In the last five years, we’ve witnessed many incredible advances in the geospatial analytics industry. With the ability to launch dozens of small satellites on a single rocket, the number of Earth-observing cameras in orbit has increased dramatically. At the same time, cloud computing and deep learning technologies have advanced such that it’s now practical to analyze petabytes of data in a timely fashion. This has given rise to numerous new applications for geospatial analytics, from flood detection to tracking oil inventories.
SAR image of Lake Houston during Tropical Storm Harvey
Yet even with all of these new developments, the ceiling for our industry remains high. We’ve only just begun to scratch the surface of the relevant use cases. For instance, outside of the typical finance and government sectors, we’re beginning to see increased interest in geospatial intelligence from other industries, such as insurance.
One of the factors powering this increase in use cases is the advent of data sources that utilize more of the electromagnetic spectrum than traditional visual imagery. For instance, we’re seeing satellites launch now that image using synthetic aperture radar (SAR), and satellites that can detect radio waves. These types of data present new challenges in analysis, as they require different kinds of algorithms to detect change and patterns. However, it’s also been interesting to see how many of the techniques we’ve developed for analyzing traditional EO imagery, such as deep learning, are also applicable to these new data sources. This is an area where we’ll continue to see development in the coming year.
Another trend that will gain momentum in 2019 is the use of GPUs to analyze non-imagery datasets. Historically, CPUs have been used for most computing needs, while GPUs have been reserved for tasks that require greater processing power, such as imagery analysis. But today, even non-imagery geospatial datasets are becoming quite massive, which means that you’ll start to see GPUs being used more and more outside of traditional computer vision tasks.
Overall, 2019 is poised to be an exciting year for geospatial analytics, with new applications being developed and advances being made in existing areas. In fact, this is a field with so much innovation on the horizon that it’s impossible to predict for certain what new technologies will evolve in the next twelve months—but it will undoubtedly be exciting to watch!
The wait is finally over! Here is the list of the Top 100 Geospatial Companies and Startups in 2019.
2019 Top 100 Geospatial Companies and Startups List
Below is the table with the list of the companies and startup that we think are the Top 100 in 2019. Please scroll down to bottom of the page to see details regarding their tech stack, funding stage, number of employees, company summary, etc in addition to being able to filter them.
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