Deploying broadband internet to rural America is a large challenge. Planning, engineering and deploying 5G networks in those same rural areas is even more complicated. Numerous challenges exist to bridging the divide including funding, political, technical, and engineering issues. Yet help is on the way.
The Director of the Federal Communications Commission, Ajit Pai, recently proposed a $20 billion "Rural Digital Opportunity Fund" to build out high speed internet to help connect up to four million homes in rural areas. Programs like this are helping move the dial on the funding and political issues. In addition, geospatial data already supports the technical and engineering issues behind deploying broadband to rural America. 3D geospatial data and continued improvements to the geospatial data will positively impact the engineering and technical issues for 5G as well. 5G will serve as the enabling technology for autonomous vehicles, smart communities and the Internet of Things worldwide. These services require access to 5G with minimum latency and are as critical to rural areas as suburban and urban.
Our first 5G Geospatial Data blog entry discussed the primary challenges of building the new 5G communications network and why high-quality geospatial data plays such a critical role in its development. 5G requires more small cells, mounted in more places, and placed with more precision than 3G or 4G technology. 3D geospatial data helps network engineers and designers to place, position, and model the infrastructure with accuracy and confidence to transmit to all sorts of connected devices.
Just as your home WiFi signal is lessened by walls and distance, dense buildings create 'urban canyons' and interfere with wireless signals in the urban landscape. To achieve low latency and fast performance, 5G small cells must be placed much closer together than previous 3G and 4G wireless network technology for complete network coverage.
The fifth-generation wireless telecommunications technology known as 5G is coming, and it's going to impact more than just your cell phone. 5G will serve as the data transmission backbone that will enable autonomous vehicles, smart cities, and the internet of things (IoT).
From a technical and infrastructure standpoint, 5G is considerably different from the 4G LTE network in use now. The rollout of 5G is a massive undertaking with significant challenges. Geospatial technology can facilitate and solve some of the most vexing issues. In this blog series, we will examine these challenges and the solutions the geospatial industry can provide.
As you drive across the country, the scenery is ever changing – geography, architecture, nature, and culture. Hidden in plain sight, however, there are many things that do not change: the dashed white line in the center of the highway, the size and shape of interstate signs, and the colors on traffic signals. Almost unnoticed in the periphery are nearly countless signs containing valuable information. A recent Continental Mapping project yielded asset inventory information on over 32,000 signs throughout the Pennsylvania Turnpike, equating to over 20 signs per mile.
Drivers – and increasingly connected and autonomous vehicles (CAVs) – rely on standardized sign and roadway design everywhere from the Pennsylvania Turnpike to the Pacific Coast Highway. The benchmark of these extensive standards is the Manual on Uniform Traffic Control Devices, or MUTCD. Standardization allows drivers to focus on operating the vehicle instead of decoding travel and safety information. Just as with human drivers, when CAVs know what asset shapes, colors and sizes represent along the roadway, they can focus more on the dynamic aspects of driving.
AI is prevalent in all aspects of our lives from the technology behind voice recognition software such as Siri on your iPhone to algorithms that evaluate your credit score or evaluate recidivism amongst convicts prior to parole hearings. I've been doing a lot of thinking lately about geospatial AI and in particular, AI that assists in the creation of geospatial data from imagery, lidar, video, SAR and other modalities. Here are 5 topics on my mind:
1. Geospatial AI is Already Here - And Changing Rapidly
A few years ago, the challenge of identifying a cat or dog in an image was thought to be exceedingly difficult yet within only a few short years, it can now be done using AI with a very high level of success (~98%). As has been often quoted, we are "swimming in sensors and drowning in data" and our industry needs the same ability to extract data from the numerous platforms and sensors at our disposal. A great deal of geospatial AI is developing and we must be aware of it, track its development and use it wisely. Geospatial AI is important for all geospatial professionals whether you are a photogrammetrist, surveyor, engineering, data scientist, CAD technician, database administrator, or GIS Analyst.
By: Paul Braun, Vice President Business Development
The geospatial industry is a prime example of how artificial intelligence can be leveraged to improve its products and services. Not all AI is created equally, however, and that could be an issue.
By: Patrick Murn, Content Marketing & Communications Specialist
In describing self-driving cars, both "autonomous" and "automated" are terms used in common vernacular. For most intents and purposes they have the same meaning. The thought process behind the terminology, however, implies that automated vehicles are one thing – components being developed to reduce the number of items that a human must concern themselves with - and autonomous vehicles – those vehicles that can fully function without human involvement.
Two significant challenges face the GEOINT community today; 1) give geospatial context to regional or global activities that allow an analyst to better evaluate a Key Intelligence Question, and 2) maintain global foundation data that meets the needs of end users.
At first blush, combining the real world with the virtual world while driving sounds like a bad idea. It's bad enough that we can be distracted by the radio or my smart phones let along more technology. However, if used correctly, there is room for augmented reality in the transportation/driving space particularly as autonomous vehicles work their way into common practice.
As a diverse surveying and mapping company, Continental Mapping uses a large variety of software products in its production workflows, as do many architecture and engineering firms. These include Bentley MicroStation, AutoDesk AutoCAD, BAE Systems Socet Set, DAT/EM Summit Evolution, Esri ArcGIS, QGIS, and Certainty 3D TopoDOT. Many of these applications allow extensions to their toolsets by providing the user with the ability to create custom tools or scripts. This helps automate repetitive workflow steps or expand the functionality to address new challenges thus saving time and increasing the quality of the data being produced.
Seems like a bit of an odd question but it's a good one. We need to be asking it more often. At the heart of it is a common concern - what needs and questions can be appropriately answered with a given GIS dataset. The things we map change. A small stream today can be a sizable river in the rainy season. Today's GIS community wants to build and use "live maps" that address these dynamic issues as opposed to "dead maps". To complicate matters, we are looking to to inject Volunteered Geographic Information (VGI), citizen science data or sensor content from the Internet of Things (IoT) into our enterprise geospatial systems. This requires us to evaluate how good an open source data set is in comparison to our base information prior to ingesting it into our enterprise GIS. What it comes down to is that we have no way to quantitatively measure quality, until now.