Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches

Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches

In contemporary agriculture and environmental management, the need for precise and accurate crop maps has never been more vital. Although object-based (OB) methods within Google Earth Engine (GEE) improve accuracy and output quality in contrast to pixel-based approaches, their application to crop cl...

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Bibliographic Details
Main Authors: Marco Vizzari, Giacomo Lesti, Siham Acharki
Format: Article
Language:English
Published: Taylor & Francis Group 2025-05-01
Series:Geo-spatial Information Science
Subjects:
Machine learning
random forest
IACS
CAP
winter cereals
summer crops
Online Access:https://www.tandfonline.com/doi/10.1080/10095020.2024.2341748
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https://www.tandfonline.com/doi/10.1080/10095020.2024.2341748

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