW PRODUCTS / NEW SOLUTIONS

The GNSS rover has evolved into an essential part of a surveyor’s toolkit, although some mystery remains as to what that magic box does and how it does it. This three-part series will demystify the rover’s inner workings.

Mapping Air Quality Neighborhood to Neighborhood

To improve urban air quality monitoring and help keep their citizens healthy, cities around the world are now combining traditional data capture techniques with geospatial technologies.

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BIM SURVEYING CE GNSS INTELLIGEN ARTIFICIAL

AI and Geographic Information Systems (Part 2) INSIDE Quality ping Air 12 Map the GNSS Rover de 17 Insi GIS AI and 21

ON THE COVER: If you think building

information modeling is great, wait for what’s to come.

What's Coming in Building Information Modeling The significance of BIM is felt across the globe, with governments, developers, and professionals hailing it as the linchpin for a smarter, more sustainable future for building and construction. But there’s more to come.

As geospatial behemoth ESRI integrates artificial intelligence into is flagship ArcGIS platform, AI is uniquely positioned to help organizations leverage the value of geographic data for the benefit of all.

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Looking Forward

By Jeff Thoreson

BIM: Into the Future

November 2023 Volume 10 Number 10 Publisher Editor-in-Chief

Jeff Thoreson jeff.thoreson@xyht.com

Director of Sales and Business Development

Chuck Boteler chuck.boteler@xyht.com

Creative Director

Ian Sager ian.sager@xyht.com

Accounting and Classifieds

Angie Duman angie.duman@xyht.com

Circulation

subscriptions@xyht.com Phone: 301-662-8171

Editor, Located

Jeff Salmon jeff.salmon@xyht.com

Editor, Field Notes

Eric Gladhill eric.gladhill@xyht.com

Contributing Writers

THE CONCEPT OF BUILDING INFORMATION MODELING has existed since the 1970s, although we certainly didn’t recognize it then. That’s when the first software tools developed for modeling buildings started to emerge. Those early applications and the computers needed to run them were expensive, so adoption wasn’t widespread.

Shawn Dewees shawn.dewees@xyht.com

Marc Delgado Giulio Maffini Jonathan Ng Juan Plaza Gavin Schrock

Copyright © 2023 xyHt magazine. Printed in U.S.A. No material may be reproduced in whole or in part without written permission from the publisher. The publisher assumes no responsibility for unsolicited material, the accuracy of information supplied by manufacturers, or opinions expressed by contributors.

BIM gradually evolved, though still far from what we recognize today as BIM, as software allowed things like time, cost, manufacturers’ details, and maintenance information to be included in the building model. In the late 1980s computer-aided design programs emerged for personal computers and academics began citing the term ‘building model’ in papers. We believe the term ‘building information model’ appeared first in the early 1990s.

Partners and Affiliates

But it was another decade before the industry began refining and defining the term and building information modeling became a real concept. In 2002, Autodesk released a white paper called “Building Information Modeling” and that helped solidify the concept of a digital representation of the building process. Of course, the last two decades have seen the concept refined and various technologies have helped it evolve far beyond what we ever imagined.

THE

IMAGING & GEOSPATIAL INFORMATION SOCIETY

And now it’s time to look to the future of BIM as contributing writer Jonathan Ng does in our cover story. He argues we haven’t seen the best of BIM yet, and where we’re going is as impressive as where we’ve been. Enjoy the issue.

– JT

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Send your Located items to located@xyht.com

Located

Compiled by Jeff Salmon

Mapping Your World | UAV/UAS | Space | New Products

Advances in Topobathymetric Lidar I’M JUST GOING TO SAY IT: USING AIRBORNE LIDAR TO CAPTURE TOPOBATHYMETRIC DATA is just so cool. Think about it: using lasers beamed down from crewed or uncrewed aircraft to map the bottom of coastal areas, lakes, and rivers? All I can say is “hooray for lasers and science.” That’s why when I saw a new report “Beyond the Surface: Lidar Advances in Bathymetry” by our friends at GeoWeek, I immediately downloaded the PDF and dived in. The report is based on interviews with six industry experts from both geospatial firms utilizing lidar bathymetry, Dewberry, NV5 Geospatial, and Woolpert, and manufacturers of lidar sensors and platforms, Hexagon, RIEGL USA, and Teledyne Geospatial. The industry experts were asked four questions regarding the importance of topobathymetric lidar data, recent technology advances in the field, selection considerations for purchasing or upgrading lidar equipment, and what the future holds for this technology. Here's a quick overview. The importance of topobathymetric lidar data: The experts pointed out that, given that 40 percent of the world’s population lives near an ocean, obtaining accurate topobathymetric lidar data is vital for designing and implementing coastal resilience plans, storm-surge modeling to protect people and property, and keeping navigational charts updated. Beyond coastal areas, the use of lidar bathymetry for river mapping for flood risk analysis and designing flood warning systems are also important uses for this technology. Recent technology advances: The report points out examples of recent innovations such as miniaturization of lidar bathymetry sensors that allow the use of smaller platforms such as UAVs and small helicopters, which are particularly useful for smaller-scale missions such 6

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as riverine and harbor environments and areas where tree cover prevents the use of higher AGL crewed aircraft. Another recent innovation is the ability to process lidar data while still in the air, known as edge computing. Using on-board computers and sophisticated software, operators can process lidar data in real time, which not only reduces overall processing time but allows for re-visiting sites to gather better data without expensive and time-consuming re-flights. Selection considerations for purchasing or upgrading lidar: The panel pointed out several issues that should be considered before purchasing or upgrading lidar gear for topobathymetric applications. Think about the application(s) to match the right lidar to the requirement, shallow versus deep water is one example, cloudy versus clear water is another. Purchasing an end-to-end platform from sensor to software is a good idea to prevent blame games between multiple vendors. Another concept pointed out was upgradability: bathymetric lidar sensor technology is advancing quickly, and you don’t want to get stuck with outdated equipment. Having a vendor that offers a trade-in option can prevent this. What the future holds: As an example of what the future holds for this sector, several of the experts pointed out the continued miniaturization of lidar sensors allowing the use of UAVs for missions in difficult environments. Hybrid shallow and deep water segmented sensors are another innovation the experts are expecting soon. AI, particularly as it applies to data classification, was also mentioned. Next steps: Topobathymetric lidar is an important tool for a growing application. Any firm looking to add this technology to its company’s toolkit should take time to check out this report at the GeoWeek news site.

Kompass: Geospatial Surveying Business Management Software (BMS) ACCORDING TO THE DEVELOPER, KOMPASS BMS SOFTWARE OFFERS a 400 percent return on investment for geospatial surveying companies. Designed by surveyors, for surveyors, it is an allin-one solution to streamline operations. Features include scheduling tasks and equipment, project management and integrated timesheets, proposal management and creation, business insights dashboard, and invoices, expenses and payments.

