Despite advances in mapping technologies and spatial-data capabilities, global mapping inequalities are not reducing. 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 economic and resource disadvantaged societies in the world, need high-quality mapping to aid in the delivery of essential services, such as healthcare, in response to severe challenges such as poverty, conflict, and global climate change. Volunteered geographical information (VGI) has shown potential as a solution to mapping inequalities. However, contributions have largely been made in urban areas or in response to acute emergencies (such as earthquakes or floods), leaving rural regions that suffer from chronic humanitarian crises under-mapped. 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 paper 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 healthcare services to victims of major limb loss during conflict.