Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed-Reference-Cited by-同舟云学术

Roadside collection of training data for cropland mapping is viable when environmental and management gradients are surveyed

Published:2019-08 Issue: Volume:80 Page:82-93
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Waldner François^ORCID,Bellemans Nicolas,Hochman Zvi^ORCID,Newby Terence^ORCID,de Abelleyra Diego,Verón Santiago R.,Bartalev Sergey,Lavreniuk Mykola,Kussul Nataliia^ORCID,Maire Guerric Le,Simoes Margareth^ORCID,Skakun Sergii^ORCID,Defourny Pierre

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference69 articles.

1. Metodologia para la estimacion de la superficie sembrada con cultivos extensivos (Version 5, 2018);Argentinian Ministry of Agriculture,2018

2. Multi resolution segmentation: an optimum approach for high quality multi scale image segmentation;Baatz,2000

3. The imbalanced training sample problem: under or over sampling?;Barandela,2004

4. Mapping of arable land in Russia using multi-year time series of modis data and the lagma classification technique;Bartalev;Remote Sens. Lett.,2016

5. Web-based earth observation data analysis system vega-geoglam in support of global agricultural monitoring research and developments;Bartalev;Geoinform. Res. Papers (in Russian),2017

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