In this special guest feature, San Gunawardana, Co-founder and CEO of Enview, discusses the marrying of AI and mapping, and why the two matter for the future of national security, disasters, infrastructure and city planning. Enview is a company creating living 3D models of our rapidly changing world. San’s background includes a PhD from Stanford on AI, embedding with the Army in Afghanistan as a technology consultant, and aerospace at NASA and ICON Aircraft.
In the modern world, geospatial intelligence has found a wealth of use cases from the military to energy transmission, smart cities and more. Going beyond traditional satellite imagery, most 3D geospatial data is generated using LiDAR scans to create precise point clouds representing objects in space. These scans, however, are painfully slow to analyze manually because LiDAR, like most three-dimensional unstructured data, contains incredible complexity and detail. A single scan can contain millions upon millions of data points.
The manual effort required can understandably make extracting actionable insights from these huge datasets costly and slow. Using AI and computer vision, however, geospatial practitioners can skip the tedious manual work and automatically generate insights from 3D data in fraction of the time, especially with advances in cloud computing and storage.
The analytic power of AI is a massive boost in finding the “needle-in- a-haystack” issues hidden within millions of data points, like a tree branch too close to a transmission line or early signs of a landslide underneath critical infrastructure. What once took days or even months to process can now be done in minutes, enhancing our understanding of the physical world as we address pressing challenges like wildfire prevention, humanitarian assistance, disaster response, and more.
Let’s see how AI and 3D modeling are being used today.
A living 3D model of the world, or a digital twin, can be used for many purposes. Software solutions work to fuse 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. Software solutions 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 hot-spots, 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.
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.
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.
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.
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