Li-Jun Zhan
Chinese Academy of Sciences
5 Papers
6 Citations
Li-Jun Zhan is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Raster graphics & Raster data. The author has an hindex of 3, co-authored 5 publications.
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Papers
A strategy for raster-based geocomputation under different parallel computing platforms
TL;DR: By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputers compatible with three popular parallel computing platforms, and practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of this strategy.
Which type of slope gradient should be used to determine flow-partition proportion in multiple-flow-direction algorithms - tangent or sine?
TL;DR: In this article, the authors revisited the tangent expression for the slope gradient (tan β ) used in the general flow-partition function in multiple-flow-direction (MFD) algorithms.
A graph-theory-based method for parallelizing the multiple-flow-direction algorithm on CUDA compatible graphics processing units
Li-Jun Zhan,Cheng-Zhi Qin +1 more
- 01 Aug 2011
TL;DR: A graph-theory-based parallel implementation on the NVIDIA GPU of a widely-used MFD algorithm (FD8) by using the parallelization strategy of the existing CUDA- based parallel SFD algorithm, and performs much faster than the traditional serial FD8 algorithm.
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How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?
TL;DR: Experimental results show that parallel raster I/O using GDAL under column‐wise or block‐wise domain decomposition is highly inefficient and cannot achieve correct output, although GDAL performs well under row‐wisedomain decomposition.
Parallelizing flow-accumulation calculations on graphics processing units-From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm
Cheng-Zhi Qin,Li-Jun Zhan +1 more
TL;DR: The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based algorithms based on existing parallelization strategies.