In collaboration with DFiD and the Department of Roads (DoR) Zanzibar, N/LAB is investigating methods combining remote sensing (drone/satellite imagery) and applied machine learning methods to automate the assessment of low volume road conditions. Traditionally such road quality surveys are conducted by driving a vehicle equipped with specialist equipment along all roads leading to non-trivial costs. High resolution imagery from drones and potentially satellites, however, provides an opportunity to automate this. The proof-of-concept project, already in phase 2, aims to do this via the the application of advanced machine learning learnt and evaluated on ground-truths collected over all roads managed by the DoR and drone imagery. Forming the first phase of the project, ground-truth data has been collected by the N/LAB team in conjunction with the DoR and includes (1) human in-car evaluations of road segment quality, (2) official measurements of road segment quality via a bump integrator, (3) measurements of road segment quality via the RoadLabPro mobile phone application recently commissioned by the World Bank, (3) video imagery of the roads and (4) raw mobile sensor measurements. Additionally as part of phase 1 drone imagery has been acquired. Phase 2 is currently ongoing.