A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

CCD联合平差方法及其初步实例检验

在常规照相天体测量工作中 ,对暗天体的定位是采用逐级定标星过渡的方法实现的。由于星表系统差和局部差的存在 ,最终定位结果可能包含难以把握的误差积累。在CCD小视场观测情况下时常难以找到足够数量的定标星。鉴于此 ,推导了CCD观测联合平差方法的严格矢量表达式 ,以期通过共同星的过渡 ,实现大天区统一平差。原理上此方法可实现相对于河外天体的定位 ,以削弱星表系统差和局部差对定位结果的影响。讨论了CCD联合平差方法实现中的问题 ,包括底片常数模型选择、切点位置改正、共同星的较差改正和法方程解算方法等 ,并分析了具体的处理措施。用实例初步检验了CCD联合平差方法的效果 ,表明通过对多张CCD观测的联合平差 ,可以削弱单张CCD观测参考星数目过少、单张CCD观测参考星分布不均、个别参考星存在位置偏差和局部区域参考星存在系统性位置偏差等不利因素对归算结果的影响 ,进而达到扩大视场和提高归算精度的目的