The Trouble with LiDAR: Solving the Data Density Problem
Modern sensors are providing increasingly detailed map imagery and elevation data, but the size of these datasets can easily overwhelm applications that wish to use them. Consider this comparison of common elevation formats:
|Data Type||Resolution||Data Density|
DTED Level 2
|resolution: 15cm||150-900MB/km2 (uncompressed)|
Automatically processing and caching the LiDAR data to a format suited for fast retrieval and display can allow real-time applications to leverage this powerful data source. Combined with high-resolution orthophotography, LiDAR elevation data can provide exceptional detail for situational awareness, and enable accurate spatial analysis such as line of sight or area inter-visibility determination.
Managing such incredible amounts of data can be a challenge, requiring an intelligent caching mechanism to enable an array of uses. Ensuring the cache is system-independent allows it to be shared between multiple applications, while persisting the cache allows it to be prepopulated allowing for faster future use.
Allowing the same LiDAR data to be interpreted in different ways, such as including or excluding various object classifications such as foliage, enables its use for multiple forms of analysis in addition to visualization.
With the ease at which new LiDAR data can be generated relative to traditional survey techniques, it is important to consider how to incorporate new or updated data. Using mechanisms to match the cached data to its original source, segments of data can be updated on the fly requiring minimal work to refresh the cache.