Have you ever wondered how much weather reporting standards changed in the last 10-15 years?
Did you know that environmentalists are among the top users of spatial data analysis software for tracking forest covers, ocean coral density, and mining activities?
Did you know India managed to safeguard 1 lakh square kilometers of its border areas using specialized Big Data models?
All the examples that I gave you are associated with the recent advancements in the fields of spatial data analysis, merged with improvements in sensor data, computer vision, and nanotechnology – all converging at the pinnacle of 3-D graphical representation of spatial data. We call it LiDAR or LADAR or Lidar. It is a short form for Light Detection and Ranging, which involves the use of laser imaging with spatial data analysis and Big Data.
Now, you might be wondering how Big Data makes LiDAR so effective!
New-age LiDAR techniques go beyond the traditional imaging and 3-D scanning techniques. We are building specialized Big Data training programs specifically created using the information on extracting data related to Earth’s various topologies, contours, and landscapes. These are used to create high-resolution data maps for specialized applications such as Surveying, Laser Guidance, Seismology, Oceanography, and Atmospheric Physics. With the new gen Big Data also pushing the bar higher in the fields of Astrophysics, Space research, Mining, and Drones, we find LiDAR companies extensively utilizing automation and Computer Vision techniques for telemetry and altimetry applications, something you would find in airplanes, drones, and automated cars and driverless underwater submarines.
Example of Big Data training in LiDAR
Let’s take a specific example of Big Data training for Lidar applications. In the aeronautical industry, we are seeing the advancement in the altimetry toolbox. One such toolbox is called the “Toolbox for LIDAR data Filtering.” This data is used to build software using digital terrain mapping fitted to simulated planes or drones and then interpolated to digital terrain models based on spatial data analysis. Lidar metrics and Big Data modeling can be used to then provide an accurate picture of obstacles that could come during the actual flight experience.
It would be callous on our part to think of Big Data as the only domain that is making Lidar grow. There are other emerging techniques within LiDAR development, such as AI and Machine Learning, Robotics, Remote sensing, GIS /GPS, and Augmented Reality. With rapid strides made in telecom networking such as 5G / 6G and IoT, we are witnessing the massive pooling of data from countless other sources, including beacons, CCTVs, and drone cameras. All these have given rise to a new technology stream within LIDAR, called Point Cloud that houses Big Data management software, AI ML tools, IoT, AR VR, and Computer Vision tools.
From Point Cloud management to LiDAR Data management, Big Data training is massively influencing the future of remote sensing, position telemetry, and navigation systems for land, air, water, and space.