Land cover classification plays a pivotal role in describing Earth's surface characteristics. However, these thematic classifications can be affected by uncertainties introduced by an investigator's bias. While land cover classification mapping is becoming easier for us due to the emergence of cloud geospatial platforms such as Google Earth Engine, such uncertainty is often overlooked. Thus, this study aimed to create a robust land cover classification map by reducing investigator-induced uncertainty from independent investigators' maps using a multiple classifier system. 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, ultimately resulting in the creation of individual classification maps. We quantified pixel-level discrepancies in these maps, which came from inherent investigator-induced variability. To tackle these uncertainties, we developed a multiple classifier system incorporating K-Medoids to group the most reliable maps and minimize discrepancies. We further applied Bayesian analysis to these grouped maps to produce a unified, accurate classification map. This yielded an overall accuracy of 92.5\% for 400 independent validation samples. We discuss how our approach can also reduce 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 attenuate these variations.

翻译：土地覆盖分类在描述地球表面特征中发挥着关键作用。然而，这些专题分类可能因研究者引入的偏差而产生不确定性。尽管诸如Google Earth Engine等云地理空间平台的出现使土地覆盖分类制图变得更加容易，但此类不确定性常被忽视。为此，本研究旨在通过多分类器系统降低独立研究者地图中由研究者引入的不确定性，从而生成稳健的土地覆盖分类图。以日本埼玉市为案例，44位研究者采用基于Google Earth Engine的点状目视解译方法，在森林、农业、城市和水体四类土地覆盖类别中收集分层参考样本。随后利用这些样本训练随机森林分类器，最终生成各自的分类地图。我们量化了这些地图中因研究者固有变异性导致的像素级差异。为解决这些不确定性，我们开发了基于K-Medoids的多分类器系统，通过聚类最可靠的地图以最小化差异。进一步对聚类后的地图应用贝叶斯分析，生成统一的精确分类图。该方法对400个独立验证样本的分类总体精度达92.5%。我们探讨了该方法如何减少单个分类地图中常见的椒盐噪声。本研究揭示了土地覆盖分类地图中因研究者差异而存在的固有不确定性，并提出了一种缓解该差异的潜在解决方案。