Open AccessNansat: a Scientist-Orientated Python Package for Geospatial Data Processing
Author(s) -
Anton Korosov,
Morten W. Hansen,
KnutFrode Dagestad,
Akira Yamakawa,
Aleksander Vines,
Maik Riechert
Publication year - 2016
Publication title -
journal of open research software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.385
H-Index - 6
ISSN - 2049-9647
DOI - 10.5334/jors.120
Subject(s) - python (programming language) , geospatial analysis , computer science , metadata , workflow , toolbox , directory , geospatial pdf , e science , geospatial metadata , database , metadata repository , information retrieval , meta data services , data mining , data science , world wide web , grid , programming language , remote sensing , geometry , mathematics , geology , operating system
Nansat is a Python toolbox for analysing and processing 2-dimensional geospatial data, such as satellite imagery, output from numerical models, and gridded in-situ data. It is created with strong focus on facilitating research, and development of algorithms and autonomous processing systems. Nansat extends the widely used Geospatial Abstraction Data Library (GDAL) by adding scientific meaning to the datasets through metadata, and by adding common functionality for data analysis and handling (e.g., exporting to various data formats). Nansat uses metadata vocabularies that follow international metadata standards, in particular the Climate and Forecast (CF) conventions, and the NASA Directory Interchange Format (DIF) and Global Change Master Directory (GCMD) keywords. Functionality that is commonly needed in scientific work, such as seamless access to local or remote geospatial data in various file formats, collocation of datasets from different sources and geometries, and visualization, is also built into Nansat. The paper presents Nansat workflows, its functional structure, and examples of typical applications