Geometry based airborne LIDAR data compression

Airborne LIDAR data often consumes hundreds of gigabytes. Existing LIDAR data compression schemes can compress the file to 5%-23% of the original size. Even after compression, the compressed data size is still in the order of gigabyte, which makes it impractical for many applications. This paper proposes a novel geometry based compression scheme. It first introduces a LIDAR classification method that accurately classifies airborne LIDAR data into tree and non-tree points; different geometry based compression schemes are then applied for different types of data. The proposed method can not only compress LIDAR data significantly, but also extract useful semantic information from the data. Experimental results show that the new approach achieves very high compression ratio, making applications that were not practical before feasible.