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BAPTISTE TRIPARD Alteia, Project Management & Marketing The need for reliable spatial data on road infrastructure quantity and quality in World Bank’s and governments' work in developing countries is high and growing: for prioritizing road investments in lending operations and build a more resilient network ; to monitor broad progress in infrastructure development and benchmark countries against peers; and to conduct statistical analyses on the impact of infrastructure on socio-economic outcomes. The available data, however, tends to be scarce, partial, or outdated. While in theory a lot can now be achieved using earth observation techniques, in practice the gap has still remained large between what is feasible at a small scale using high resolution data and actual applications deployed at scale - mainly because of the data input cost. The objective of our project is precisely to bridge this gap and illustrate how freely accessible satellite data coming from public-funded initiatives like Sentinel, open source data like OpenStreetMap or crowd sourced initiatives like Mapillary can be combined and analyzed at scale – and at a reasonable cost – to generate reliable metrics on road infrastructure quality at the relevant level for decision making. The approach leverages massive amounts of satellite data accessible through AWS, open source data, AWS infrastructure and deep learning models to build accurate road networks at scale, enriched with key attributes such as pavement and roughness indexes. Copyright: www.intergeo.de More about INTERGEO BERLIN, 10 - 12 OCTOBER 2023: www.intergeo.de