Despite advances in mapping technologies and spatial data capabilities, global mapping inequalities are not declining. Inequalities in the coverage, quality, and currency of mapping persist, with significant gaps in remote and rural parts of the Global South. These regions, representing some of the most economically and resource-disadvantaged societies in the world, need high-quality mapping to aid in the delivery of essential services, such as health care, in response to severe challenges such as poverty, conflict, and global climate change. Volunteered geographic information (VGI) has shown potential as a solution to mapping inequalities. Contributions have largely been made in urban areas or in response to acute emergencies (e.g., earthquakes or floods), however, leaving rural regions that suffer from chronic humanitarian crises undermapped. An alternative solution is needed that harnesses the power of volunteer mapping more effectively to address regions in most need. Machine learning holds promise. In this article we propose centaur VGI, a hybrid system that combines the spatial cognitive abilities of human volunteers with the speed and efficiency of a machine. We argue that centaur VGI can contribute to mitigating some of the political and technological factors that produce inequalities in VGI mapping coverage and do so in the context of a case study in Acholi, northern Uganda, an inadequately mapped region in which the authors have been working since 2017 to provide outreach health care services to victims of major limb loss during conflict.

尽管制图技术和空间数据功能取得了进步，但全局制图不平等并没有减少。测绘的覆盖范围，质量和币种仍然存在不平等现象，全球南部偏远和农村地区存在巨大差距。这些地区代表着世界上一些经济和资源最弱势的社会，需要高质量的地图绘制，以帮助提供基本服务，例如医疗保健，以应对贫困，冲突和全球化等严峻挑战。气候变化。自愿性地理信息（VGI）已显示出解决映射不平等问题的潜力。在很大程度上，城市或为应对紧急事件（例如地震或洪水）做出了贡献，然而，遭受长期人道主义危机之苦的农村地区却未得到应有的重视。需要一种替代解决方案，该解决方案可以更有效地利用志愿者映射的力量来解决最需要的区域。机器学习有希望。在本文中，我们提出了半人马座VGI，这是一种混合系统，将人类志愿者的空间认知能力与机器的速度和效率相结合。我们认为，半人马座VGI可以有助于减轻某些政治和技术因素，这些因素会在VGI测绘覆盖率方面产生不平等，而这是在乌干达北部Acholi的案例研究的背景下进行的。自2017年以来，为冲突期间严重肢体丧失的受害者提供外展医疗服务。机器学习有希望。在本文中，我们提出了半人马座VGI，这是一种混合系统，将人类志愿者的空间认知能力与机器的速度和效率相结合。我们认为，半人马座VGI可以有助于减轻某些政治和技术因素，这些因素会在VGI测绘覆盖率方面产生不平等，并且在乌干达北部Acholi的案例研究中确实如此，这是作者一直在工作的区域自2017年以来，为冲突期间严重肢体丧失的受害者提供外展医疗服务。机器学习有希望。在本文中，我们提出了半人马座VGI，这是一种混合系统，将人类志愿者的空间认知能力与机器的速度和效率相结合。我们认为，半人马座VGI可以有助于减轻某些政治和技术因素，这些因素会在VGI测绘覆盖率方面产生不平等，并且在乌干达北部Acholi的案例研究中确实如此，这是作者一直在工作的区域自2017年以来，为冲突期间严重肢体丧失的受害者提供外展医疗服务。