SparkPNT has released the SparkPNT Facet FP, a high-precision GNSS receiver designed to deliver centimeter-level accuracy with a focus on long-term flexibility, ease of use, and open-source innovation.
Built for a rapidly evolving positioning landscape, the Facet FP combines multi-band, multi-constellation GNSS support with fully open-source firmware — giving users a platform that can adapt as technologies continue to advance. Built to last, all models are contained in a robust waterproof cast-aluminum housing, with an internal structure designed for compatibility with the company’s Flex system of GNSS modules.
This gives users the choice between three different modules, plus the choice of having tilt-compensation or not, offering six different options with a range of price-points, securities and accuracies for different needs and applications.
Other notable features include:
MFi Certification
LoRa radio with detachable antenna
Internal survey-grade L1/L2/L5/L6 GNSS antenna
IP-67 rated housing that blocks out dust, water and other environmental hazards
Hexagon | AutonomouStuff’s hardware rack inside the Ford Transit shuttle. (Photo: Hexagon | AutonomouStuff)
When it comes to ground transportation, most of the R&D regarding GNSS is aimed at developing driver-assist systems and, ultimately, driverless cars and trucks. For that purpose, GNSS receivers are integrated with inertial navigation systems, radar, lidar, computer vision and ultrasonics.
Leveraging decades of robotics experience and knowledge of control algorithms, AutonomouStuff, part of Hexagon’s Autonomy & Positioning division, has developed a software stack for autonomous vehicles based on the Apollo open-source software stack.
“Think of this software stack as a brain powering the autonomous platform,” said Kevin Fay, product manager for Hexagon’s platforms and vehicle software business. The software stack can be customized across platforms and to meet equipment needs.
Most recently, in a collaborative project with the National Advanced Driving Simulator at the University of Iowa, AutonomouStuff worked with the Automated Driving Systems for Rural America project to outfit a Ford Transit 350HD shuttle for autonomous operation. First, it created a drive-by-wire system that enabled electronic control of the vehicle, and then it installed positioning, navigation and perception sensors. The result is a platform ready to be autonomous as soon as the software stack is integrated.
Rural roads — which have a wider range of speeds than urban ones — may be encumbered by wildlife or heavy equipment. They also vary in surface from asphalt to gravel, providing a particularly challenging test environment for the autonomy software.
“The Iowa vehicle has done a sizable amount of automated driving on a combination of urban and rural roads, where traditional sensing falls flat,” Fay said. “It has excelled in areas such as gravel roads that have limited or no lane markings, or are narrower than normal. We deployed it earlier this year to do things such as traffic-light detection with the cameras on board, so that it navigates traffic-light intersections appropriately.”
While rural roads are generally free of the GNSS multipath challenges presented by urban canyons, they also provide fewer navigation landmarks. Another challenge is inclement weather. During snowstorms, Fay pointed out, country roads might be unplowed. “If you run on the right lane of the road all the time, you might be out of the ruts that are on the road, and then you’re struggling to get through.” The vehicle must learn to navigate appropriately in those conditions.
The University of Iowa Ford Transit shuttle is a limited deployment, mainly to collect data for research purposes. Meanwhile, it is giving real rides to residents, though with a safety driver. “They’re always attentive, but their hands will be next to the wheel,” Fay said. “There will be times where they may have to take over.”
Other universities and companies are using the platform to further their autonomy programs. Most of them are doing urban driving in complex routes with live traffic, for a total of a dozen vans nationwide.
Hexagon equips the vehicles with a variety of sensors, including a front-mounted adaptive radar, a roof-mounted Velodyne lidar, a roof-mounted NovAtel GNSS receiver and cameras mounted inside the vehicle. “Which ones we provide depends largely on the customer and on which software they’re deploying,” Fay said. “We provide our customers a complete package that can be used with minimal work out of the box. It has the software, the interface to the vehicle, and sensors on it. But we can also provide them with a vehicle that simply has an interface for control, and they add their own computer and software on top of it.”
Hexagon’s first Ford Transit was deployed in 2021. The company released the current version in the spring of 2022, and the Iowa project is slated to run through the middle of 2023. “We’ve not had something running in live traffic before,” Fay said, “so it allows us to continue to grow our skill sets and our overall expertise.”
The Google Smartphone Decimeter Challenge (SDC) competition, co-sponsored by the Institute of Navigation (ION), took place this summer. For the competition, teams developed high-precision GNSS positioning using a pool of smartphone GNSS + inertial measurement unit (IMU) datasets accompanied by high-accuracy ground truth. Teams competed to achieve the best location accuracy with the datasets provided. Winners received cash prizes and sponsored attendance at the ION GNSS+ 2022 conference in Denver, Sept. 19-23, to present their results.
Origins
The SDC has its origins in the Android Operating System, which is an open-source platform. In 2016, Google made GNSS raw measurements available as a public application programming interface (API) on all Android phones. Since then, the available measurements have become more sophisticated and more accurate. For example, dual-frequency carrier-phase data is now available on many Android phones. This enables new areas of research.
