Most commercial mapping services tend to use either data generated by them or data provided by external entities, usually at a price. Efforts that try to keep this information freely available are likely to depend on the help of volunteers, for instance the OpenStreetMap project. To support them, this document presents a system that reduces the work of mapping streets manually. It is a proof of concept of a street mapping device that captures video, keeps useful images, and identifies traffic signs. Due to the limited storage capacity, the device analyzes the images and discards the ones without relevant differences, for this it uses the Structural Similarity index to decide the images that will be stored. Then, it uses an AlexNext-based neural network to identify traffic signs and provide extra useful information about the streets, which later is added to the metadata of the images. This proof of concept illustrates a modular system flow to automate the street mapping task, which can later be extended and improved for more robust and accurate results. This system can be applied to generate more mapping information in cities, such as the Guadalajara Metropolitan area.