GISCafe Industry Predictions 2020 – Part 4

We have received an overwhelming response to our request for Industry Predictions for 2020. This demonstrates that many people are thinking ahead to ways to make GIS and geospatial technology better and more productive in the coming year and beyond.

While we have more exciting editorial topics planned for 2020, we will continue to publish the Predictions that have already been received by our deadline.

This week’s topics range from remotely sensed data, new sensors, machine learning and deep learning, to the shift from terrestrial laser scanners to mobile laser scanning, big data, small UAVs, indoor mobile mapping, automatically generated assets from reality capture. Companies covered include L3Harris Geospatial, RIEGL USA, NavVis, and GeoCloud.

Joey Griebel, North America Sales Manager for L3Harris Geospatial

“The commercial availability of high-quality remotely sensed data has never been greater and 2020 is set to deliver even more with the launch of new sensors and the rise in revisit rates. The first U.S. SAR satellite constellation is set to be launched by Capella Space, and Planet labs has plans to add spectral bands on the SuperDove. New sensors and higher revisit rates offer the possibility for advanced, time-series analysis that will finally realize the promise of remotely sensed data being used as predictive tool rather than primarily a reactive one.

For anyone tasked with monitoring and analyzing changes to the landscape, this abundance of data makes it possible to identify issues anywhere on Earth and to monitor their impacts – on a yearly, monthly and even daily rate. This unprecedented visibility means that threats can be identified early – whether that threat is to the environment, infrastructure, or a population center – and offers a window of time to find a solution before a situation escalates into a full-blown disaster.

But the explosion in the availability of sensors and platforms creates a Big Data challenge for extracting useful information, especially in a timely or proactive way. Software automation supported by Artificial Intelligence (AI) takes complex image processing and analysis capabilities to a new level. Deep Learning, a form of AI, enables the user to ‘teach’ the software how to recognize nearly any feature or condition in the data from spatial, spectral or other characteristics.

While Machine Learning algorithms typically require a user to ‘show’ the computer hundreds of examples of a feature in order for it to be identified, Deep Learning technology (like the ENVI® Deep Learning module) uses iterative algorithms that can learn from just a dozen examples in some cases. In the coming year, Deep Learning will become essential to rapidly classify and make sense of what is changing and how.  This will push the boundaries and increase the possibilities for using remotely sensed data.”

Joey Griebel is the North America Sales Manager for L3Harris Geospatial, supporting customers with the ENVI portfolio for over 10 years. He focuses his time on supporting customers in both the commercial and federal markets, from consultants to scientists to analysts.

James Van Rens, Senior Vice President of RIEGL USA

“Big Lidar Data Gets Bigger

The innovations coming from the RIEGL Development Team have produced mapping systems that have two million Hz performance capabilities. This is great news from a data acquisition productivity perspective. As we are living in an age of big data it is now less daunting than it would have been 5 years ago.

Small UAV Systems Have Bigger Performance

The introduction of the RIEGL VUX-240 for UAVs is nothing short of remarkable. In one very small system you are realizing the dramatic technological development of the last 10 years. Miniaturization is a continuing trend while keeping or increasing performance.

Precision and High-Fidelity Matter

The crisp and precise point clouds of RIEGL’s Ultimate Lidar will continue to be in demand. There are many new systems that have been introduced into the industry that have broadened the field but for Surveying and Mapping it is all about the accuracy.

Information not Data – Answers at the Point of Work

There will continue to be development of faster data processing. There will be new tools that facilitate fast rendering of point clouds to useable formats for BIM and GIS work.

Construction Builds Up

With the introduction of Rapid TLS High Speed Stop and Go Scanning – contractors now have simplified tools that move with them. The dramatic improvements in Site and Materials Management will contribute to Safety, Quality Control and Performance Management. This will directly result in increased adoption of the technology.

X-Ray Vision Becomes a Reality

RIEGL is building new facilities in Europe and North America. These new facilities are being scanned from the ground up and will be demonstration products for the Digital Twins Movement. Just in time, RIEGL has developed X Ray Vision of point clouds to facilitate between the walls viewing of mechanical runs as built.

The Importance of Authoritative Geospatial Information Accelerates

Accuracy Matters; Every use case needs excellent data as the foundation.”