Goals
The competition had two goals:
• Stimulate the research and development of high-accuracy algorithms that can produce submeter position accuracy on phones.
• Establish a publicly accessible repository of labeled data so that all future research on location algorithms can be judged in a consistent way against a standard set of data.
The first goal was met beyond our expectations. A total of 1,381 teams participated in the two competitions of 2021 and 2022. Discussion among competitors on the competition platform (kaggle.com) was wide-ranging, incredibly collegial, and beneficial to the entire community.
Competitors have written and shared detailed descriptions, and these have been reviewed and commented on by other competitors. Moreover, winners have written formally peer-reviewed papers and made presentations at the ION GNSS+ conferences, which are available from ion.org.
The second goal is a work-in-progress and is intended to be the legacy of the events.
Legacy
Disciplines such as machine learning have established benchmarks that make it possible to compare new approaches to previous ones in a proper quantitative way. In the GNSS community, this convention has been missing — a glance across papers at conferences will show that different algorithms tend to be presented with different test data and different metrics. Usually, the authors collect this data, and it is often fairly sparse (one or two drive tests, for example). Also, the reader never knows whether the data was cherry-picked (were bad results not mentioned?).
The SDC data provides:
• 206 different drive tests
•86 total hours of dual-frequency (L1, L5) data with code and carrier-phase measurements
•All labeled with ground-truth positions and velocities collected using NovAtel SPAN ISA-100C, with precise lever-arm compensation and validated with Google’s analysis tools.
The Kaggle site allowed users to submit their results, then automatically scored them against the ground-truth data. We advocate that all GNSS researchers use this resource to measure their location algorithm improvements in a standard way. This creates trust in published results, accelerating the recognition and adoption of truly great improvements for the benefit of the entire industry and GNSS users worldwide.
The top three winners of this year’s Smartphone Decimeter Challenge described their projects to Matteo Luccio, GPS World editor-in-chief.
Suzuki
Taro Suzuki, Chiba Institute of Technology
1st Place Winner: Two-Step Optimization of Velocity and Position using Smartphone’s Carrier Phase Observations
What is your research focus and how does it relate to the contest?
My current research focuses on the accurate positioning of vehicles and mobile robots in urban environments where GNSS multipath occurs. I usually use commercial GNSS receivers for my research. This competition is very relevant to my current research, except that the smartphone is replacing a receiver.
How long have you been developing the technology or approach you used to win the contest?
The competition was held for three months, but I concentrated my efforts on the past three weeks. However, I used technologies and resources developed in my previous research (for example, source code developed in last year’s competition).
Have you participated in previous editions of this contest?
Yes, I participated in the last competition and won. The approach used in this year’s competition is based on the method used to win last year’s competition, with additional innovations and improvements.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The competition provides a training dataset, which contains raw GNSS observations from a smartphone installed on a vehicle as it travels on real roads. In addition to GNSS observations, the training dataset contains the ground truth of the smartphone’s position. The training dataset includes a wide range of GNSS signal conditions, such as driving on highways around San Francisco and Los Angeles, driving on tree-lined urban streets, and driving in tunnels and under overpasses. I have developed an algorithm that uses a training dataset containing ground truth to accurately estimate the location of smartphones in a variety of GNSS signal reception environments.
What accuracies were you able to obtain?
The competition metric was “average of 50th and 95th percentile horizontal errors.” The metrics are computed for each of the 36 runs in the test dataset, which are divided into public and private groups, then the metrics are averaged in each group to compute the final score. My final score was 1.382 m for public and 1.229 m for private. The best score given after the competition was 1.372 m for public and 1.197 m for private. The final result achieved sub-meter accuracy in the median (50th percentile).
What are the key features of your approach?
The key point of my method is global optimization using graph optimization, unlike a conventional Kalman filter or least-squares-based positioning methods. In addition, highly accurate relative position estimation using the time difference of carrier wave phases of smartphones contributed to the accuracy. Because the competition dataset included environments such as tunnels and elevated structures in which GNSS cannot be received at all, I devised an algorithm with two optimization steps (first velocity optimization, then position optimization) and applied it to the competition. This method enables highly accurate position estimation for vehicle driving data in various GNSS signal reception environments using only smartphone GNSS observation data.
What end-user applications are you expecting your approach to enable?
Decimeter-accurate location estimation could lead to lane-level navigation for vehicles, pedestrian navigation, and advanced location-based smartphone games.
Dai
Shubin Dai, Kaggle Community
2nd Place Winner: Improving Smartphone GNSS positioning using Gradient Descent Method
What is your research focus and how does it relate to the contest?
I am a data scientist and one of the top competition grandmasters on Kaggle. My research interests include computer vision, natural language processing, autonomous driving, and reinforcement learning. I placed in the top three in 14 related competitions (13 of which were solo). So, despite my lack of background knowledge in the GNSS field, these methods, skills and experiences helped me find a solution.
