  Land cover classification plays a critical role in describing Earth's surface
characteristics. However, these classifications can be affected by
uncertainties introduced by variability in investigator interpretations. While
land cover classification mapping is becoming easier due to the emergence of
cloud geospatial platforms, such uncertainty is often overlooked. This study
aimed to create a robust land cover classification map by reducing
inter-investigator variability from independent investigators' maps using a
Bayesian inference framework. In Saitama City, Japan, as a case study, 44
investigators used a point-based visual interpretation method via Google Earth
Engine to collect stratified reference samples across four different land cover
classes: Forest, Agriculture, Urban, and Water. These samples were then used to
train a random forest classifier, resulting in the creation of individual
classification maps. We quantified pixel-level discrepancies in these maps
arising from inherent inter-investigator variability. To address them, we
developed a Bayesian inference framework to produce a unified classification
map. This framework updates the classification probability based on a Dirichlet
distribution and yielded an overall accuracy of 0.851 for independent
validation samples, an improvement over the average accuracy of 0.728 for the
individual maps. We further improved the results by introducing K-Medoids to
group the most reliable maps as the input for Bayesian inference, achieving an
overall accuracy of 0.858, the highest among all approaches tested. Our
approach also effectively reduced salt-and-pepper noise, which is often found
in individual classification maps. This research underscores the intrinsic
uncertainties present in land cover classification maps attributable to
investigator variations and introduces a potential solution to mitigate these
variations.
