中文摘要：

随着传感器和其他数据采集技术的不断进步以及对地观测网络的建设和启动．空间数据的高性能处理和分析成为摆在地学工作者面前的瓶颈。本文以此为出发点。按照不同地学领域（陆地、大气、海洋）的空间数据载体的形态的不同．将空间数据划分为反映固态基质信息的陆地空间数据。反映液态基质信息的陆地水文空间数据，反映液态基质信息的海洋流体空间数据和反映气态基质信息的大气流体空间数据四类．并对每类数据的最小单元问题进行了初步的分析。本文详细阐述了地学空间计算的涵义．并根据计算行为模式及计算的侧重点的不同．将地学计算过程分为深度计算过程与主动计算过程（即“数据→特征→知识”的一般计算过程）．并就此进行了阐释。以基于特征的遥感信息提取和目标识别工作为例．对上述理论进行了说明和验证。最后对空间数据计算模式相关问题进行了总结．并对以后的研究做了展望。

英文摘要：

With the developments in satellite sensor technology, data acquisition technology developed rapidly; and with the start of a series of space-based observation network for Earth science, such as EOS, GTOS, ECOS, GOOS etc., high performance processing and analysis of tremendous data becomes the bottleneck faced by us. According to the shape differences between different data carriers of terrene, ocean and atmosphere, this paper divides spatial data into four classes ：terrestrial-solid based spatial data, terrestrial-liquid based spatial data, marine-floating based spatial data and atmospheric-floating based spatial data. Then this paper introduces the concept of the basic unit and proposes their actually existing style in the four types of spatial data mentioned above. Furthermore, this paper simply reviews geocomputation and expands it to geo-spatial computation. Then this paper discusses the connotation and classification of geo-spatial computation and summarizes the general computing procedure： data → features →knowledge. According to the differences of the computational behavior and the computing emphasis, this paper divides geo-spatial computation into two classes： deep-computation and proactive-computation. Consequently, this paper explains the computing pattern of deep-computation and proactire-computation. What＇s more, a case study of information extraction and target recognition from remote sensing image based features was done to illustrate and testify the ideas mentioned above. Finally, this paper summarizes the relative problems about spatial data computation and expects the direction of future researches.