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GDAL/OGR和地理空间数据I/O库

已有 824 次阅读 2020-10-12 20:38 |个人分类:科研进展|系统分类:论文交流

Qin, C-Z. and Zhu, L-J. (2020). GDAL/OGR and Geospatial Data IO Libraries.  The Geographic Information Science & Technology Body of Knowledge (4th Quarter 2020 Edition), John P. Wilson (Ed.). DOI:10.22224/gistbok/2020.4.1 (link is external)


这是一篇约稿,我和组里朱良君博士一起为UCGIS(University Consortium of Geographic Information Science;美国大学地理信息科学联盟)的GIS&T Body of Knowledge(“Body of Knowledge documents the domain of geographic information science and its associated technologies (GIS&T). By providing this content in a new digital format, UCGIS aims to continue supporting the GIS&T higher education community and its connections with the practitioners”)撰写的一个Entry,按要求大约是供大学或研究生课程1节课上的一个知识点内容。全文是网页版形式,刚上线,校稿还没更新。

PD-33 - GDAL/OGR and Geospatial Data IO Libraries

Abstract:  Manipulating (e.g., reading, writing, and processing) geospatial data, the first step in geospatial analysis tasks, is a complicated step, especially given the diverse types and formats of geospatial data combined with diverse spatial reference systems. Geospatial data Input/Output (IO) libraries help facilitate this step by handling some technical details of the IO process. GDAL/OGR is the most widely-used, broadly-supported, and constantly-updated free library among existing geospatial data IO libraries. GDAL/OGR provides a single raster abstract data model and a single vector abstract data model for processing and analyzing raster and vector geospatial data, respectively, and it supports most, if not all, commonly-used geospatial data formats. GDAL/OGR can also perform both cartographic projections on large scales and coordinate transformation for most of the spatial reference systems used in practice. This entry provides an overview of GDAL/OGR, including why we need such a geospatial data IO library and how it can be applied to various formats of geospatial data to support geospatial analysis tasks. Alternative geospatial data IO libraries are also introduced briefly. Future directions of development for GDAL/OGR and other geospatial data IO libraries in the age of big data and cloud computing are discussed as an epilogue to this entry.




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