Multiple Classifiers in Google Earth Engine for Land Use/Cover Classification

Authors: Yuhao Wang*, 0002
Topics: Land Use and Land Cover Change, Remote Sensing, Geographic Information Science and Systems
Keywords: Google Earth Engine,land use land cover
Session Type: Paper
Day: 4/6/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Human activities are the major force of driven land use/land cover (LULC) in the past decades; it has a significant impact on the environment systems on the planet. Thus, analyses the LULC is an essential step before we make any further research. Different classification algorithms have been developed to classify the land; while each of them may have pretty good accurate result, but inevitably has disadvantages that none could produce a perfect classification result to fit all LULC categories (Chen et al., 2017). Therefore, a multiple classifier system is brought to combine multiple land classifiers’ results to make a better land classification. In this study a method is proposed, based on the GEE platform to build a multiple classifier system with Adaptive Boosting(AdaBoost) to improve the land classification accuracy (Chen et al., 2017).

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