With the development 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 and etc., high performance processing and analysis of tremendous data become the bottleneck we face. According to the functional differences between different data carrier 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 in which the features or characters are homogenous and then 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 active-computation. Deep-computation (from data to features) is to extract the basic units through certain methods, such as clustering, so deep-computation emphasizes particularly on computing amount. Active-computation (from features to knowledge) is based on the basic units obtained by deep-computation. Firstly the spatial relationships between the units are computed, and the decisions can be made effectively and efficiently with domain knowledge and domain models through web services, so deep-computation emphasizes particularly on intelligence of computation.
Consequently, this paper analyzes the computing pattern of the four types of spatial data mentioned above. What's more, a case study of information extraction and target recognition from remote sensing image based on features was done to illustrate and testify the ideas mentioned above. In the end, this paper summarizes the relative problems about spatial data computation and expects the direction of future researches.