Spatiotemporally improving the rural access index - a remote sensing based approach

Abstract

Many countries, especially in the global south still lack the ability to effectively pursue basic policies, which can lead, in the worst case, to armed conflicts. Access to markets is a key factor for economic growth and an important component in reducing poverty. The SDG 9.1.1 addresses the proportion of the rural population who live within 2km of an all-season road, which can be mapped by the Rural Access Index (RAI), introduced by the World Bank in 2006. This requires the road network of so-called all season roads, population distribution and rural areas. We developed a fully automated approach, using remote sensing and other open source data to calculate the RAI on an annual basis between 2013 and 2020 for the Lake Chad region. We achieved an overall accuracy between 97.0% and 97.5% in detecting all-season roads using a Random Forest classification. Our method shows similar results to those published by the World Bank. However, our approach provides a higher spatial and temporal resolution measuring the RAI compared to previous studies and is independent of field studies.