Apple's latest Pro and Pro Max series features a LiDAR scanner, allowing night mode portraits and a faster autofocus in low light. The LiDAR scanner on your Apple device not only helps you take sharper pictures but lets you enjoy helpful augmented reality apps and fun games.
For most consumers, this is their first experience with LiDAR technology. But light detection and ranging has been around for more than 60 years. Hughes Aircraft Company built the first LiDAR prototype in 1961. One of the earliest LiDAR applications was by the United States space program, which used the technology for remote sensing to map the moon’s topography during the 1971 Apollo 15 mission.
Today, LiDAR is more down to earth, with an expanding range of LiDAR applications in computer vision and automation. It’s commonly used for object detection in industries like agriculture and utilities—especially to inspect power lines, pinpoint crops for fertilization, detect fruits, estimate and monitor forest canopy structures, identify urine patches in pastures, and assist with pruning fruit trees using high-resolution LiDAR data.
💡Related article: CloudFactory quickly annotated LiDAR data for LineVision, reducing turnaround time by 66%.
LiDAR, or light detection and ranging, is an active remote sensing technology that uses light in the form of a pulsed laser to measure distance.
Here's what makes LiDAR special:
LiDAR allows for visibility through dense environments, such as forest canopies. It can create high-resolution digital elevation models with vertical accuracy of up to 1 centimeter. A LiDAR device has several components: a laser scanner, a GPS, and an Inertial Navigation System (INS). The equipment typically mounts onto a mobile vehicle, such as a drone, UAV, or automobile.
Need high-quality LiDAR data for your AI development? CloudFactory has AI-driven solutions designed for efficient LiDAR data labeling so you can get to market faster. We blend advanced automation with human expertise to streamline the annotation process. Contact us today.
Functionally, LiDAR systems are either airborne or terrestrial. Here’s a brief look at each.
Airborne LiDAR is placed on a drone or helicopter and is helpful for applications that require a bird’s eye view of a vast area. Here, two types of standard LiDAR. The first, topographic, uses a near-infrared laser to map land areas. The second, bathymetric, uses a green water-penetrating light to map underwater terrain.
Terrestrial LiDAR works on the ground and is either mobile or static. Mobile LiDAR systems mount on moving platforms such as autonomous vehicle AI applications to identify objects in the driving environment.
Unlike mobile, static LiDAR systems are installed on stationary structures such as tripods—this type of LiDAR is prevalent in archeology, surveying, mining, and engineering.
LiDAR data is accurate, fast, and beneficial for any location where the structure and shape of objects must be determined.
This 2-D image of a street scene has been annotated with bounding boxes. Source: UnderstandAI, a DSpace company.
The same street scene is shown here in a 3-D LiDAR sensor image that is annotated using 3-D annotation. Source: UnderstandAI, a DSpace company.
LiDAR is a valuable technology for several industries, from autonomous vehicles to surveying.
Below are seven interesting applications of LiDAR:
💡To be useful for computer vision, and more specifically, supervised machine learning, LiDAR data must be accurately labeled, which is a big job that can be difficult to scale.
The challenge for AI developers is transforming massive, raw data into structured datasets that can be used to train machine learning models. This process often involves extensive data acquisition, followed by hours of data labeling to prepare the information for algorithms that enable machines to interpret and understand the visual world through LiDAR mapping, 3D models, and other sensor-driven inputs.
CloudFactory has AI-driven solutions designed for efficient LiDAR data labeling, blending advanced automation with human expertise to streamline the annotation process for industries like autonomous vehicles, urban development, and geographical mapping.
The unique mix of technology and human insight speeds up product development and reduces costs. It also enhances model performance metrics such as mean Average Precision and Intersection over Union, making it critical for businesses focused on optimizing AI and machine learning initiatives.
Data labeling no longer needs to be a major bottleneck, consuming a significant portion of your AI project time and contributing to a high failure rate due to data quality issues.
CloudFactory addresses these challenges by efficiently sourcing and annotating data, applying effective techniques, and reducing time and errors associated with labeling, leading to accurate datasets and faster convergence on ground truth data.