Reliable land cover classification using AI and Machine Learning faces significant challenges from limited sampling and human-introduced biases. Even with accessible platforms like Google Earth Engine, inter-investigator variability and 'salt-and-pepper noise' persist, especially with multiple human contributors. This study tackles these issues by synthesizing machine learning with a classical Bayesian inference framework. We integrated unsupervised clustering of investigator maps with a Bayesian framework using Dirichlet distributions. In Saitama City, Japan, 44 investigators collected reference samples from Landsat-8 imagery. We trained Random Forests, Support Vector Machines (SVM), and Neural Networks, enhancing them with k-Means/k-Medoids clustering to group reliable maps. The Bayesian framework then refined class assignments through sequential probability updates. Bayesian inference on SVM maps achieved the highest overall accuracy of 0.857, improving upon non-Bayesian SVMs. We found better investigator labeling correlated with higher accuracy. Furthermore, SVM-based fused maps, refined by k-Means, significantly reduced salt-and-pepper noise. This research demonstrates an effective synthesis of machine learning and Bayesian inference, offering a powerful strategy to combine classifications while reducing variation and noise.