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Table 2 Examples of application of classification methods towards SDGs using EO data

From: Monitoring sustainable development by means of earth observation data and machine learning: a review

SDGs Field Main finding References
SDG 2 (Zero Hunger) Agriculture Multi-temporal crop classification reduces the unfavourable effects of using single-date acquisition [81]
The proposed method performed similar to SVM and RF in the classification of crops with similar phenology [57]
Developed an efficient framework for multi-temporal crops classification [82]
SDG 6 (Clean Water and Sanitation) Wetland The developed framework for coastal plain wetlands classification had high accuracy. [83]
SDG 8 (Decent Work and Economic Growth) Slavery The approach was used to help to liberate slaves by mapping brick kilns. [58]
SDG 11 (Sustainable Cities and Communities) Land use The approach based on CNN achieved an accuracy of  98% for land use and land cover analysis [84]
The proposed approach confirmed its suitability for urban planning because it had a superior performance compared to the global one [56]
Living conditions Deep learning demonstrated a high potential to map areas of deprived living conditions [85]
Land cover The multivariate time series algorithm showed high accuracy for rare land cover classes [86]
SDG 13 (Climate Action) Climate The model based on decision trees, and used to classify local climate zones, achieved a good performance [75]
SDG 14 (Life Below Water) Marine habitat SVM and K-NN classifiers achieved an accuracy higher than 90% on mapping coastal marine habitat [50]
SDG 15 (Life on Land) Land cover The approach used allowed to differentiate the hyperspectral subclasses from the classes [87]
Forest Sentinel-2 is considered a powerful source of data for forest monitoring and mapping [52]
RF was the best method to predict and map the area and volume of eucalyptus [88]