2023
DOI: 10.1016/j.ecoinf.2022.101955
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Application of machine learning approaches for land cover monitoring in northern Cameroon

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Cited by 54 publications
(30 citation statements)
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“…The GLCLU data, which consists of 10 × 10-degree granules, is publicly available at . This published GLCLU dataset has been widely used in global land monitoring systems (Tubiello et al, 2023; Yuh et al, 2023; Zhu et al, 2022) and has been created using Landsat Analysis Ready Data (ARD) and machine-learning tools that were locally and regionally calibrated for higher classification accuracy (Potapov et al, 2022).…”
Section: Methodsmentioning
confidence: 99%
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“…The GLCLU data, which consists of 10 × 10-degree granules, is publicly available at . This published GLCLU dataset has been widely used in global land monitoring systems (Tubiello et al, 2023; Yuh et al, 2023; Zhu et al, 2022) and has been created using Landsat Analysis Ready Data (ARD) and machine-learning tools that were locally and regionally calibrated for higher classification accuracy (Potapov et al, 2022).…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, the dataset has been validated using independently collected reference data, ancillary data from the UN Food and Agricultural Organization, and other global land cover products from the NASA Global Ecosystems Dynamics Investigation (GEDI) service (Potapov et al, 2022; Yuh et al, 2023). Given its high accuracy (above 85%) and availability, we have used this dataset for our analysis.…”
Section: Methodsmentioning
confidence: 99%
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“…The accuracy of individual categories can also be evaluated in terms of "Producer Accuracy" (PA) and "User Accuracy" (UA) [62]. The PA defines the percentage accuracy of each class in a map, calculated by dividing the number of correct pixels in each class by the total number of pixels of that class from the reference data [63]. The UA defines how close the resulting classification map is to ground observations, calculated by dividing the number of correctly classified pixels in each category by the total number of pixels classified in that class [63].…”
Section: Accuracy Assessment and Comparisonmentioning
confidence: 99%
“…By utilizing these methods, one can obtain a high-quality LULC map with relatively less time and effort 6 . LULC classifications using ML algorithms have been a popular research topic in recent years, with many articles exploring this area [7][8][9] . Traditional ML algorithms usually work very well when the study area is small and the classes to be mapped are relatively few.…”
Section: Introductionmentioning
confidence: 99%