James Van Rens is the Senior Vice President of RIEGL USA, the North American distributor of RIEGL Laser Measurement Systems, which is recognized as the performance leader in the mobile mapping, civil infrastructure, airborne scanning, unmanned, hydrographic, bathymetric, mining and terrestrial based industries. Mr. Van Rens holds a combination BA/BS degree from Marquette University and has over thirty years of experience in remote sensing, 3D mapping, and LiDAR technology.

Mr. Van Rens is an industry leader in 3D mapping techniques and their applications and lectures extensively to professional and lay audiences alike.

Felix Reinshagen, co-founder and CEO of NavVis

“The discipline of Indoor mobile mapping will continue to demonstrate rapid growth in 2020. I predicted last year there’d be greater awareness of spatial data in many industries, and this has indeed come to pass. There’s also been a steady advancement beyond traditional floorplans and BIM models towards powerful digital twin platforms, which enjoy deep integration into enterprise business models.

Looking to the year ahead, I can confidently say that the shift from terrestrial laser scanners to mobile laser scanning will be as much of a paradigm shift as the transition from total stations to terrestrial laser scanning over a decade ago.

The trend of broader adoption was – and continues – to be a driver here, with the standardization of workflows for mobile scanning, ranging from data acquisition to modeling, and a greater understanding of how to integrate them with data from static sources.

Additionally, there are more SLAM (simultaneous localization and mapping) devices coming to the market than ever before, offered by the most prominent players in the AEC industry. These mobile mapping platforms will be available in a broad spectrum of form factors and functionality, addressing both data quality and portability, so it will be easier than ever to select the right tools for your specific use-case.

The benefits they bring – streamlined workflows, better tools, and reduced support requirements – will deliver falling investment and operational costs, which in turn will lead to brand new use cases. As well as expected applications like as-built documentation and BIM modeling, we’re seeing a significant rise in deployment for activities like facilities management, remote visits for construction monitoring, and factory planning and operations.

What else? I predict the rise of automatically generated assets from reality capture, used by a diverse set of professionals. From a flourishing ecosystem of accessible and intuitive software, the seamless byproducts of mobile scanning are mesh models, full-color floorplans, and panoramic images. In addition to surveyors, specialists in other industries like automotive and energy will become increasingly adept at using these assets, completing simple tasks like floor space optimization without having to resort to conventional modeling. The additional value of these assets justifies the effort that goes into creating them.

Another major trend is where the scan becomes the model. The latest generation of 3D scanning devices can produce point clouds and images which are so realistic and so easy-to-use that they reduce – and in some cases eliminate – the need for modeling altogether. This new era of mobile scanning is already being leveraged by factory planners (one of the new use cases cited above) to test alternative layouts by moving around cropped sections of the point cloud without having to model anything. Another value opportunity is to update models on a more regular basis, a real step towards ensuring that 3D models are never out of alignment from the physical counterparts.

The last trend I’d forecast is concerned with artificial intelligence and computer vision. Today, we generate spatial data for the benefit of people. But with the development of autonomous driving, there’s also a growing need for spatial intelligence exclusively for use by autonomous machines. This need will naturally extend indoors, as human and robot work side-by-side within complex built environments for manufacturing, warehousing, and logistics. Scan data that has been correctly captured and processed is paramount to safety and efficiency on the 21st-century shop floor.

All of these trends will build on each other so that the momentum in adoption we’re seeing will change the industry even further over the next year. We expect every player in the industry to adapt to the new reality of mobile scanning and intelligence.”

Felix Reinshagen is co-founder and CEO of NavVis, a company specializing in indoor mapping. He has a Ph.D. in information system research and is active as a speaker and writer on digitalization, AI, 3D mapping, and location-based services.

Dr. Yuri Raizman, CEO & co-founder, GeoCloud

“I recognize four parallel and interdependent trends in the modern development of the geographic information field.

Multiple geo data sources available on the market and a huge amount of geo data acquired every minute and needed to be processed. To the traditional sources of Geoinformation, like satellite, airborne and mobile images and LiDAR, new sources have been added recently – mini satellites and UAV/Drone-based imagery and LiDAR. Their number is constantly growing as the volume of data they create.

From one side, that enormous volume of data enables getting more information about the Earth and getting it more frequently. From another side, this huge volume of data should be processed and should be processed in a fast manner.

In many cases, it is not enough just to provide standard mapping products, but more information about specific objects is required. For such cases, 3D Modeling and Machine Learning and Artificial Intelligence (ML/AI) methods are coming into picture. The amount of data and higher and higher resolution enable an effective use of 3D Modeling and ML/AI methods.