Better Roads Thanks to Lidar

Avineon Earns Esri Parcel Management Specialty AVINEON, INC., A GLOBAL PROVIDER OF DIGITAL MODERNIZATION, SPATIAL INTELLIGENCE, GIS managed services, and engineering support solutions, has earned the Esri Parcel Management Specialty within the Esri Partner Network. A member of the Esri Partner Network for more than two decades, Avineon is now a Gold Partner and has also been recognized as a Cornerstone Partner with extensive ArcGIS experience. The company has been supporting customers’ parcel management needs throughout its 31-year history. Qualifying to receive the Parcel Management Specialty demonstrates Avineon’s commitment to and expertise in land records, parcel management, and ArcGIS Pro.

LEUVEN, A CHARMING UNIVERSITY TOWN IN BELGIUM, is relying on lasers to keep its streets in tip-top shape. While best known for its high-level educational institutions, medieval architecture, and traditional beer production, Leuven is also positioning itself as a leader in using innovative and cutting-edge technology to manage its road assets. This summer, Leuven urban managers partnered with Xenomatix, a company that develops true solid-state lidar, to create the 3D digital model of the city’s entire road network. Data to build the 3D model of the road was collected by a vehicle that was mounted with an HD camera and paired with a specially designed road lidar scanner developed by Xenomatix. The laser scanner captured data on defects and damages on the road surface, while at the same time mapped the road’s geometry. Some 150 kilometers of streets have already been surveyed, and with Leuven’s urban managers pleased with the results, plans are now on the way to completely scan the entire road network. — Marc Delgado, marc.delgado@xyht.com

Lidar SLAM Equipped Robot Lawnmower Changes Lawncare ANYBODY ELSE TIRED OF MOWING THEIR YARD and looking for a little robotic assistance? The Neomow X is equipped with an advanced lidar SLAM navigation system that ensures precise positioning and boundary adherence. This cutting-edge navigation system creates accurate 3D maps

for lawn navigation, allowing the Neomow X to traverse the lawn with precision and ease, all without setting perimeter wires. This is a significant improvement over other mowers that rely on RTK technology or visionbased systems, which may not perform as well in all lawn conditions.

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EVENTS Trimble Dimensions November 6-8 Las Vegas, NV GoGeomatics Expo November 6-8 Calgary, Alberta, Canada Smart GEO Expo November 8-10 Gyeonggi Provence, Republic of Korea

Quicker Position Fix THERE’S NO DOUBT THAT THE INVENTION OF THE GPS and other Global Navigation Satellite Systems (GNSS) have greatly improved the way we map, locate, and measure. So how about making it faster for GNSS receivers to get a first position fix so that we could all get our jobs done rapidly? Galileo, the European-operated GNSS, is doing just that with the deployment this summer of its improved “I/NAV” navigation message that boosts the satellite system’s time to first fix (TTFF) by a factor of two or three. TTFF is important because it measures the time delay between switching on a receiver until the moment that it starts to provide valid mapping information. To boost the TTFF, the I/NAV delivers the following upgrades: minimal amount of information needed for the receiver to generate a first-position fix, a robust coding method that can recover lost data due to poor satellite visibility, and a more straightforward access to timing information in the navigation message. Intensive tests conducted last year have shown that the new I/NAV does not have any negative impact on commercial Galileo receivers available in the market. “This testing was crucial for the entire Galileo system, as it means that end users are now able to receive a first positioning fix twice as fast, down to just 16 seconds,” said Stefan Wallner, head of the Galileo First Generation Signal in Space Engineering Unit at the European Space Agency (ESA). — Marc Delgado, marc.delgado@xyht.com

RIEGL UAV Development Fleet Expanded by Additional Multicopter Platforms SINCE JUNE 2023, TWO MULTICOPTERS FROM THE DUTCH MANUFACTURER Acecore Technologies, a NOA and a ZOE (X8), have been reinforcing the RIEGL UAV fleet in Horn, Austria. With the ever-growing number of test and calibration flights for production and development purposes at the RIEGL headquarters in Europe, acquisition of additional UAV platforms was necessary and the new carrier platforms complement the existing UAV fleet available in Horn. In addition to test and calibration flights, the two new multicopters are also being utilized for customer demonstrations in Europe. This now makes it possible to demonstrate the complete system (UAV platform and RIEGL payload) live, impressively showcasing the advantages of this proven integration.

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Pacific GIS & Remote Sensing User Conference November 27-December 1 Suva, Fiji 2nd Ramon International Geospatial Intelligence 360 Conference December 4-5 Tel Aviv, Israel GeoWeek February 11-13, 2024 Denver, CO Geospatial World Forum May 13-16, 2024 Rotterdam, Netherlands

Will Your Next Trip Will Be Navigated by AI-powered Maps DRIVERS AND PASSENGERS OF CARS EQUIPPED WITH MAPBOX, a real-time location and navigation platform, can soon expect better and safer rides thanks to artificial intelligence technology. After acquiring $280 million in funding, the U.S.-based company announced it will speed up its efforts to introduce AI into cars that will work “across a wider range of roads and more geographic locations.” The company’s maps are now found on dashboards of vehicles produced by Toyota, General Motors, and BMW, while at the same time powering the geospatial needs of the logistics sector. “This investment will allow Mapbox to bring its AI technology closer to the sophisticated camera and lidar sensors inside the vehicle, so split-second decisions can be made with the best data possible,” said Mapbox CEO Peter Sirota. — Marc Delgado, marc.delgado@xyht.com

Teledyne FLIR Expands Turnkey Neutrino Ground ISR Series TELEDYNE FLIR HAS ANNOUNCED TWO ADDITIONAL MID-WAVE INFRARED (MWIR) imaging solutions for integrators developing ground-based intelligence, surveillance, and reconnaissance (ISR) systems. The Neutrino Ground ISR 300 and Ground ISR 420 offer high-performance imaging, long life, and a low switching cost through the combination of Teledyne FLIR’s MWIR camera modules, continuous zoom (CZ) lenses, and image processing with control electronics from InVeo Designs LLC. With the choice of either 640-by-512 or 1280-by-1024 thermal pixel resolution, the modules provide optimal thermal sensitivity while the zoom, focus, and boresight retention enable superior auto-focus and focus-to-range. They also feature common camera interfaces with 30Hz Camera Link or Gigabit Ethernet and 1080P30 HD-SDI or 720P60 HD-SDI to streamline development further.

Trimble Releases 2022 Sustainability Report BUILT AROUND THE COMPANY LIVING ITS VALUES AND MISSION of transforming the way the world works, a Trimble report features how the company is “Shaping a Sustainable Future” for the planet and the communities it serves. Trimble is also introducing a new way of communicating about its environmental, social, and governance (ESG) commitments—building resilience, empowering people, and leading with integrity—that reflect Trimble’s approach to sustainability as part of its business strategy. The report summarizes Trimble’s sustainability initiatives and performance, highlighting the company’s sustainability approach, end-user industry solutions, community philanthropy, employee engagement and development, as well as diversity, equity, and inclusion (DEI) initiatives. The report’s data aligns with the Sustainability Accounting Standards Board (SASB) standards for electrical and electronic components and software and IT Services. It also aligns with the United Nations Sustainable Development Goals (UN SDGs).