How long have you been developing the technology or approach you used to win the contest?
I spent about 50 days on this competition, including learning principles of GNSS and understanding all kinds of algorithms by reading books, papers and source codes. The Kaggle platform is very helpful when we want to get started in a new field.
Have you participated in previous editions of this contest?
I did not participate in the competition held last year, but I learned a lot from solutions of recent years, particularly the third-place solution.
Where, in what GNSS signal conditions, and at what speeds were the test data collected?
The benchmark datasets include raw GNSS measurement and raw readings from inertial sensors, using smartphones (Xiaomi Mi 8, Google Pixel 4, etc.) enabled with dual-frequency and ADR (accumulated delta range) in driving scenarios, collected in the San Francisco Bay area.
In the GSDC2021 dataset, there are 29 drives with 73 phone GNSS logs in the training set and 19 drives with 48 phone logs in the test set. Compared to 2021’s competition, in the GSDC2022 dataset we can see more data overall and a wider variety of routes: 62 drives with 170 phone logs are provided in the training set and 36 drives with only one phone per drive are provided in the test set.
The drives in the training set took 15 to 60 minutes at an average speed of 18 m/s.
What accuracies were you able to obtain?
According to the metric of this competition, the score is calculated as the mean of the 50th and 95th percentile distance errors. The score on my local validation set is 1.929 m, the score on the public test set is 1.608 m, and the score on private test set is 1.499 m. When we calculate the mean error, the score is 1.401 m on a validated set, the mean error of 40% of the trips are under 1 m. I think the competition metric is more reliable as the 95th percentile distance error is also important.
By the way, my local validation set is more difficult to optimize than the test set, so the mean error on the test set is expected to be lower than 1.401 m.
What are the key features of your approach?
The competition data is noisy due to multipath effects, non-line-of-sight receptions, receiver noise and missing data, therefore it’s quite challenging. I found that the optimal estimation for each point locally is not stable and can be affected by noise at that point on the track. If we can find a solution to a whole track globally, the noise can be reduced as the model must follow all kinds of constraints, such as geometry constraints, speed constrains, and global acceleration constraints.
Although we could extend the WLS and Kalman filter solution to take more points on a track into consideration, it’s not so easy to model all kinds of constrains. On the other hand, if we use a global optimization method, such as factor graph optimization and neural networks, we can add the constrains easily, which makes it more efficient to conduct experiments.
Following the solution of the third-place winner in last year’s competition, I used the global optimization method by taking into account gradient descent, pseudorange, pseudorange rate, accumulated carrier phase (ADR), phone speed and acceleration constraints of every time epoch on a track. When optimizing the track using gradient descent, the losses are designed to filter out abnormal data and reduce the noise by a series of physical and geometrical rules. I spent much time searching for the constraints, proving them and turning them into losses that can be used to update the coordinates iteratively during the competition.
What end-user applications are you expecting your approach to enable?
According to the setting of this competition, we can post-process data collected using Android phones, which is easily obtained. The track obtained can then be optimized using the solutions from this competition. The solutions from the first and the second place can both be considered as a framework that can be extended by adding more constrains to it to improve accuracy.
Everett
Tim Everett, RTK Consultants LLC
3rd Place Winner: An RTKLIB Open-Source-Based Solution
What is your research focus and how does it relate to the contest?
I develop and maintain the demo5 fork of the popular RTKLIB open-source GPS/GNSS software tool. I have optimized this software for low-cost precision GNSS solutions, so it is very closely related to the goals of this competition. My background is in control system theory and I worked in product and technology development for servo systems in the disk drive industry for 25 years before switching to the GNSS field. The mathematics turns out to be quite similar between the two as both are problems in precision positioning, just different in scale. In disk drives, it is nanometers over centimeters and in precision GNSS, it is centimeters over kilometers.
How long have you been developing the technology or approach you used to win the contest?
I have been developing and maintaining low-cost precision GNSS solutions in the RTKLIB software for about six years but have only worked with smartphone solutions in the last year or two.
Have you participated in previous editions of this contest?
I did not participate in last year’s competition but I did work with the data after the contest was over and shared a solution using RTKLIB that would have placed fifth in the competition.
What accuracies were you able to obtain?
I achieved a score of 1.648 m on the private leaderboard. This represents the average of the 50th percentile and the 95th percentile of the errors as scored by Kaggle. Kaggle does not provide any further breakdown of this number but, based on the training data for which ground truths were provided, this corresponded to a 50th percentile error of roughly 0.9 m and a 95th percentile error of roughly 2.3 m. With a small tweak to my solution after the competition was over, I was able to improve my private leaderboard score to 1.593 m, which would have been within 1 cm of the third-place solution.
What are the key features of your approach?
My approach was to use the existing post-processing kinematic (PPK) solution algorithm in RTKLIB but to reoptimize it for the unique characteristics of the smartphone observation data. A PPK solution is the post-processing equivalent of a real-time kinematic (RTK) solution and is a differential solution that relies on differencing the receiver observations with observations from a nearby base station to cancel out most of the largest error sources — including atmospheric, orbital and clock errors — since these errors are common between the two sets of proximate observations.