The development of the above methods leads to development of automatic methods of object detection and recognition and this, in its turn, leads to automatic mapping.

To be effective, these methods require computers that are more powerful, more storage and software licenses. In most of the cases, the computers should be GPU based and with a lot of CPU and RAM, that defines its high cost. Software licenses generally cost tens of thousand dollars.

Not every company can afford holding of such highly cost powerful servers in its premises. Moreover, in any case, the company that can afford it pay for it a large sum connected to the hardware itself, software licenses and to its maintenance and updating.

In many cases, the decision of purchasing such costly computer infrastructure based on availability of a large project. However, in many cases, there are no large projects justifying the creation of such an infrastructure, and for small or sporadic projects, such investments are not worth.

www.geocloud.work is a modern cloud-based SaaS (Software-as-a-Service) platform providing solutions for this situation. The platform provides an instance approach to pre-installed 3^rd party desktop software. The software is installed on powerful computers providing high performance processing calculations. Customers do not need to buy software licenses, to buy powerful computers, to maintain the infrastructure, to pay for maintenance and updates. They can use the service on a Pay-Per-Use or a Period Subscription basis and use it only when they need it – no obligation and no minimal payment. 

The platform provides a remote access to a variety of licensed software for 3D Modeling, Rendering, Photogrammetry, GIS, RS, Mapping, Marine Mapping, Cartography, Geodesy, Geology, CAD, 3D CAD, BIM, Airborne/UAV/Drone imagery processing and editing, storage and computers and is used today by hundreds of customers worldwide.

Customers can operate by themselves the software installed on the platform and can install their software on powerful computers and use it or enable it to their colleagues or customers.

For software vendors, the platform provides 24/7 sales channel on a Pay-per-Use or/and a Period Subscription basis, one-time software installation, a variety of hardware configurations (Windows, Linux, GPU, CPU, RAM), an approach to diverse customers worldwide, reduced marketing and sales costs and prevention of piracy.

No special cloud-based software development from software vendor’s side is required.”

Dr. Yuri Raizman, CEO & co-founder, holds the positions of CEO of GeoCloud and Chief Scientist of PhaseOne Industrial – both companies are active in the GeoInformation field.

Prior to GeoCloud, from 2008 until 2017, Yuri served as VP EMEA and Chief Scientist at VisionMap. From 1992 until 2008, he served as Chief of Photogrammetry and Remote Sensing and National GIS Manager at the Survey of Israel (SOI), and as a guest lecturer at the Israel Institute of Technology.  He is also a member of the editorial boards of several journals in the field of Geodesy and Geoinformation.

Yuri has over 30 years of experience in photogrammetry, aerial survey, mapping, GIS, standardization, mapping and GIS products development. He published more than 100 articles on aerial survey, photogrammetry, GIS and standardization, and prepared
more than 20 national technical instructions, standards, and specifications.

He is Vice President of ILSPRS (Israel Society of Photogrammetry & Remote Sensing), a member of IIAC (ISPRS Industry Advisory Committee), and a member of ALSI (Association of Licensed Surveyors in Israel). He served as chairman in the Commission on the GIS Standardization of the Standards Institution of Israel, the Standardization Group of the Inter Ministry Commission on Geoinformation, and the Commission on the Technical Standards and Specifications for Photogrammetry and Topographic Mapping in the Survey of Israel.

Yuri holds a Master’s degree in aerial survey and photogrammetry from Moscow State University of Geodesy and Cartography (MIIGAiK), and a Ph.D. in photogrammetry from the Moscow State Research Institute of Geodesy, Aerial Survey, and Cartography (CNIIGaiK).

Tags: 3D Laser Mapping, autonomous vehicles, climate change, crowdsourcing, data, geospatial, GIS, GNSS, imagery, indoor mapping, Infrastructure, intelligence, laser scanner, LiDAR, location, mapping, maps, mobile mapping, navigation, reality modeling, remote sensing, satellite imagery, situational intelligence, small sats, social media, underground mapping

Categories: 3D Cities, analytics, autonomous driving, Big Data, cloud, crowd source, data, drones, field GIS, geocoding, geospatial, GIS, government, GPS, handhelds, hardware, in car navigation, indoor location technology, laser radar, lidar, location based services, location intelligence, mapping, mobile, Open Source, photogrammetry, public safety, remote sensing, resilient cities, satellite imagery, sensors, spatial data, survey, telecommunications, UAVs, utilities

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