Virtual Surveyor Unveils Photogrammetry App Drone Survey Software VIRTUAL SURVEYOR HAS ADDED DRONE PHOTOGRAMMETRY CAPABILITIES to the latest release of its popular Virtual Surveyor smart drone surveying software. The new Terrain Creator app photogrammetrically processes drone photos to generate survey-grade terrains which then transfer seamlessly into the traditional Virtual Surveyor workspace where the real survey work can be performed. “Virtual Surveyor software is now two desktop apps in one subscription package that create a seamless end-to-end drone survey workflow to save time and money,” said Tom Op ‘t Eyndt, CEO of Virtual Surveyor, which is based in Belgium. “Terrain Creator removes complexity from the drone photogrammetry process, offering a visual and intuitive application to produce an orthomosaic and digital surface model (DSM) from drone photos.” NOVEMBER 2023

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Getting Control of Your Geospatial Business IN THE GEOSPATIAL INDUSTRY, THE CHALLENGE OF MANAGING VAST AMOUNTS OF DATA, coordinating fieldwork, and ensuring seamless communication between teams is ever-present. SurvTech, a renowned name in the industry, was no stranger to these challenges, and with a growing clientele and an expanding team, the need for a robust and efficient business management system became paramount. SurvTech's effort to find the ideal solution was filled with trials and tribulations. The company had tried various systems, but none seemed to offer the comprehensive features and ease of use that managers and company officials desired. The team often found itself grappling with fragmented data, inefficient workflows, and a lack of real-time insights. Then they came across Ray Murphy of Murphy Geospatial in Ireland and discussed the impact of Kompass on businesses in the geospatial sector. Intrigued, they decided to delve deeper. Upon initiating contact with the Kompass team, SurvTech found a responsive and dedicated team ready to tailor the system to their specific needs. The implementation process was smooth, with Kompass offering extensive training sessions, ensuring that every member of Survtech was comfortable with the new system. The Kompass system was built by geospatial, surveying, and engineering professionals, so it is able to tackle the wide range of project management and operational elements that geospatial firms need.

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The ability to import legacy data into Kompass BMS was a gamechanger for SurvTech, allowing the company to have all its valuable data in one unified platform, enhancing decision-making and operational efficiency.

TRANSFORMATIVE RESULTS

The impact of Kompass on SurvTech’s operations was revolutionary. The system's comprehensive features, from scheduling to real-time project tracking, brought about a level of efficiency that the team never experienced before. Some of the benefits included enhanced project management as tracking project progress became a breeze, allowing for timely interventions, and ensuring project deadlines were consistently met. The platform facilitated seamless communication between field teams and the central office, ensuring everyone was always on the same page. The robust reporting features of Kompass provided the SurvTech management with invaluable insights, driving strategic decisions and fostering growth. Reflecting on the journey with Kompass, the SurvTech team emphasizes the importance of embracing change and being open to new technologies. Their advice to other companies in the geospatial sector is clear: "In a world driven by data and technology, having a system like Kompass BMS is not just an advantage, it’s a necessity.”

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A Breath of

FRESH AIR

To improve urban air quality monitoring and help keep their citizens healthy, cities around the world are now combining traditional data capture techniques with geospatial technologies. By Marc M. Delgado, PhD

An Aclima car collecting air quality data in Washington D.C. Credit: Brittany Diliberto / Aclima

f you happen to be driving or walking around Washington. D.C., this past summer, then there is a good chance that you crossed paths with one of the blue and white cars that were cruising along the streets of the U.S. capital. And although the vehicles may look like another ride-hailing car, they are definitely not out and about to fetch passengers. Rather, they are scooping samples of air, which will then be analyzed and mapped for traces of harmful chemicals and particles. These pollution-detecting vehicles are part of a program between the city’s Department of Energy and Environment (DOEE) and climate tech company Aclima. They aim to gather data on air pollution at the scale of neighborhoods and help improve air quality. Since 2015, the company has been collaborating with Google’s Street View platform to map air pollution in various urban locations including Dublin, Hamburg, New York, and San Francisco.

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Charting air pollution at the street-level is made possible by Aclima’s mobile-mapping technology that captures billions of air quality data points as the cars move along the roads, while at the same time effectively ascribing location information to them. Once these data points are plotted in two-dimensional detail, they reveal what the company calls “hyperlocal maps” that provide urban air quality conditions at very high temporal and spatial resolutions. Hyperlocal maps have an advantage over traditional air quality maps derived from stationary sensors because the information they provide comes from a larger area. Stationary sensors, on the other hand, are usually hoisted high up on city light poles or attached to buildings. As such the air quality information that they can provide is only valid for a particular zone, and generally within a narrow radius. Mobile sensors, similar to those employed by Aclima, can actively monitor

pollutants in the air over a wider area and take into account the round-the-clock changes in vehicular traffic. The resulting street-level snapshots of emissions captured by these mobile sensors can reveal varying levels of air contamination block by city block and at different times of the day. Access to this important source of air pollution information can thus help authorities and citizens create local-level air quality mitigation strategies for their own neighborhoods. In the U.S., air pollution is still a problem that plagues many areas. According to the latest State of the Air report by the American Lung Association, a non-profit health organization, more than one in three Americans live in places with unhealthy levels of air pollution. But while D.C. may not be on top of the most-grimy places on the list, the city still experiences spikes in levels of ozone and particle pollution, which

are unsafe for people’s health. These two pollutants will be measured and mapped by the project, along with other noxious chemicals like nitrogen dioxide, carbon monoxide, carbon dioxide, and black carbon. The vehicles have been busy collecting air samples around the four pilot neighborhoods of Ivy City, Brentwood, Buzzard Point, and Mayfair. More areas will be included next summer. DOEE’s interim director Richard Jackson says the city is looking forward to Aclima’s insights and hoping they will provide data on “reducing air pollution and promoting healthier District Wards.” Other cities around the world are likewise leveraging geospatial technologies to improve urban air monitoring activities. From deploying location-aware sensors, to employing maps and apps, as well as commissioning Earth Observation satellites, there are plenty of geospatial ways to make the air in our cities cleaner and healthier. Here are some of them.

port in 2017, one in five of London’s schools are located in areas with poor air quality. In an effort to measure and map the exposure of school children to air pollution, 250 students from five London primary schools took part in the Breathe London Wearables project sponsored by the mayor of London. For a week, each student carried a backpack equipped with air quality sensors that measured particulate matter and nitrogen dioxide (NO2) levels, and which then were automatically geo-tagged using built-in GPS trackers. After scientists at Kings College London analyzed the data, the resulting maps showed not only the

ant and highly polluting vehicles have to pay to enter. Based on last year’s data alone, the measured impact of the zones to the city’s air quality has been very positive, showing a 46 percent reduction in nitrogen dioxide pollution from traffic. The ULEZ has now since expanded to include all of London. “I hope the success of this scheme will act as a blueprint for cities around the world to battle their own toxic air emergencies,” said mayor Sadiq Khan.

