Open Source Linux GIS Software
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GIS Software for Linux
View 10 business solutionsBrowse free open source GIS software and projects for Linux below. Use the toggles on the left to filter open source GIS software by OS, license, language, programming language, and project status.
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openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.
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GeoDMA
Geographic feature extraction and data mining
GeoDMA is a plugin for TerraView software, used for geographical data mining. With a single image, the user can perform segmentation, attributes extraction, normalization and classification. -
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A system that shall predict good days and locations for cross country free flying such as paragliding by comparing current weather predictions with statistics about past weather predictions and flights from online contests.
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Phenalysis
Analyze agronomic plant research plots in aerial orthomosaic images.
A graphical user interface to import, analyze and export plots from orthomosaic images of agronomic trials. Please cite the following reference in your work if you use Phenalysis: Khan Z and Miklavcic SJ (2019) An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images. Front. Plant Sci. 10:683. doi: https://doi.org/10.3389/fpls.2019.00683 This tool is being developed through the sponsorship of the Australian Research Council's Industrial Transformation Research Hub on Wheat in a Hot and Dry Climate. https://www.wheathub.com.au/ -
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SPAWNN
SPatial Analysis With self-organizing Neural Networks
The SPAWNN toolkit is an innovative toolkit for spatial analysis with self-organizing neural networks which is particularily useful for spatial analysis, visualization and geographical data mining. To run the toolkit, simply download and execute (double-click) the jar-file. Please cite: - Hagenauer, J., & Helbich, M. (2016). SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks. Transactions in GIS, 20(5), 755-775. Other related publications: - Hagenauer, J. (2016). Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. Journal of Geographical Systems, 18(1), 1-15. - Hagenauer, J., & Helbich, M. (2013). Contextual neural gas for spatial clustering and analysis. International Journal of Geographical Information Science, 27(2), 251-266.
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