Because smartphones have very poor GNSS antennas and they were mounted inside vehicles, the signal quality is much lower and the multipath much greater than those for which the RTKLIB algorithm was optimized. In addition, the smartphones were using the L5 frequency band, whereas RTKLIB was optimized for the more commonly used L2 frequency band. One of the main goals of my optimization process was to include many low-quality observations in the solution that would normally be discarded, but to de-weight them appropriately.
What end-user applications are you expecting your approach to enable?
RTKLIB software is currently used to provide precision solutions for many end-user applications such as surveying, drone photogrammetry, sports tracking, precision agriculture, utility location, marine navigation and ground subsistence monitoring. Although smartphones won’t replace dedicated low-cost GNSS receivers, the challenging nature of the smartphone data severely stresses the RTKLIB algorithms and exposes numerous opportunities for improvement that are much less obvious with more typical, higher quality data. I have pulled these improvements into the main branch of the demo5 version of RTKLIB, and hence this work should immediately improve the quality of all these applications and extend their use into more challenging environments.
Photo: Google
Acknowledgements: Thanks to the Institute of Navigation (ION) for co-sponsoring the 2022 Smartphone Decimeter Challenge. Thanks to Luke Walcher and Tolu Ojelade for their contributions to the photos used in this article.
The open-source release of FGI-GSRx software receiver widens its user base and offers researchers, students and developers a chance to utilize the research platform for innovations.
The GSRx software receiver, developed by the Finnish Geospatial Research Institute (FGI), is now being released as open source for use by the GNSS community.
FGI-GSRx has been extensively used as a research platform for the last decade in different national and international research projects to develop, test and validate novel receiver processing algorithms for robust, resilient and precise positioning, navigation and timing (PNT).
FGI-GSRx has been used to develop algorithms for detecting GNSS jamming and spoofing events in several past R&D projects. It is also used to develop mitigation algorithms to offer a resilient PNT solution to the user.
The FGI-GSRx software receiver will be discussed in the next edition of the textbook GNSS Software Receivers by Borre, Fernández-Hernández, Lopez-Salcedo and Bhuiyan. The book will be published by Cambridge University Press in August.
Uses of the software receiver
The software receiver can be used in universities and other research institutes to provide graduate-level students and early-stage researchers with hands-on training in GNSS receiver development. It can also be used in the GNSS industry as a benchmark software-defined receiver implementation.
The software receiver is already being used in the “GNSS Technologies” course offered widely in Finland at the University of Vaasa, Tampere University, Aalto University and the Finnish Institute of Technology.
The open-source release of FGI-GSRx will enable any third-party developer, researcher or student to use the platform to develop, test and validate innovative algorithms. It offers a flexible interface and configuration files, so that researchers can further implement their own codes or algorithms at different receiver processing stages. This allows the user to go much deeper into the coding without addressing all the implementation details, explained Research Professor Zahidul Bhuiyan, FGI, National Land Survey of Finland.
Meeting evolving industry needs
The GNSS market has faced a transformation in the past two decades, with new features and signal properties being added to the modernized satellite navigation systems at an increasing pace. A software-defined receiver enables algorithm optimization and testing in this rapidly changing industry.
The multi-constellation FGI-GSRx receiver has evolved to provide diversity and improved accuracy. When the FGI-GSRx was first developed, it was able to track the Galileo test satellites GIOVE A and GIOVE B. Since then, FGI researchers have been continuously developing new capabilities to the software receiver with the inclusion of Galileo in 2013, the Chinese satellite navigation system BeiDou in early 2014, the Indian regional satellite navigation System NavIC in late 2014, and the Russian satellite navigation system GLONASS in 2015.
Geoscience Australia is developing open-source software — named Ginan — that will provide real-time corrections to positioning signals of all the GNSS constellations.
Once operational, Ginan will improve the accuracy of location data from 10 meters down to 3 to 5 centimeters for users with an internet and mobile connection. It will enable industry to provide reliable centimeter positioning to their customers, the agency said in a press release.
“Ginan is part of an exciting and innovative Australian Government program to enable precise positioning technology across the whole of the Australian continent,” said Martine Woolf, head of Geoscience Australia’s National Positioning Infrastructure Branch. “It will provide industry with the ability to use precise point positioning, bringing significant economic and social benefits to Australia.”
Examples of how this data could be used include reducing fertilizer and chemical spray waste in agriculture. It could also improve the operational efficiency of large mine sites through greater use of automation.
“Ginan will allow Australians to enjoy the benefits of precise positioning through the creation of new services and products, and in doing so, drive Australia’s economic growth,” Woolf said. “Our precise location data will inform of near real-time atmospheric conditions, which is already being used by the Bureau of Meteorology to assist with their weather predictions. It will also enable a greater understanding of movements in the Earth’s crust and provide insight into earthquakes, sea-level changes and the atmosphere.”