POLLENS IN PARIS

Come springtime, most Parisians start updating their smartphones with specially designed apps to help them check

LEARNING FROM LONDON

School kids in London who are reading Charles Dickens’s famous stories will always be transported to a time when their city was black and grimy. The tales were often set when foggy weather mixed Nitrogen dioxide pollution over Washington D.C. and New York, as captured from space by the TEMPO up with the dark smoke and satellite. Credit: NASA soot from the burning of coal that came from heating houses and powering factories. Fortunately for everyone, the use of coal trajectories of the students from home to the one main thing that makes them say in London is now restricted. school and back, but also the correspond“atchoum!” during the season: Pollen. There is, however, a new pollutant that ing pollution measurements at street level. Allergy to pollen, however, is prevalent is tarnishing the air of the city: road transThe maps created from the study not just in the French capital. According port. Roughly half of the nitrogen dioxide confirmed what most parents and teachers to the country’s National Aerobiological (NO2) emissions in London comes from already assumed: the children that walked Surveillance Network (RNSA for its acrovehicles, and the concentration of this gasto and from school through busy main nym in French), at least one adult in three eous pollutant is highest along busy roads. roads were exposed to higher levels of air suffers from allergy in the entire country Aside from causing acid rain, NO2 can pollution than those who chose to travel due to these microscopic-sized grains. But harm the lungs of children who are most through back streets. the poor air quality in the City of Light vulnerable to the effects of air pollution Such valuable information provided by makes allergies to pollen even worse for compared to adults. And even if children the air quality mapping project has now people living there. spend most of their time inside schools, they been used as one of the factors in demarcatIn most of Europe, plants release their are still not spared from exposure to this ing the boundaries of Ultra-Low Emispollen from springtime through autumn, contaminant, especially during their daily sion Zones (ULEZ) in the city. London’s resulting in high pollen concentrations journeys to attend classes. According to a reULEZs are special areas where non-complithat can affect human health for about six

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Students in London wear backpacks equipped with air quality sensors. Credit: Environmental Research Group

months. Combining that with ozone and particulate matter pollution, similar to the levels found in cities like Paris, often leads to higher severity of allergic symptoms among sufferers. Pollen counts are also affected by weather patterns. In general, flowers bloom during warmer and windy periods, resulting in more pollen in the air. But pollen’s relationship with rain is quite special. Although rainwater can wash away pollens from the atmosphere and cause a lowering of pollen concentration immediately after a rain event, the physical impact of the raindrops can also trigger big clumps of pollen to break apart and release even smaller pollen particles into the air, causing another problem for allergy sufferers. Incorporating this interplay of weather and air quality is crucial to effectively track the levels of pollen in the air. “It is very important to act on these two factors, especially near the ring road (of Paris) where there are 11 percent more children who are asthmatic,” said Anne Souyris, deputy mayor of the city in charge of health, in an interview with the French website Geo. In France, there are at least nine pollen-tracking mobile apps that cover the entire country, and each one has its pros and cons. Météo Pollen app is one of them and could just be the most geospatially complete. Created by the French start-up Weather Force, the app pools pollen forecasts from Europe’s Copernicus At-

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mosphere Monitoring Service (CAMS) and the latest local weather information from space-based weather satellites operated by the French National Meteorological Service (Météo-France). Such an approach, which combines high volume data, is definitely nothing to sneeze at, especially when calculations are updated several times a day to achieve even more accurate pollen forecasts. What’s more, by using data sources obtained from different locations, the Météo Pollen app can deliver air pollen information at a greater detail that adapts to the user’s whereabouts. Pollen information is also forecasted two days in advance for plants like grasses, birch, olive, and ragweed. This highly geo-localized and time-sensitive approach makes the app very useful. These forecasts do not take the health impact into account, but they provide the pollen information at much higher spatial resolution for the specific area than other methods.

FORECASTING DUST STORMS IN BARCELONA

It can operate at a peak power of 11.15 Petaflops, meaning it can perform more than 11,000 trillion operations per second. Scientists working at the Barcelona Dust Regional Center are using this high-speed computing power to provide daily dust forecasts that cover much of Europe, Northern Africa, and the Middle East. During last year’s dust storm in southern Europe, for example, the center’s forecasts were vital in helping people in Spain prepare days before the dust reached their area. Citizens took travel precautions stayed mostly indoors during the storm. Excessive dust in the air is harmful to human health

A supercomputer that operates inside a former chapel is helping Barcelona and other cities around the Mediterranean predict when the next dust storm will strike. It does this by combining data from a network of groundbased sensors and several earth observing satellites in space to predict the movement of dust storms in the atmosphere. MareNostrum (“our sea” in Latin) is one of the most powerful supercomputers Interface of the French Météo Pollen app. Credit: Météo Pollen in the world.

Cars in Spain covered by dust from last year’s storm. Credit: iStock

and can cause respiratory ailments. Dust storms can also impede sea, land, and air transport, as well as decrease solar energy output. Timely forecasts are important to prevent the risks associated with such storms, but ultimately the reliability of these forecasts will depend on the quality of available data and technological advancements in computing. According to Carlos Pérez García-Pando, an expert on sand and dust storms at the Barcelona Dust Center, new improvements in global observation systems and techniques show promising prospects. “Dust forecasts that use actual satellite aerosol data perform better than forecasts that depend only on modeling to define initial conditions,” he wrote in The Conversation.

OTHER DEVELOPMENTS

This year’s launch of NASA’s latest satellite, the TEMPO (Tropospheric Emissions: Monitoring of Pollution) is

exciting news for scientists and urban residents. It’s the first space-based instrument to provide hourly measurements in cities of aerosol as well as other air pollutants such as ozone, nitrogen dioxide, formaldehyde, water vapor, and several trace gases. Initial air quality data has already been released, but its coverage is limited only to cities in North America. Closer to Earth, another proof of geospatial technology’s enduring utility in air quality monitoring is the latest Application Programming Interface by Google, Aclima’s long-time collaborator. Made public last August, this new Air Quality API is available from the Google Maps Platform and it will allow developers to map air pollution and pollen information based on the company’s artificial intelligence algorithms and multiple data sources, including monitoring stations, sensors, models, meteorological data, satellites, land cover maps, and live traffic information.

“We’re dedicated to building tools that organize environmental information and make this data useful for companies, cities, and partners,” said Yael Maguire, Google’s vice president and general manager for Geo Sustainability. The search company’s latest foray into air quality mapping is another win for urbanites who may now have the option to follow least-polluted routes using their smartphones. And with AI now making inroads to improve pollution forecasting, it’s good to keep in mind that these hightech tools can only gauge how dirty the air is. The next logical step is to actually clean our environment, and it will require us all to roll up our sleeves. ■

Marc Delgado, PhD, is a GIS specialist who crisscrosses continents teaching GIS in Asia, Europe, South America, and Africa.

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How Navigation Systems are Guiding the UAV LiDAR Revolution

Introducing UAV LiDAR

three dimensions. Data points are collected at a rate of millions per second, enabling highly detailed maps to be automatically generated. This requires simultaneous calculation of the target's position as well as keeping track of the UAV's position and orientation during flight. This is where inertial navigation systems (INS) come in. They can provide the georeferenced position, navigation and timing (PNT) data needed for UAV LiDAR to carry out mapping and surveying tasks with exceptional precision, efficiency and safety.

