Volunteered geographic information (VGI) is often cited as a potential solution to persistent global inequalities in map data, particularly in areas undergoing humanitarian crises. Poor volunteer engagement, slow data production, and low-quality outputs have limited progress, however, and can unintentionally exaggerate inequalities. Hybrid machine learning–VGI (ML–VGI) frameworks can help to overcome these challenges through a combination of workflow automation and purposive human input, but the use of these workflows is rare in practice. Here, we implement an ML–VGI framework (Centaur VGI) and undertake a detailed comparative usability assessment against an existing, widely used VGI mapping platform to demonstrate its potential to improve volunteer engagement, mapping speed, and data quality. Our results suggest that through automated building, searching, and labeling, the Centaur VGI platform provides greater usability, quicker data production, and improved data quality for most users. Consequently, we provide the first evidence that hybrid ML–VGI approaches can be used to facilitate increased public participation in humanitarian building mapping efforts and thus help reduce global inequalities in map data.

自愿提供的地理信息 (VGI) 通常被认为是解决全球地图数据持续不平等的潜在解决方案，尤其是在发生人道主义危机的地区。然而，志愿者参与度低、数据生成缓慢和产出质量低下会限制进展，并可能无意中加剧不平等。混合机器学习-VGI (ML-VGI) 框架可以通过结合工作流自动化和有目的的人工输入来帮助克服这些挑战，但在实践中很少使用这些工作流。在这里，我们实施了一个 ML-VGI 框架 (Centaur VGI)，并针对现有的、广泛使用的 VGI 制图平台进行了详细的可用性比较评估，以证明其在提高志愿者参与度、制图速度和数据质量方面的潜力。我们的结果表明，通过自动化构建，通过搜索和标记，Centaur VGI 平台为大多数用户提供了更高的可用性、更快的数据生成和更高的数据质量。因此，我们提供了第一个证据，表明混合 ML-VGI 方法可用于促进公众更多地参与人道主义建筑制图工作，从而有助于减少地图数据中的全球不平等。