Ginan 1.0 will be publicly released in June 2022. An alpha version is now available on the Ginan GitHub repository, with a beta version planned for user testing from February 2022.
Ginan concept overview. This diagram illustrates how Australia’s network of GNSS ground station infrastructure streams GNSS satellite observations for Ginan to process and analyze, providing correction data to users through an internet connection. (Diagram: Geoscience Australia)
A thoughtful name
Ginan is named for a star that aided the First Australians as they navigated across the continent.
Woolf said the name of the software is a gift from the Wardaman people from the Northern Territory. Geoscience Australia sought permission to use the name Ginan as part of its commitment to respectfully engage and collaborate with Australia’s First Peoples.
“In the language of the Wardaman people, Ginan means ‘a red dilly-bag filled with songs of knowledge’. We like to think of this software as being similar to a dilly-bag full of knowledge because of the benefits it will unlock,” Woolf said. “Ginan is also the name of the fifth-brightest star in the Southern Cross. Just as the Southern Cross helped the First Australians to navigate this land, the positioning capability we are developing here at Geoscience Australia will enable us to know exactly where we are and where we are going.”
Wardaman Elder Diganbal Rosas said the dilly-bag was an important part of the Wardaman songline of the Katherine region. Songlines help to culturally and physically map land and seas through the transmission of traditional knowledge, cultural values, lore and wisdom across the landscapes. They are a living ancient memory code linking the environment, language and culture.
“Ginan [in our language] has all of the Wardaman knowledge regarding connection to country — all of the stars, the skies, the country, the people and the kinship. Everything we do is held in that dilly-bag, in that Ginan,” Rosas said. “The star teaches us many aspects of that spiritual connection to country, how it all began through those songlines, and how that story connects country to the stars. It is significant [that the Wardaman people have allowed Geoscience Australia to use this name] and I think it is a great opportunity for us to showcase our partnership.”
The Ginan initiative is part of Geoscience Australia’s Positioning Australia program, which is improving the accuracy of location-based data across the nation, bringing it from meters to centimeters.
Mowi is an open-source reference design for Septentrio’s highly accurate GNSS module mosaic. It offers Wi-Fi and Bluetooth communication, which can easily be programmed for custom applications.
Septentrio, a manufacturer of high-precision GNSS positioning solutions, has added to its open-source resources for GPS/GNSS module receivers with mosaic wireless, which it calls mowi.
Mowi combines the Septentrio mosaic-X5 or mosaic-H module receiver with a dual-mode Bluetooth and integrated Wi-Fi from the well-known ESP32-WROVER programmable module by Espressif Systems. It is an addition to the already existing mosaicHAT board, designed on the Raspberry Pi platform.
“We are excited about the mowi project being part of the GitHub and prototyping community,” said Gustavo Lopez, market access manager at Septentrio. “The project is available as open-source, thus empowering the community to easily fit autonomous or robotic systems with communication and highly accurate and reliable GNSS positioning technology. Mowi empowers the native Ethernet features of the mosaic module, the perfect tool for fast prototyping and developing proof-of-concept projects in a simple and connected way.”
The mowi project facilitates accurate and reliable GNSS positioning for robotic and autonomous devices, on a hardware level. Numerous engineers today use the ESP32 and the multiple libraries available for internet-of-things (IoT) prototyping. The mowi board is an easy way for integrators to get started with Septentrio’s mosaic-X5 or mosaic-H heading module receivers.
The mowi board can be used on its own or plugged into a mobile computer such as Raspberry Pi or Arduino to deliver high-accuracy positioning with high update rates, suitable for machine navigation, monitoring or control. The internet connection via Wi-Fi or Bluetooth enables numerous industrial IoT applications, simplifying the connectivity to mobile data for the delivery of GNSS corrections needed for centimeter-level RTK positioning.
On top of the wireless communication, the 47.5 x 70 mm board can host IoT applications in its internal memory. It has onboard logging and exposes interfaces such as USB, serial communication and general-purpose pins. The schematic’s reference design, PCB layout and documentation are openly available for prototyping or further customization.
Septentrio, a leader in high-precision GNSS positioning solutions, is offering two open-source resources for its GPS/GNSS module receivers.
The first, ROSaic, is a Robot Operating System (ROS) driver for the mosaic-X5 module as well as other Septentrio GNSS receivers.
The second project, mosaicHAT, is an open source hardware reference design combining mosaic-X5 with a Raspberry Pi single-board computer.
Both projects facilitate integration of centimeter-level reliable positioning into robotic and other machine automation applications.
Photo: Septentrio
ROSaic driver operates on ROS, a widely used programming environment within the industry as well as academics, commonly used for integrating robot technology and developing advanced robotics and autonomous systems. ROS allows data from numerous sensors to be combined allowing high levels of autonomy.
