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Airborne LiDAR for DEM generation: some critical issues

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Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the high-density characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.
Title: Airborne LiDAR for DEM generation: some critical issues
Description:
Airborne LiDAR is one of the most effective and reliable means of terrain data collection.
Using LiDAR data for digital elevation model (DEM) generation is becoming a standard practice in spatial related areas.
However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges.
This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction.
Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data.
Commonly used and most recently developed LiDAR filtering methods are presented.
Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail.
In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation.
Feature specific elements such as breaklines contribute significantly to DEM quality.
Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed.
Given the high-density characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data.
Extraction of breaklines and integration of the breaklines into DEM generation are presented.

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