MSMT_IS30 had an overall accuracy of 95.1% and kappa coefficient of 0.898 as against 85.6% and 0.695 for NUACI, 89.6% and 0.780 for FROM_GLC, 90.3% and 0.794 for GHSL, 88.4% and 0.753 for GlobeLand30, and 88.0% and 0.745 for HBASE.
Using the LUCAS (Land Use/Cover Area frame Survey) reference data, the producer’s accuracies of MSMT_IS30, FROM_GLC, and GlobeLand30 were ,, and , respectively; the corresponding user’s accuracies were ,, and , respectively. Validation dataset link: https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid
The global overall accuracy, producer’s accuracy, and user’s accuracy for NUACI are 0.81–0.84, 0.50–0.60, and 0.49–0.61, respectively. The values of the kappa coefficient for NUACI and GlobeLand30 are 0.43–0.50 and 0.25-0.49 at the global level, respectively. Validation dataset link: http://www.geosimulation.cn/GlobalUrbanLand.html
GHSL and GlobeLand30 had an overall accuracy of 70.64% and 67.86%, respectively, the kappa coefficients were 0.4603 and 0.4051, the producer’s accuracies were 45.98% and 40.03%, and the user’s accuracies were 84.26% and 83.55%, respectively.
GHSL had an overall accuracy of 84.18%, -measure of 0.71, user’s accuracy of 84.06%, and producer’s accuracy of 68.77% for 45 cities across the globe. In addition, GHSL had an overall accuracy of 78.92% and 85.86%, a user’s accuracy of 85.46% and 81.51%, and a producer’s accuracy of 82.77% and 56.78% for urban and rural areas, respectively.
For the Asian HBASE classification model, according to the result of the cross-validation, the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient were 91.5%, 90.3%, 97.9%, and 0.90, respectively.