  Maintaining accurate, up-to-date maps is important in any dynamic urban
landscape, supporting various aspects of modern society, such as urban
planning, navigation, and emergency response. However, traditional (i.e.
largely manual) map production and crowdsourced mapping methods still struggle
to keep pace with rapid changes in the built environment. Such manual mapping
workflows are time-consuming and prone to human errors, leading to early
obsolescence and/or the need for extensive auditing. The current map updating
process in OpenStreetMap provides an example of this limitation, relying on
numerous manual steps in its online map updating workflow. To address this,
there is a need to explore automating the entire end-to-end map up-dating
process. Tech giants such as Google and Microsoft have already started
investigating Machine Learning (ML) techniques to tackle this contemporary
mapping problem. This paper offers an analysis of these ML approaches, focusing
on their application to updating Open-StreetMap in particular. By analysing the
current state-of-the-art in this field, this study identi-fies some key
research gaps and introduces DeepMapper as a practical solution for advancing
the automatic online map updating process in the future.