The mosaicHAT project facilitates accurate and reliable GNSS positioning for robotics and automation on a hardware level. Numerous engineers today use Raspberry Pi for prototyping and initial integrations. The mosaicHAT board is an easy way for integrators to get started with Septentrio’s mosaic-X5 GNSS module.
By plugging mosaicHAT into a compatible Raspberry Pi, users have access to high-accuracy positioning with a high update rate, ideal for machine navigation and control, the company said. The small 56×65 mm board exposes basic interfaces such as USB, serial and general-purpose communication pins. The reference design, footprint and documentation are available for easy board printing or further customization.
“We are excited about both the ROSaic driver and the mosaicHAT being part of the GitHub community and we highly appreciate the initial authors work as well as the future contributors,” said Gustavo Lopez, market access manager at Septentrio. “Both projects are available as open source, thus empowering the community to easily fit autonomous or robotic systems with highly accurate and reliable GNSS positioning technology.”
Auterion and Impossible Aerospace are collaborating to bring to market the US-1 UAV, which has a two-hour flight time.
Auterion is the provider of Auterion Enterprise PX4, an open-source-based, enterprise operating system for drones. Impossible Aerospace is Silicon Valley, California-based drone manufacturer on a mission to assemble the highest performance electric aircraft.
“During critical public safety incidents, real-time intelligence from a UAV is extremely important. This is why the two-hour flight time of the US-1 is a clear necessity.” said Spencer Gore, CEO of Impossible Aerospace. “We turned to Auterion for software because their operating system is auditable and trusted for government applications.”
“Public safety organizations can now field a drone with government solicited, cyber-secure and trusted software that enables the drone to stream real-time footage to a command center,” said Kevin Sartori, co-founder of Auterion. “Choosing Auterion and its open-source, open-standards approach will greatly simplify the integration of the US-1 into the IT-infrastructure of public safety organizations.”
Thousands of professional drone pilots and businesses around the world count on open-source flight control software PX4, which was created by Auterion co-founder Lorenz Meier in 2011 and has evolved into a global developer community. Similar to Red Hat, Auterion builds the open-source infrastructure so that drone manufacturers can go to market faster with new products flying trusted software.
The US-1 quadcopter made its public safety debut in February with a California-based police force. The drone gives police agencies a new category of assets that sit between lower-end drones and police helicopters. This enables a wider usage of aerial imagery and reduces the cost for first responders at the same time.
Open access to information of geographic places and spatial relationships provides an essential part of the analytical processing of spatial data. Access to connected geospatial programs allows for improvement in teaching and understanding science, technology, engineering and mathematics.
Emerging Trends in Open Source Geographic Information Systems provides emerging research on the applications of free and open software in GIS in various fields of study. While highlighting topics such as data warehousing, hydrological modeling and software packages, the book explores the assessment and techniques of open software functionality and interfaces.
It will serve as a resource for professionals, researchers, academicians, and students seeking current research on the different types and uses of data and data analysis in GIS.
Topics covered include:
Data Warehousing
Geo-Crowdsourcing
Geospatial Databases
Geospatial Facilities
Hydrological Modeling
Multimedia Codes
Search Mechanisms
Software Packages
Spatial Data Mining
270 pages, ISBN13: 9781522550396/ISBN10: 1522550399, publisher: IGI Global
LocationTech open source project provides core technology for geospatial big-data analytic solutions.
LocationTech has released five open source projects that provide core technology used to build geospatial big data analytics solutions.
A working group of the not-for-profit Eclipse Foundation, the LocationTech community builds software for geospatial technology. The Eclipse Foundation enables collaboration on open source software. Besides geospatial technology, the foundation’s 300-plus open source projects include tools for software developers, system engineers and scientific research.
LocationTech provides technology for the $500 billion in worldwide geospatial industry. Its projects can be used to efficiently process satellite images, analyze maps for the agriculture industry, visualize smart-city sensor data, and in many other geospatial use cases.
The LocationTech community has grown to include nine open source projects, 18 member organizations and more than 100 developers.Collaborating geospatial organizations include Boundless, Red Hat, Radiant Solutions, IBM and Oracle.
“Geospatial big data analytics technology is becoming more and more important across all industries, such as agriculture, transportation, and government,” said Mike Milinkovich, executive director of the Eclipse Foundation. “LocationTech is delivering on the promise of providing key technology for companies that enable large-scale analytics of geospatial data. Having an open source community, like LocationTech, that accelerates adoption and innovation of geospatial technology will have a significant impact on the entire industry.”
The new project releases include the following:
GeoWave is a software library that connects the scalability of distributed computing frameworks and key-value stores with modern geospatial software to store, retrieve, and analyze massive geospatial datasets. GeoWave takes multidimensional data, such as spatial or spatial-temporal, and indexes it into a key-value store such as Apache Accumulo or Apache HBase. These distributed storage technologies, in addition to complementary distributing processing frameworks such as Apache Hadoop and Apache Spark, have proven capabilities to unlock the potential of massive datasets across a variety of domains.
