Processing the massive LiDAR point cloud is a time consuming process due to the magnitude of the data involved and
the highly computational iterative nature of the algorithms. In particular, many current and future applications of LiDAR
require real- or near-real-time processing capabilities. Relevant examples include environmental studies, military
applications, tracking and monitoring of hazards. Recent advances in Graphics Processing Units (GPUs) open a new era
of General-Purpose Processing on Graphics Processing Units (GPGPU). In this paper, we seek to harness the computing
power available on contemporary Graphic Processing Units (GPUs), to accelerate the processing of massive LiDAR
point cloud. We propose a CUDA-based method capable of accelerating processing of massive LiDAR point cloud on
the CUDA-enabled GPU. Our experimental results showed that we are able to significantly reduce processing time of
constructing TIN from LiDAR point cloud with GPGPU based parallel processing implementation, in comparison with
the current state-of-the-art CPU-based algorithms.
KEYWORDS: Remote sensing, Data processing, Data integration, Data storage, Databases, Sensing systems, Sensors, Associative arrays, Standards development, Information fusion
With the rapid development of remote sensing technology, the available amount of remote sensing data collected by
various sensors is increasing at a tremendous rate during the last decade. The large volume and high complexity of
remote sensing data make the effective management and processing of mass remote sensing data a difficult technical
challenge. We design and implement a prototype system named RSDPS-G (Remote Sensing Data Processing Software
based on Grid), which using the Grid technologies to provide an "open platform" for handling computing resources,
data and processing services for mass remote sensing data processing. The remote sensing data management, especially
data discovery and data integration, is one of the most important components of the RSDPS-G, due to the large volume
and high heterogeneities of remote sensing data on Grid. However the current methods of remote sensing data
management is only at the syntactic metadata level, thus can't address the semantic heterogeneity and interoperability
challenges. In this paper, we present a framework for semantic discovery and integration of remote sensing data, which
solving semantic heterogeneity and interoperability issues in remote sensing data management on Grid.
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