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Understanding the macroscope initiative and geoml:


How is it possible to use large amounts of data on a global scale to find patterns in space and time that humans can’t see? The coming together of technologies like LIDAR and machine learning is making it possible to make microscopes, which can be used by businesses and governments to monitor and analyze risks in many ways.

Microscopes have been around for hundreds of years. They are tools that let people see and study things that are too small for the human eye to see. Microscopes can be thought of as doing the opposite. They are systems that are made to find patterns in space and time that are too big or move too slowly for humans to see. In order to work, they need to be able to collect information about the whole planet over a certain amount of time. They also need to be able to process that information and show it in an interactive way. Macroscopic are like geographic information systems, but they also have other tools that use multimedia and machine learning.

planetary geoML and the initiative for the microscope. OmniSci is an accelerated analytics platform that uses data science, machine learning, and GPU to query and visualize big data. They offer ways to look at data visually, which can help track and predict different kinds of conditions for large geospatial areas.

The Convergence of Data and Technologies In a world where the amount and importance of data is growing at an exponential rate, it is becoming more and more important for organizations to be able to use that data. Since data now moves digitally, we have to change how we collect and combine information from many different sources and formats.

Because of this, it can be hard and often requires very complex pipelines to get data from its raw state to a state where it is ready for analysis and then to do the analysis itself. Traditional software approaches don’t scale very well, so teams are turning more and more to machine learning algorithms and pipelines to do things like classifying features, extracting them, and keeping an eye on their condition. This is why companies like OmniSci use ML as part of a larger macroscope pipeline to provide analytics methods for applications like powerline risk analysis and naval intelligence.

OmniSci is using its technology to keep an eye on the plants growing near power lines in Portugal by the district. In partnership with Tesselo, they are using imagery and LIDAR technologies to build a more detailed and flexible picture of the land cover that can be updated every week. Using stratified sampling for machine learning and GPU analytics for real-time integration, they can get and display billions of data points from sample sites for vegetation near transmission lines.

For large-scale projects like the ones above, there are usually two things that are needed: a lot of data and machine learning. A lot of data is needed to accurately represent specific locations, and machine learning is needed to classify the data and keep an eye on it all the time. OmniSci tries to answer the question of how these two needs can be met in a way that is dynamic, fast, and efficient from a technical point of view. The OmniSci software is a platform with three layers. Each layer can be used on its own or with the other layers. The first layer,

OmniSci DB is a SQL database with built-in machine learning that makes it easy to run queries quickly. The Render Engine, which is in the middle, does rendering on the server. It works like a map server and can be used with the Database Layer to turn results into images. OmniSci Immerse is the last layer. It is an interactive part of the front end that lets the user play with charts and data and ask questions of the back end. The OmniSci ecosystem as a whole can take in data from a wide range of sources and formats and talk to other SQL databases using well-known protocols. Traditional data science tools can be used together by data scientists, which makes it easy to analyze the data. OmniSci’s solution is based on the idea that the code should go to the data instead of the data going to the code.

Case Study for Analysis of Fire-Transmission Line Risk

One case study for OmniSci Immerse shows how risk analysis can be done for firepower-transmission lines. Growing plants can pose a big risk to power lines for companies like PG&E, and it can be difficult and inefficient to keep track of the risks as they change. But by combining imagery and LIDAR data, OmniSci is making it easier to map out the physical structures of different areas, like Northern California, so that risk can be assessed without having to go there. OmniSci’s platform uses three factors—physical structure, plant health over time, and different wind speeds in different places—to figure out how likely it is that a fire will strike a tree. They are looking at both the big picture and the small details so that utility companies can decide what actions to take through continuous monitoring.

There are many other ways that macroscope technologies and methods can be used besides the firepower-transmission line risks analysis example. OmniSci gives you a way to do interactive analyses on datasets with more than a billion rows, and they can help you find efficient ways to do important tasks like finding anomalies. Make sure to join the upcoming Data for AI community for the virtual event to find out more about the technology behind OmniSci solutions and how they could be used.


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