When the idea of integrating LiDAR (Light Detection and Ranging) technology into unmanned aerial vehicles (UAVs) first emerged, it sparked a new paradigm for both technologies. Suddenly, UAV operators not only gained a bird’s eye perspective Improving data collection with navigaof the land their aircraft flew over but also tion solutions the ability to simultaneously generate 3D Australia’s Nextcore is one company maps with ease. It sent waves of excitement pioneering the adoption of next-generation across several industries, from agriculture UAV LiDAR. One key trend they have and forestry to construction and surveying. observed is the increasing demand for So far, much of the discussion in UAVs capable of surveying at higher altithese industries and tech circles has been tudes, particularly from clients working in focussed on the use of challenging terrain. LiDAR for data colIn a recent lection, object idenproduct build, one tification, robotics key requirement was and autonomous cars. to design a UAV Yet, one frequently LiDAR capable of overlooked area is capturing surhow the integration vey-grade data from of navigation systems 80 meters AGL Example of a LiDAR survey point cloud. could greatly improve (above ground level), Image courtesy of EyeVi Technologies. UAV LiDAR applitwice the industry cations, particularly in surveying. norm. Maintaining survey quality at this altitude, however, requires a LiDAR Why accurate navigation is crucial for system paired with an ultra-precise INS. UAV LiDAR Advanced Navigation’s Certus Evo, a LiDAR operates by emitting light pulses MEMS-based INS boasting performance and measuring their return times to comparable to a fiber optic gyroscope, determine the target’s relative position in proved to be the ideal solution.

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Nextcore’s RN100 UAV-mounted LiDAR, featuring Advanced Navigation’s Certus Evo, a compact, high-performance INS.

Certus Evo demonstrated key advantages over competitors. It was highly accurate, cost-effective, easily integrated, and available in OEM format, making it a superior choice for customers. The end product for Nextcore was a UAV LiDAR capable of surveying areas in hard-to-reach and obstacle-laden environments from up to 120 meters high exceeding their initial goal by 50%.

Have we reached the ceiling?

Despite the success of UAV LiDAR technology across many industries, traditional aerial surveying practices, which rely on piloted aircraft and photography, are still prevalent due to concerns around the expense of UAV LiDAR equipment and its data accuracy in difficult settings. As navigation technologies continue to improve, there is no doubt UAV LiDAR will be able to play an increasingly dominant role in terrain mapping and surveying tasks, offering unmatched accuracy, efficiency and cost-effectiveness. For more information about Advanced Navigation’s case study with Nextcore, please visit advancednavigation.com.au or scan the provided QR code.

Part Three:

What’s Inside

YOUR GNSS ROVER? The third and final installment of a look at the inner workings of high-precision surveying rovers. Gavin Schrock

n parts one and two, which appeared in the two previous issues, we focused on antennas, amplification, housing, channels, boards, and gates. This installment continues with signal matching, positioning engines, and additional components.

MATCHING SIGNALS

To distill the observed signals into something that positioning engines can utilize, the ASIC must match many signal structures. “There is an interface control document (ICD) published by constellation provider, like the U.S. government that defines a signal in space,” said Dr. Stuart Riley, vice president of technology/GNSS at Trimble. “That is enough information for somebody who’s skilled in the art of GNSS, to be able to design the receiver and, and because of code-division multiple access (CDMA), the first phase of receiving a signal is to generate a precise replica of that code.”

similarity, then you’ve got optimal lock and your measurements flow. ASIC design has had to rapidly evolve in recent years to be able to accommodate all the new satellites and signals, as has the processing power for the positioning engines, to be able to do the many more things being asked of receivers. Several manufacturers have introduced new models that can handle this increased load, far greater than rovers of only a few years ago. There are older receivers that can “track” multiple constellations, however, they may not be powerful enough to fully utilize every new signal, some signals of which were

For example, the GPS C/A code, which is the most widely utilized GNSS signal today, includes a short spreading code that repeats every millisecond and lower rate navigation data rate. Then you try and synchronize by matching up the local replica with the one that's been transmitted. “A further step is to generate a carrier replica of the received signal, which also provides the carrier observable,” said Riley. “You have Part of the antenna assembly of the Trimble R12i – Image credit: Michael Dix, Trimble to generate a replica of code that’s generated by this. It’s the same for all of these signals. The Galileo ICD defines the Galileo codes, and you just make a local replica of that, and you have to time and frequency align it in what we call a correlation operation. And at that point, once you get maximum correlation, maximum

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only introduced within the past five years.

ENGINES

“Think of the ASIC as hardware acceleration for signal tracking or correlation operations,” said Riley. “Essentially, they aid in the decoding or tracking of the satellite, then everything else is in software. So, from the ASIC, we get measurements. They’re not in the international system of units. They’re not in meters. They’re just sort of hardware words. We have scaling that we have to apply.” It’s a measurement Components and complete rovers undergo extensive testing. In addition to outdoor testing, anechoic chambers layer that’s in software that are used, where individual variables can be controlled. These include simulated signals, interference, multipath, takes different things from and even playbacks of observations recorded in different environments around the world. Image credit: NovAtel both the software and the hardware, combines it together and gets measurements to cycles, or referring to the way it considers which “First, FGO iterates several times to meters, or whatever units are desired. signals and satellites to include. get a reliable estimation under the strong Then in the software layer beneath, Trimble called one of its approaches nonlinearity, and EKF mostly does this in processors on the board, you have the HDGNSS. Tersus calls its latest engine only once. The iteration leads to comparameasurement engines, sometimes called approach “Extreme RTK.” There are bly high precision for estimated states in processing engines, navigation, or posimany others. each step. The high precision state will act tioning engines. With the refined products Manufacturers continue to improve as a decent linearization point and benefits of the ASIC, these engines do the magic and update positioning engines. There the following epochs. of multilateration. This is where steps like may be many varied approaches, but "Second, FGO maintains a batch of fixing pseudo-range ambiguities happens, when considering the current state measurements and states, while EKF where code, clock, and orbit biases are of GNSS, advances in the underlying may only take one epoch into considapplied, where differential, RTK, and PPP science, and advances in hardware and eration. This batch processing enables approaches are activated. algorithms, the results tend to be very adjusting the states in a wide span, and The reality of taking in so many signals good all-around (caveats about very-lowthus gets optimized estimation results from so many satellites, and (depending cost systems noted). by taking better advantage of the coron approach) putting them under one Not to go too far down the rabbit hole relation between states.” giant filter to process together, takes a lot of approaches for positioning engines, Chi explained where FGO might of horsepower. Also, consider that the but a few aspects could be mentioned. No be preferred: “Since the FGO-based engines are computing positions at a high doubt you’ve heard about Kalman filters, a algorithms need to maintain a batch of rate ( bit.ly/RTK-Rate). For instance, in mathematical rather than a physical filter, state, the dimensions of the solvers are nearly all RTK rovers the default is five or employed in most rovers. relatively higher, causing a comparably 10 times per second (5Hz – 10 Hz, even “Generally, factor graph optimization larger computational load, which is a with a base output of one second, or 1Hz). (FGO) based algorithms exhibit better disadvantage over EKF. Certain specialized applications can solve accuracy and robustness, and extended Therefore, the EKF-based algorithms at even higher rates. Newer rovers need to Kalman filter (EKF) based algorithms are commonly used in cost-sensitive be far more powerful. Again, this can be a have higher computational efficiency,” (limited computing capability) or rechallenge for very-low-cost systems. said Cheng Chi, senior algorithm engial-time-sensitive (needs fast processing) A whole separate treatise would be neer with Tersus GNSS. “Theoretically, products, while FGO-based algorithms are needed to cover all of the varied approaches there are two main advantages of the commonly used in corresponding non-sento positioning solutions. Leica Geosystems FGO-based algorithms over the EKFsitive products where improved accuracy calls their engine approach “Self-Learning” based algorithms. and robustness are also required.”

