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Why geospatial data needs artificial intelligence

January 27, 2021 1 Comments

By San Gunawardana, Guest Author

Advances in geospatial technology have opened up many new possibilities in areas such as national security, urban planning and emergency preparedness. When I was embedded with the U.S. Army as a scientist in Afghanistan, I got to experience firsthand the exceptional value of 3D data. The military used nation-scale imagery and lidar to generate 3D maps that then informed their safety-critical operations. However, since lidar—like most three-dimensional unstructured data—contains incredible complexity and detail, it was painfully slow to analyze manually.

As a result, the impact of this technology was severely restricted by speed and cost due to the significant manual effort required to extract actionable insights. As we looked to the future, where lidar would become commonplace in consumer electronics and automobiles, it became clear that there was an opportunity to combine computer vision/AI with large-scale cloud computing to rapidly and automatically generate actionable insights from 3D data.

Screenshot: Enview

Screenshot: Enview

After returning from Afghanistan, I reconnected with Krassimir Piperkov, a former colleague from ICON Aircraft, and fellow Stanford alum, to launch Enview. Our objective was to automate 3D geospatial analytics and create a living 3D model of the world to help organizations to protect their critical infrastructure and communities.

Powering geospatial data with AI can take the limits off 3D data analytics, prevent threats from becoming incidents, and protect critical infrastructure. What used to take days or months to process can now be done in minutes, enabling analysts, operators, and decision-makers across the public sector to make timely and accurate decisions. By enhancing our understanding of the physical world, this technology empowers us to tackle pressing challenges like wildfire prevention, humanitarian assistance, disaster response, and more.

Let’s take a look at how AI-powered 3D modeling is being put to use.

Digital twins

A living 3D model of the world, or a digital twin, can be used for many purposes. Enview’s software fuses many different data sets together to create digital twins that are global in scale but have high-resolution to enable local decision-making. These digital twins include 3D terrain, vegetation, buildings, and infrastructure such as power lines, roads, and water works. Enview also fuses real-time and forecasted conditions, such as wind, temperature, humidity, traffic, and IoT (internet of things).

This sort of rich representation of the physical world is an incredibly complex big data challenge. Data comes from radically different sensor modalities, with different resolutions, formats, time-domains, and accuracy. AI plays a critical role in automating the fusion of these datasets, by helping to intelligently align and then fuse them into a cohesive entity. 3D geospatial data is particularly challenging, as it is unstructured data, which requires a new generation of deep learning frameworks whose convolutional kernels are specifically developed from the ground up to work on unstructured data. Further, the datasets are massive in scale. A square-mile of 3D lidar data can have hundreds of millions of points; the magnitude of the data easily passes the petabyte scale when one considers applications that span nation-scale areas. In order to process this volume of data, modern geospatial AI architectures must be containerized and dynamically deployable across cloud compute resources to generate timely insights.

AI is essential to help human experts to extract meaningful insight from this overabundance of data. The application of automated workflows allows experts to look at larger areas, with more speed and higher frequencies. This machine-assisted cognition draws upon the respective strengths of people and computers to do what neither could do on their own.

Humanitarian aid and disaster relief

3D models can be built to monitor hurricane hotspots, such as the Gulf Coast, before major storms strike. By layering in real-time weather information such as rainfall, winds, and flooding, these models can help with planning, emergency response, and relief efforts.

This data also provides life-saving insight that can assess damage to buildings, transportation, and downed power lines, in addition to determining where to send medical and relief supplies, and how to best get them there. 3D data can help to lessen the impact of future weather events by updating the baseline understanding of how storms impact coastal communities so they can plan for the future.

Screenshot: Enview

Screenshot: Enview

Infrastructure protection

Inadequate clearances between vegetation and power lines can result in wildfires and unplanned power outages. Many federal, state, and local regulations are in place to mandate clearances, and power line operators monitor their networks continuously to ensure that they abide by these regulations and prevent incidents and outages. However, doing so by walking or flying the lines and judging distances with the human eye is challenging and inaccurate.

The ability to identify the exact location and clearances of high-risk vegetation early, and at scale, lets operators identify, prioritize, and address problem areas proactively. Lidar-driven programs have helped with risk-reduction, but are constrained by the massive levels of manual data manipulation required to derive insights from this 3D data. The automation of 3D geospatial analytics through AI, machine vision, and parallel computing enables the accurate and rapid identification of at-risk areas, protecting critical infrastructure and communities.

Screenshot: Enview

Screenshot: Enview

Fighting wildfires

Devastating wildfires resulting in the loss of life and property have become commonplace in the western U.S. and other parts of the world. The tools and methods previously relied on to keep communities and infrastructure safe are now struggling to keep up with this increased threat.

Geospatial information, including 3D data, provides a digital view of the physical world and, when paired with AI, gives stakeholders the informational edge they need to minimize wildfire damage, injuries, and deaths. This technology can be used to automatically build and update real-time, high-resolution wildfire risk maps that give firefighters and communities more notice when threats are imminent, and provide firefighters with real-time situational awareness when they’re fighting the blazes.

Change detection

According to the Pipeline and Hazardous Materials Safety Administration (PHSMA), third-party excavations are one of the leading causes of pipeline incidents in the U.S. These incidents can lead to service disruptions, expensive repairs, and sometimes serious injuries or deaths.

Detecting signs of excavation or earth movement via aerial patrolling is challenging and costly, while resource limitations make it difficult for pipeline operators to continuously monitor remote areas such as farms. AI-powered 3D maps can be used to monitor topography and accurately detect changes that threaten pipelines in real time.

3D data provides remarkable value when it comes to decision-making as it relates to many different applications—from military defense to protecting neighborhoods from wildfires. However, its success hinges on one thing: speed. The ability to process 3D geospatial data rapidly, and at scale, is made possible through advances in AI and cloud computing. In the future, we can expect to see more exciting and innovative use cases for AI-powered geospatial technology.

Headshot: San Gunawardana

San Gunawardana is co-founder and CEO of Enview, a geospatial analytics company. After finishing a Ph.D. in aerospace engineering at Stanford, Gunawardana went to Afghanistan, where he combined data analytics and remote sensing to detect threats and prevent incidents. He is excited to apply those insights to help the energy sector solve problems. He has done computer vision at NASA, built imaging satellites with the Air Force, and was an early employee at ICON Aircraft.

1 Comment on "Why geospatial data needs artificial intelligence"

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  1. Nice article
    Another interesting Digital Twin application that’s worthy of mention is 3D – Spatially Aware Machine AI. Industrial 3D spatial computers for multiple digital twin machine immersion in cooperative 3D virtual environments, all computing precision real time 3D surface interaction in real time. Although similar to commercial 3D gaming’s electronic consoles, (Xbox™, PlayStation™ and PCs), rather than for entertainment, 3D-SAM consoles determination the complex inter-machine (shape and orientation changes) 3D spatial relationships. These computed 3D spatial inter-surface interaction/ relationships data is then delivered to external mission automation controllers. Essentially, 3D spatial-AI for applications like, airborne refuelling arms, UAV landing on moving platforms, (rolling/ pitching ships decks), mining machines or grain harvesters with discharge chutes, etc.

    For these cooperative machine projects, simple machine centroid (latitude/ longitude/ height) doesn’t provide the complex spatial surface interaction knowledge essential for meaningful real time control. However, from each machine 3D-SAM Processor (console) installed, an infinite amount of precise 3D inter-machine spatial data sets (spatial AI) can be generated for external mission controllers. Australian mining has been employing this technology for some years now for optimised control and alternate/ dissimilar safety anti-collision applications.

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