Aerodyne And AWS Collaboration Solves Industrial Problems With Drone Data
JAKARTA - At AWS re:Invent, Amazon Web Services (AWS) announced that Aerodyne, the world's leading drone solution provider, has run its DRONOS software as a service (SaaS) platform on AWS.
Using AWS, this Malaysian-based startup will help drone operators around the world to grow their businesses, in the telecommunications, agriculture, surveillance, logistics and energy industries in 45 countries.
DRONOS is an innovative, comprehensive drone service platform, which allows drone users to integrate, analyze, and understand drone data to optimize operations, encourage efficiency, and conduct independent work inspections of the air.
In the agricultural industry, drone technology is used in fields to encourage the implementation of precision agriculture. Aerodyne develops Lake data in AWS using Amazon Simple Storage Service (Amazon S3) to store and convert drone data, including satellite, agricultural, and weather images and data, into exploitable insights.
Aerodyne and AWS also helped overcome global food safety challenges, by developing the Agrimor platform, which enables farmers to use drones in breeding, spraying, plant analysis, and agricultural mapping, thereby increasing their crops by 67 percent.
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Meanwhile, in the global telecommunications industry, drones can be used for activities to monitor, manage and monetize infrastructure appropriately, such as telecommunication towers and antennas that are large and remote with each other.
The collaboration that has been run with AWS strengthens our presence in the global drone ecosystem. Through cloud flexibility and machine learning use, we can combine valuable data to help people from various industries, from agriculture, telecommunications, and energy to make faster and better decisions," said Kamarul A. Muhamed, founder and CEO of Group Aerodyne, in a statement received on Friday, December 1.
Going forward, Aerodyne plans to experiment with AWS's generalized artificial intelligence (AI) capabilities to build a large language model that can help companies plan drone flights better and visualize drone data in the amount of up to nearly 1 petabyte.