Visible (RGB): Captures data in the visible wavelength range,including red (620-750 nm), green (495-570 nm), and blue (450-495nm).

Near-Infrared (NIR): Captures data in the near-infrared wavelengthrange (around 750-950 nm).

These bands can be combined to provide you with:
• 4-Band combined NIR and RGB (RGBN) (4-Band CIR)
• 3-Band combinedNIR and RGB (NRG) (3-Band CIR)
• NDVI (NormalizedDifference Vegetation Index)

The addition of the near-infrared band is particularly useful forapplications such as:
• Vegetation analysis: NIR data helps in assessing plant health and vegetation indices.
• Remote sensing: 4-band imagery aids in land cover, classification, crop monitoring,and environmental studies.
• Surveillance: NIR data can enhance visibility in low-light conditions and helpdetect camouflaged objects.

Gimbal Stabilization: The camera is mounted on a highly stable, multi-axis gimbal system that compensates for the motion of the aerial platform. This ensures steady, blur-free imagery even in challenging conditions.

Multi-Sensor Payload: ISR gimbal systems often incorporate multiple imaging sensors to capture different types of data:
• Electro-Optical (EO) Sensor: High-resolution daylight camera for detailed color imagery.
• Infrared (IR) Sensor: Thermal imaging camera for night vision and heat signature detection.
• Laser Range Finder (LRF): Measures the distance to a target for precise geolocation.

Zoom Capabilities: Powerful optical and/or digital zoom, allowing operators to acquire detailed imagery from long distances.

Video Tracking: Target tracking capabilities, enabling the system to lock onto and follow moving targets automatically.

Geo-Referencing: Geo-tagged imagery with GPS coordinates, allowing for precise location identification and mapping.

Rugged Design: Built to withstand harsh environmental conditions, including extreme temperatures, vibrations, and moisture.

LiDAR Sensor: The system incorporates a high-precision LiDAR sensor that emits rapid pulses of laser light to measure distances to objects and surfaces. The sensor records the time it takes for each pulse to return, allowing it to generate a dense point cloud of the scanned area.

GPS/INS Integration: The system integrates high-accuracy GPS (Global Positioning System) and INS (Inertial Navigation System) to precisely determine the position and orientation of the LiDAR sensor during data acquisition.

High-Resolution Cameras: In addition to the LiDAR sensor, up to 6 high-resolution RGB or multispectral cameras are integrated to capture detailed aerial imagery. This imagery can be used to colorize the LiDAR point cloud and provide additional visual context.

Data Processing: The raw LiDAR and imaging data undergo extensive post-processing to create the final 3D asset map. This involves point cloud filtering, classification, and georeferencing, as well as the generation of digital elevation models (DEMs), digital surface models (DSMs), and 3D meshes.

Applications: 3D asset mapping aerial systems with LiDAR are used in various industries, including:

• Utilities: Mapping and inspection of power lines, pipelines, and other infrastructure assets.

• Transportation: Highway and railway corridor mapping for planning, design, and maintenance.

• Urban Planning: Detailed 3D mapping of cities for urban development, land use analysis, and smart city applications.

• Mining: Volumetric measurements, site planning, and monitoring of mining operations.

• Forestry: Forest inventory, canopy height modeling, and biomass estimation.