Land use classification is a critical information to monitor a territory, support its development and plan its evolution. Aerial imagery is generally used as the base media to derive a land use classification by manual digitisation. However, this task has proved to be very time consuming given the generally large extent of the area to be classified. We investigated how state-of-the-art deep learning methods can be used to automate land use classification from aerial imagery. The work focused on the city of Dar-Es-Salaam where both aerial imagery and a land use classification ground truth were available. [Research Article]
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