  This paper explores the concept of leveraging generative AI as a mapping
assistant for enhancing the efficiency of collaborative mapping. We present
results of an experiment that combines multiple sources of volunteered
geographic information (VGI) and large language models (LLMs). Three analysts
described the content of crowdsourced Mapillary street-level photographs taken
along roads in a small test area in Miami, Florida. GPT-3.5-turbo was
instructed to suggest the most appropriate tagging for each road in
OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a
state-of-the-art multimodal pre-training method as an artificial analyst of
street-level photographs in addition to human analysts. Results demonstrate two
ways to effectively increase the accuracy of mapping suggestions without
modifying the underlying AI models: by (1) providing a more detailed
description of source photographs, and (2) combining prompt engineering with
additional context (e.g. location and objects detected along a road). The first
approach increases the suggestion accuracy by up to 29%, and the second one by
up to 20%.