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In general, just about any rover you pick up is doing EKF, however, FGO is being leverage in some approaches for processing GNSS and IMU together, for instance, for some no-calibration tilt compensation solutions. Be it autonomous, DGPS, RTK, network RTK (NRTK/RTN), PPP, or hybrids, the components of the receiver are designed to distill the raw observations into something the positioning engines can use. The rover must also take into account the clock, orbit, and reference frame differences and biases of each constellation. Once you have a position, the job of the receiver is pretty much complete. The engines do not “think” in terms of datums, units, or coordinates that you use on a day-to-day basis. Projections and transformations are handled in the field software. The same with corrections. One misconception about real-time networks is that they are broadcasting “state plane coordinates.” No, they are just sending observation state representation (OSR) “corrections.” Broadcast precise point positioning (PPP) simply provides state space representations (SSR) of clock, orbit, ionospheric model data, etc. The rover applies the corrections and gives you a position, but everything else happens in your field software.

ADDITIONAL COMPONENTS

For communications between the rover and sources of corrections, a rover can pack a UHF or spread spectrum radio internally (or externally), and/or modem (for IP or NTRIP sourced corrections). Often though, users will use the modem built into their data controller or tablet, or Wi-Fi to a portable or phone hotspot. One disadvantage of built-in modems is that they can eventually become obsolete (as happened with the sunset of 3G). Long gone are the days of connecting the rover “head” to the data controller via cables. Most controllers/tablets connect via Bluetooth, so you will find Bluetooth as a standard component of nearly all newer rovers. Most new rovers have no-calibration tilt compensation, almost a standard in the industry. Internal components to enable this can include IMU, electronic gyros, and accelerometers. We’ve even seen a tilt compensated rover that has downward facing laser to be able to skip measure-ups. While there was a wave of magnetically oriented tilt compensation systems over a decade ago, those were problematic, and surveyors typically would not use them. The new wave of IMU and trajectory-oriented no-calibration tilt systems, introduced only five years ago,

No-calibration tilt compensation has become a standard feature on most new rovers, adding IMU and modified antennas to designs – Image sources: Lee Landman (left), G. Schrock (right)

are getting a much better reception, and have become standard on nearly all new (upper tier) rovers (bit.ly/Rover-Tilt). Cameras are appearing on rovers, for photogrammetrically computing offset points from images. One common approach is to carry the rover parallel to the objects desired, with the camera facing the points of interest, and it will take a series of images as you move. Afterward, in the field software, you pick a point you wish to measure in multiple images. Another variation is picking a point from the live view of the camera, then walking the rover across the scene. It will automatically identify the same point in subsequent images, until it has enough to compute the position of the point. We’re even seeing rovers with two cameras—one for photogrammetry and another pointing downward for stakeout. What could be next in rover components? Solid-state lidar is a likely candidate, not just to do limited “scans” but to assist in point stabilization in the way that mobile mapping systems leverage live SLAM data. For that matter, a SLAM scanner could possibly be added. Those musings aside, rovers continue to evolve. However, the present state of rover technology (and performance), through parallel R&D is rather astounding, and fairly ubiquitous (across the high-end classes of many brands and models). It is difficult to look at a rover and datasheet and conclude that it would have some magical advantage over a different one. Field testing is the only way to determine that. This exercise in looking under the hood of a typical survey rover was (for me) very educational. However, in many ways, that unit on top of the pole is still doing a lot of magic that only GNSS engineers and scientists fully understand. That’s fine, they can keep developing amazing gear, and we’ll put it through its paces in the field, let them know what works, what doesn’t, and what we’d like to see in the future. ■

Gavin Schrock is a professional land surveyor who writes on surveying, mapping, GIS, data management, reality capture, satellite navigation, and emerging technologies.

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Revolutionizing Wide-Area Mapping

hase One, a pioneer in medium- and large-format digital cameras and imaging systems introduced its latest innovation, PAS Pana at last week’s InterGeo in Berlin. PAS Pana revolutionizes country-wide area mapping providing maximum productivity, resolution options, and full data ownership. “With its large swath of 48,800 pixels you can now capture a maximum area in minimal time. Data collection has never been more efficient. We are extremely proud to be able to add this system to our aerial systems portfolio,” said Robert Bosch, Phase One Product Manager Manned. PAS Pana is a seven-camera wide-field system that redefines the landscape of aerial mapping precision and effectiveness. With a strategic configuration comprising five RGB cameras equipped with 150 mm lenses, and two NIR cameras with 70 mm lenses, PAS

Pana reaches a total swath of ~ 48,800 pixels across flight direction. Together with its impressive range of high-resolution images that stretch from an astounding 2.5 cm Ground Sampling Distance (GSD) to an exceptional 30 cm GSD, customers now can cover the largest area with the lowest number of flight hours, while maintaining impeccable image clarity based on their needs. A Phase One customer’s unique need was the beginning of this latest innovation. Mike Mueller, Senior VP of Operations at Surdex Corporation comments, “To achieve the schedule and quality requirements of our clients, we must collect data rapidly. The high resolution and sensitivity of the PAS Pana system provides the speed and performance to meet our clients’ ever increasing requirements.” He continues, “Phase One has built a team of industry experts, backed

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by capable support group, ensuring efficient issue resolution.” As with all PAS systems, also PAS Pana is fully integrated with iX Suite which offers the most effective workflow and supports the mapping projects from planning to execution and processing.

PAS Pana at glance

• 48,800-pixel swath for wide area coverage. • Minimize flight hours, capturing more data in less time. • Pair it with iX Suite, Phase One seamless workflow where data quality is assured from the earliest possible stage. • Operated with a broad range of resolution options. • Choose from a wide range of GSD coverage from 2.5 cm to 30 cm based on your needs. • Plug-and play design for any aircraft type. • Save precious data importing time using IIQ format, only available with iX Suite. • Free from content program restrictions. • Versatility for diverse mapping endeavors.

Learn more here: https://phaseone. ws/3F0Wpf0

What AI Can Do

FOR GIS?

By Juan B. Plaza and Giulio Maffini

Part 2

efore we can even attempt to describe what AI can do for GIS, we need to understand what a GIS is and how it is used. What do these three letters mean? A pretty good definition is: A geographic information system consists of integrated computer hardware and software that store, manage, analyze, edit, output, and visualize geographic data. Much of this often happens within a spatial database, however, this is not essential to meet the definition of a GIS. GIS is a big-umbrella term for literally hundreds of software tools, developed over the past 60 years, each with strengths and focus to support particular disciplines. But many GIS share basic spatial functionality. Unfortunately, despite efforts to develop cross-industry standards, GIS brands using different computer languages, terminology, and interfaces make them all dissimilar. Commercial GIS and their internal algorithms are proprietary and not open to scrutiny. One of the principal differences between the types of GIS is the underlying data model used to represent the real world. The two GIS data models are vector vs. raster. Each has advantages and disadvantages.