GeoGig 1.2 is a tool for geospatial data versioning. It enables users to leverage versioning of their geospatial data and to enable replication and synchronization workflows, in addition to supporting end-to-end data management workflows. The new GeoGig 1.2 release improves the collaborative version workflow by improving cloning and push/pull performance and provides an updated Web API to align with the latest version of GeoServer.
GeoTrellis 1.2 is a geographic data processing Scala library designed to work with large geospatial raster datasets. The tool provides developers with a set of utilities to help create useful, high performing web services that load and manipulate raster data (data normally used to represent satellite or aerial images). The new release includes a number of optimizations and new features including distributed computation support for viewshed and Euclidean distance through Apache Spark.
GeoMesa 1.3.5 is a distributed, spatio-temporal database built on a number of distributed cloud data storage systems, including Apache Accumulo, Apache HBase, Apache Cassandra, and Apache Kafka. The suite of tools brings spatial-temporal data, real-time IoT, and sensor workloads to the cloud. GeoMesa’s novel indexing schema enables efficient queries resulting in rapid access to large data stores for any client application.
Java Topology Suite (JTS) 1.15 is a Java library for vector geometry providing spatial data types, spatial relationships and spatial operations. JTS is an established open source project that recently moved to the LocationTech community. New technical features for JTS 1.15 include K-Nearest Neighbor search for STR-Tree, improved handling of Quadtree queries, support for GeometryCollection, and a new JTSTestRunner command-line application. This initial LocationTech release the project is changing from LGPL to a dual license of Eclipse Distribution License (EDL) / Eclipse Public License (EPL) . This license change opens up JTS to a wider range of organizations and applications.
“LocationTech is becoming the critical nexus for organizations looking to develop and deploy geospatial Big Data solutions,” says Eddie Pickle, Managing Director of Open Source Programs at Radiant Solutions.
“The latest release of GeoGig to LocationTech represents a huge leap forward. Not only does it support versioning workflows for traditional geospatial data, but it is now optimized for spatio-temporal analysis of big data and streaming datasets from IoT sensors,” says Anthony Calamito, Chief Geospatial Officer and Vice President of Products
The LocationTech Working Group is also organizing the annual FOSS4G NA conference May 14-16, 2018, in St. Louis, Missouri, followed by a Community Day on May 17. Members of the LocationTech community will be speaking and showcasing their open source projects at this conference.
The vision of the LocationTech community is to be the leading provider of core technology for geospatial big data analytics. The five projects being released reflect the growing investment towards achieving this vision.
Boundless Desktop is a native, cross-platform desktop GIS built upon open-source software.
Boundless has been awarded a $36 million contract by the National Geospatial-Intelligence Agency (NGA), the primary source of GEOINT for the U.S. Department of Defense and the U.S. Intelligence Community.
The new contract supports NGA GEOINT Services and purchases services required to package, deliver, maintain and patch accredited open-source geospatial software packages.
NGA delivers geospatial intelligence, or GEOINT, that provides a decisive advantage to warfighters, policymakers, intelligence professionals and first responders. Both an intelligence agency and combat support agency, NGA fulfills the president’s national security priorities in partnership with the intelligence community and the Department of Defense.
NGA also is the lead federal agency for GEOINT and manages a global consortium of more than 400 commercial and government relationships.
Boundless offers an open GIS ecosystem through a combination of technology, products and experts that gives enterprises deeper intelligence and insights using location-based data. The Boundless platform is built upon open source technology and open APIs that generate actionable location intelligence across third-party apps, content services and plugins for enterprise applications.
In November 2016, the company extended its GIS platform with Boundless Connect, a subscription service to a comprehensive repository of GIS data, and Boundless Desktop, a full-featured, professional desktop GIS.
“It is great to see an organization like NGA adopting open source GIS,” said Andy Dearing, CEO of Boundless. “So many organizations are quickly realizing the power and flexibility of open source and the value that Boundless brings to market. This announcement further demonstrates the NGA’s commitment to Boundless and we are excited to continue our work with the agency.”
Last month I raised my anxiety level by writing about a revenant threat from terrorist-initiated biological attacks.
The same concerns were also cited by Director of National Intelligence James Clapper during recent Congressional testimony. These “poor man’s nukes” could potentially be more devastating than 9/11 and reach into every community and even our own homes. Additionally, the threats are not easy to ferret out and just as difficult to stop in our very complex and interconnected world.
From bioterrorism to natural disaster emergency management, predictive analytics used with geospatial tools and big data is proving to be a powerful new intelligence tool that may help counter global threats.
TransVoyant Predictions
TransVoyant CEO Dennis Groseclose.
Last year there was a lot of buzz at GEOINT surrounding a relatively new company in this field called TransVoyant. Several weeks ago, I visited TransVoyant’s Alexandria, Virginia, headquarters to learn more about their capabilities first hand. I was fortunate to be able to speak with TransVoyant CEO Dennis Groseclose, an Air Force Academy graduate who, with Tim Fleischer, a Naval Academy graduate and successful entrepreneur (Radian, PD Systems), co-founded TransVoyant.
