The STEM project, funded by the Autonomous Province of Trento (Italy) aims at implementing algorithms and tools for the efficient processing of remotely sensed data for forestry and agricultural applications. All the implemented tools are integrated in the open source Q-GIS software using a dedicated plug-in developed in the project. External open source libraries such as GRASS and R, as well as in-house algorithms, are integrated in the plug-in in order to fulfil all the required functionalities of the systems. All the algorithms were developed to use already available data as input and to prevent the additional costs of further acquisitions. Supervised classification algorithms and well-tested procedures were chosen in order to assure the maximum reliability and stability of the provided results in every operative condition. The final goal is the development of a turn- key systems for public administration technicians allowing easy and transparent classification of wide areas and their simple query to define the land use and in particular the orchard and plantation typologies

Dalponte, M.; Nex, F.; Delucchi, L.; Neteler, M.G.; Ramondino, F.; Pedron, L.; Gianelle, D. (2014). The STEM project: an open source solution for agricoltural monitoring. In: International Symposium on crop growth monitoring (ISCGM), September 13-16, 2014, Nanjing, China. url: http://iscgm2014.netcia.org.cn/iscgm/Index.jspx handle: http://hdl.handle.net/10449/24548

The STEM project: an open source solution for agricoltural monitoring

Dalponte, Michele;Delucchi, Luca;Neteler, Markus Georg;Gianelle, Damiano
2014-01-01

Abstract

The STEM project, funded by the Autonomous Province of Trento (Italy) aims at implementing algorithms and tools for the efficient processing of remotely sensed data for forestry and agricultural applications. All the implemented tools are integrated in the open source Q-GIS software using a dedicated plug-in developed in the project. External open source libraries such as GRASS and R, as well as in-house algorithms, are integrated in the plug-in in order to fulfil all the required functionalities of the systems. All the algorithms were developed to use already available data as input and to prevent the additional costs of further acquisitions. Supervised classification algorithms and well-tested procedures were chosen in order to assure the maximum reliability and stability of the provided results in every operative condition. The final goal is the development of a turn- key systems for public administration technicians allowing easy and transparent classification of wide areas and their simple query to define the land use and in particular the orchard and plantation typologies
Open Source
Forest
Remote sensing
2014
Dalponte, M.; Nex, F.; Delucchi, L.; Neteler, M.G.; Ramondino, F.; Pedron, L.; Gianelle, D. (2014). The STEM project: an open source solution for agricoltural monitoring. In: International Symposium on crop growth monitoring (ISCGM), September 13-16, 2014, Nanjing, China. url: http://iscgm2014.netcia.org.cn/iscgm/Index.jspx handle: http://hdl.handle.net/10449/24548
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/24548
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