In a vector-based GIS, points, lines, and areas (defined as polygons) are used to represent the real world. Geographic features like property parcels and jurisdictional boundaries are precisely and accurately represented. However, defining the boundary between a field and forest edge, deciding where to put the demarcation line is difficult. If the decision to place the line is made by someone who digitizes these features over an aerial photograph or satellite image, the resulting accuracy of the line placement depends on the scale of the underlying image and the skill of the digitizer. Vector data is very precise. That is both an advantage and weakness. How can it be a weakness? Well, if 10 people digitize over a photograph to define where the fields and forests are, you are likely to find that they do not overlap very well. When vector layers are topologically overlaid, spurious slivers of polygons will be created. In the vector data world, we need to understand the difference between precision and accuracy. Vector based representations of real-world features are always very precise, but that should not be confused with accurate.

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Raster-based GIS data works with pixels and attributes associated with that pixel. X, Y, Z, and some thematic attributes. The X and Y accuracy is limited to the pixel size. If the pixel size is one meter, raster is not a very good way to represent a legal survey of property parcels. Raster data are well suited for thematic data. Raster data inherently has a built-in statement of their spatial (the size of the pixel) and the thematic measurement accuracy of the sensor. For many years vector- and raster-based GIS evolved in separate silos. Vector GIS for legal boundaries and cadasters and raster GIS for image analysis software. Today vector and raster data models are accessible and integrated in many GIS. Two developments in the vector data world are worth noting. The first is that there are huge repositories of CAD (vector) digital files that have been created by surveyors, engineers, and architects to represent roads, utilities water, sewer, gas and electrical, and communications infrastructure and building and structures. While CAD data is vector based it is important to recognize the difference with vector GIS data. CAD data are digital drawings of lines that may look like a map but when you zoom in, are often spaghetti like vectors that may lack topology, such as closed polygons or connected networks. There has been a lot of human and technical effort invested in converting CAD drawings into georeferenced and topological GIS vector data. This continues to this day.

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A second major development is the massive increase in the exponential acquisition of lidar point data for densification of urban environments and transportation networks using truck-mounted equipment, drones, and satellites. Being able to combine these massive point clouds requires new GIS functionality because legacy tools lack the algorithms to manage the massive volume of data. Entirely new sets of skills are required to understand how to combine these point clouds in a way that extracts the desired features and preserves the accuracy and precision of the source data. GIS software has become complex. It takes years of training to learn how to properly use modern GIS. The education and training of GIS professionals requires a basic understanding of spatial analysis and a very detailed understanding of the internal capabilities of all the GIS that are out there. Thus far, we have just been discussing data collection and integration. Let us move on to actually using GIS for analysis. To do this we will use two examples.

The first is a traditional one involving a very powerful basic GIS capability— overlaying thematic layers of GIS data to perform a suitability analysis. The GIS layers in this simple example is for a region in Italy. For this region we have: • a DEM Digital Elevation Model from Agency X, • a soil map from Agency Y, • Rainfall precipitation records for several weather stations from Agency Z, and • Property parcels from Agency W. The analytical task is to find the places that are most suitable for growing a particular crop, say grapes for making wine. You know the best slope and aspect conditions, the rating of soils and the ideal precipitation for growing grapes. You specify these as rated conditions, and you want the GIS to make a map showing where the best to worst conditions are for growing grapes. You also want a tabular report that calculates how much land in each property parcel falls into the suitability categories. This is a simple task and tens of thousands of GIS trained professionals can do this analysis within an hour. That is of course, if all the

input data layers have the same projection systems and are aligned and rectified. That is a big if. Map projections are a challenge. Representing a 3D surface on 2D requires making decisions about what map projection system to use. There are literally hundreds of map projections. These are the standard ones, and each can have very specific parameters that increase the possibilities to the thousands. All GIS work is in 3D so a drawing of a map may look like 2D but every point in a vector has a Z or height coordinate. The Z-axis elevation can be captured at the time of digitizing, or it can be added to a 2D vector layer by assigning a height to each vertex. This can be done by overlaying the 2D geographical layer from a baseline Digital Elevation Model (DEM). Another challenge relates to ensuring consistency in the attribution of thematic classes. If the GIS data layer has not been preprocessed, we are talking (depending on the number of input data layers), many hours or days to make them ready

takes. Such mistakes can accumulate and make any analysis incorrect or misleading. For the GIS specialist this grunt work takes a lot of time. An AI application that understands all these basic rules would be a huge benefit. If AI is trained to have the knowledge and capability to rectify separate data sources, it will expedite the speed and quality with which GIS users can perform their work. The economic and financial benefit of using AI to expedite the data preparation process prior to GIS analysis is very large. Preparing spatial data for GIS analysis is probably 80 percent to 90 percent of the work effort of a GIS specialist during a year. If AI could cut the amount of time a GIS specialist spends on data preparation by 50 percent, you would increase the GIS analyst’s output productivity in doing analysis by more than 400 percent. Worldwide, that is likely worth billions of dollars in economic benefits. The second example is about increasing the efficiency and accuracy of spatial

Image Courtesy of Automapp.cloud

for analysis. This is just for a GIS analysis with four layers. If you increase the number of layers to 10 or 100, there is an exponential increase in time required to prepare to perform a proper analysis. Humans require lots of training to do this, and it is easy to make simple mis-

data during the acquisition stage. The example is from the electric utility sector, specifically inspecting transmission and distribution networks for preventative maintenance. Basically, artificial intelligence is the capacity of software algorithms to identify

patterns and make conclusions based on them. This process of “teaching” software algorithm to recognize patterns is called machine learning (ML) and is based on a repetitive sequence of examples that “teach” the AI engine that certain items belong to certain categories. For example, in electric utilities’ high voltage conductors, wire threads tend to brake when exposed to certain elements like wind, corrosion, and constant bending. By photographing hundreds if not thousands of these different broken threads, ML can create an intelligent database of instances where that image means “broken thread.” It is estimated that 750 images can provide 80 percent accuracy guaranteed, with thousands more needed to approach 95 percent. Once the AI algorithm has been taught what a broken thread looks like, the software can quickly scan endless images of electric conductors and determine which sections are broken at a speed that greatly exceeds the capacity of humans to do a similar job. But does AI have the capacity to understand all this about GIS? To put it to the test we had a dialogue with Chat GPT 3.5 one of the available public AI apps. Like GIS there are many AI apps available, and you can now even build your own. The strength of Chat GPT is that it has a two-way natural language interface, and it has already been trained on a general basis. We put 20 very detailed questions to Chat GPT 3.5. From the answers we received, we can confirm that even this natural-language AI application is trained, and it has deep knowledge of GIS spatial analysis. For example, it: • Has detailed operational knowledge of 10 of the top GIS in the industry. • Knows how to perform thematic GIS overlays. • Knows how to ensure that spatial data layers are in a consistent map projection. • Knows how to perform rubber sheeting to align the layer with other layers. • Knows how to derive slope and aspect layers from a DEM. • Knows how to append a height to

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the vertices of a 2D polygon using a registered DEM. • Knows 10 GIS scripting languages for GIS analysis. • Knows the scripting language of ARC/GIS. • Knows how to write scripts for ARC/ GIS and QGIS? • Understands the practical limit of the number of GIS layers that can be overlaid in ARC/GIS and QGIS. • Knows how to clean up CAD data for use in GIS. • Is able to use both vector and raster data layers for spatial analysis. • Is able to assist in editing large lidar point clouds. Because Chat GPT is a text-based AI model, it can only process and generate text-based information. This introduces some limitations. For example: • Chat GPT does not have access to GIS software and cannot accept GIS data to actually perform GIS operations directly. • Chat GPT does not have the capability to analyze the content of photographs or images.