Previously, Dennis led industrial base optimization restructure for the $37 billion dollar unmanned space launch program for the U.S. Air Force; directed and implemented Worldwide Supply Chain Optimization for IBM; and served as vice president at Lockheed Martin. These experiences built his expertise to solve complex supply chain and global risk management problems using advanced analytical intelligence. In 2011, Dennis and Tim put their collective experience together to form TransVoyant, a company that specializes in creating live and predictive insights from real-time big data.
The Internet of Things (IoT) has been a key component of their operation. In the mid-80s, connected remote sensors numbered in the thousands. In 2016 that number is expected to reach 6 billion connected “things” worldwide with estimates of 30 billion by 2020.
TransVoyant collects, cleanses and analyzes over 200,000 external events around the world every minute (such as severe weather, natural disasters, labor strikes, inventory locations, news, terrorism incidents, disease outbreaks and energy prices) from real-time IoT data sources such as sensors, radar, GPS, satellites, smartphones and meters. It then continuously applies advanced data scientist-crafted analytics to these data streams to assess important current and future behaviors, impacts, correlations, patterns and exceptions that deliver live and predictive insights ranging from forecasts of port disruptions and precise shipment arrival times to forecasts of economic flows to real-time and predicted threats to people and assets. The resulting insights — provided via cloud services, system API connections, email and mobile applications — improve mission-critical decision making.
The geospatial grid connection
This was all sounding like science fiction and black magic until an “aha moment” for me, as Dennis explained how they use a “multi-dimensional grid cell mathematics” based data structure to apply complex algorithms to real-world data and events. This put the very complex process of continuous real-time machine analysis that “understands” normal and abnormal behavior, both current and future, into something that was familiar to me.
Decades ago, I used the first release of ArcINFO GRID, now called ArcGIS Spatial Analyst, to complete my master’s thesis. For those of you that haven’t used a grid-cell-based GIS, let me highlight the differences between that and traditional GIS software.
Traditional GIS software describes our world as points, lines or polygons with topology describing the mathematical spatial relationship between each geographic element and its linked record in a database. This topological model is somewhat cumbersome and slow because the shapes and topological relationships are complex.
Grid: David Kidner, Mark Dorey & Derek Smith, University of Glamorgan, Wales, U.K. CF37 1DL
The other kind of GIS is a grid cell or raster-based GIS. The data model is significantly simpler because — unlike a traditional GIS of points, lines and polygons — the grid-based GIS world is broken up into simple uniform grid cells.
The big advantage is that the data structure and tools lend themselves to very fast processing. Almost any mathematical formula can be used to operate on the individual or collective grid cells. Most grid-based systems use predefined mathematical operations such as shortest path analysis, interpolation including Kriging or very complex formulas using map algebra.
So, very similar to the famous Napoleon Hill quote, “Whatever the mind can conceive… it can achieve.” With a grid cell GIS, if an analyst can think of a way to describe an analytical process and predictive results as a mathematic expression or formula, it can be done very quickly in the grid cell environment. (See two previous columns for more in-depth information — “GRID Cell Modeling” and “Topology is not Topography”.)
So what does grid cell GIS look like in action?
Evacuations during a flood.
Proactive Emergency Response
In my discussions with Dennis, a TransVoyant customer segment that caught my attention was support of first responders. Emergency responders are using TransVoyant to help with very early disaster response. One specific example is evacuation of invalid patients before a flooding disaster becomes life threatening.
A hospital evacuation.
Using TransVoyant’s analytics on an extensive network of satellite imagery, 911 and 311 calls, water-stage sensors, street closures, weather forecasts, registries and more, responders can predict areas that are at high risk for flooding hours before flood waters rise. Among other essential emergency management actions, these early warnings provide emergency responders with the ability to identify specific neighborhoods and homes that have disabled residents who can be easily evacuated, increasing the safety and efficiency of their operations.
Here is a screen capture of TransVoyant Continuous Decision Intelligence (CDI) predicting a flood event.
TransVoyant Continuous Decision Intelligence (CDI) predicts a flood.
Other Clients
TransVoyant’s live and predictive insight solutions have attracted customers ranging from large multinational corporations to National Security and Intelligence agencies.
I know that one hears echoes of Minority Report when reviewing the tools and capabilities of TransVoyant. However, given the serious threat we face for a situation far worse than 9/11, I have no reservations about using open-source data aggregation and brilliant analytics that correlate and uncover patterns of life and global anomalies to divine a threat.
So, will predictive analytics be the tool that stops a bio terrorist or saves lives in critical emergency situations? I don’t know, but the potential threat is too grave not try every tool, including continuous precognition, in our collective toolbox.
TransVoyant will be an exhibitor at GEOINT 2016 this month, so stop by and learn more.
Since we are approaching Mother’s Day and Memorial Day, I’d like to call your attention to my May 2014 column. It’s the best column I ever wrote.