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However, Chat GPT can still give you advice on how to get what it cannot do directly, like: • How to set up an AI application with direct access to your GIS spatial data and software and perform operations on your own computer. • Which other AI models are available to analyze the content of photographs. • How to access the AI models and systems specifically designed for analyzing the content of photographs and images. If you are interested, you can read the full responses that Chat GPT provided to our questions by following this QR code.

So, in conclusion, GIS professionals are uniquely positioned to benefit from AI given the enormous amounts of

data that are gathered and collectively rendered into spatial databases that in essence are the background for all geographical analysis and representations, common in a GIS and also by expediting inspections of enormous amounts of data faster than humans. ■

Giulio Maffini started his career in the 70s as an urban and regional planner. Later he founded a company (TYDAC) to build Spatial Analysis PC desktop software (SPANS). In the early 90s he was part of team that commercialized an all-relational, multi user, Oracle-based enterprise GIS (VISION*) for Utilities, Telecom’s, and Municipalities. He is now an advisor to spatial technology companies. Juan B. Plaza is the CEO of Plaza Aerospace, a drone and general aviation consultant firm that specializes in modern uses for manned and unmanned aviation in the areas of mapping, lidar, and precision GNSS. Our next article will explore what GIS can do for AI.

BUILDING INFORMATION MODELING How cloud integration can unleash its full potential BY JONATHAN NG

n the building and construction industry, Building Information Modeling (BIM) has revolutionized the way professionals conceive, construct, and manage our built environment. Since its introduction in the 1970s, BIM has evolved from rudimentary 2D digital representations to becoming the backbone of modern construction projects. Today, its significance is felt across the globe, with governments, developers, and professionals hailing it as the linchpin for a smarter, more sustainable future for building and construction.

BIM enables a more efficient and integrated approach to designing, building, and managing infrastructure with reduced errors and improved project outcomes. Yet, as the industry is met with the challenges of digitization on the one hand and the huge potential of technological development on the other, the true power of BIM is yet to be fully harnessed. One way in which BIM is developing in this digital context is its integration with cloud-based software. Used most often for planning, design, and coordination for a project, when integrated

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with cloud solutions, BIM can enhance the precision and efficiency of often overlooked areas, such as detailed activity programming, resource management, and progress monitoring. These areas of BIM application are often overlooked, yet bring with them numerous benefits.

DIGITAL SOLUTIONS TO TRADITIONAL CHALLENGES

Considering the complexities of modern projects, ranging from environmental considerations, tight budgets and timelines, and increasing amounts of data generated with little standardization between firms on the best practice to process it, cloud-integrated BIM offers a streamlined solution that gives firms more insight, understanding, and control over their projects. Firstly, cloud-integrated BIM models offer more detailed activity programming to give better insight into projects. This is enhanced even further when it is combined with 4D production planning tools,

which means firms can create detailed plans weeks in advance, ensuring field crews have everything they need before construction starts, thus minimizing delays. Progress monitoring also becomes clearer with digital platforms offering the ability to allocate time phases for budgets so that resources can be accurately allocated and shortages avoided. This leads to better resource management during the building project. Rather than doing this via a time-consuming visual inspection of the site, progress can be recorded and approved automatically, sending real-time updates to all stakeholders. Taking this one step further, technology firms in the building space are also beginning to integrate AI systems to autonomize this often laborious step in the construction process.

Thus, cloud-integrated BIM can create a more streamlined approach to a building project across all stages—from design and planning to on-site construction and management. Each step feeds into the next, simplifying the workflow and optimizing the data leveraged.

BIM TO CLOSE THE DATA LEVERAGE GAP

One company that has embraced cloudbased BIM solutions is the global construction and development company Skanska. Information in the building industry is often siloed, and while this is manageable if the amount of data is small, more often than not construction projects will use large amounts of data that constantly move between different verticals. For example, information gathered during the planning stage will be applied to the design phase and cost estimations will be used to help

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determine activity programming. As a result of this inefficiency, the building industry sees 96 percent of data from a project go unused. With this in mind, making sure key information during every stage of a project is clear and actionable was a primary goal for Skanska while undertaking their digitization journey. For this, they needed high-quality information and efficient digital tools that could manipulate data across all verticals. They implemented a cloud-based integrated BIM platform to combine the software tools they were already using for planning, estimation cost, and design. This way, data between the departments could be consolidated, which led to more meaningful and actionable information during the projects. Simulating the construction design and schedule is just one arrow in the BIM quiver. As Skanska found, the benefits reaped using this technology are felt during the construction phase, too. Integrating BIM with the cloud helps to seamlessly bring any planning data and data captured directly in the field, together into a platform that can be accessed via something as accessible as a mobile application. This allows personnel to record any progress or problems faced in real-time against which the project simulation can update immediately. This way, delays, clashes, and overspending can be forecasted and dealt with promptly. In the face of ever-evolving technology solutions, the building and construction industry stands firmly in a digital renaissance. With the sheer amount of digital solutions on offer and the vast amounts of data they generate across the industry, the goal now is to ensure we can organize, harness, and apply this data in the most efficient ways possible. BIM and its integration with cloud-based solutions help us achieve that by uniting each stage, or silo, of a project into a simplified, coordinated, and optimized workflow. ■

Jonathan Ng is global sales director for Hexagon’s Geosystems Division

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New Mexico State University Geomatics Department 1060 Frenger Mall – Room 130 Las Cruces, NM 88003 Phone: (575) 646-6748 Email: kwurm@nmsu.edu or elaksher@nmsu.edu Website: https://et.nmsu.edu/geomaticssurveying// Fully online program and +2 option. BS Degree

Troy University Surveying and Geomatics Sciences Program Geospatial Informatics Department 344 Wallace Hall Troy, AL 36082 Phone: (334) 808-6727 Fax: (334) 670-3796 Email: geospatial@troy.edu Website: www.troy.edu/geospatial BS Degree, ABET-ASAC accredited www.instagram.com/troygeospatial www.tiktok.com/@troy_geospatial

University of Maine Surveying Engineering Technology Program 5711 Broadman Hall, Room 119 Orono, ME 04469-5711 (207) 581-2340 Email: um.set@maine.edu Website: http://www.umaine.edu/set/svt/ Bachelor Degree. abet-taac

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