Room: Maasai Mara
Friday, 15:30
Duration: 20 minutes (plus Q&A)
video on media.ccc.de
video on YouTube
In recent years, data creation methods have evolved, incorporating machine learning, AI, deep learning, and virtual reality to streamline processes. However, these advancements have not uniformly benefited communities in developing countries. Nonetheless, OMDTZ tirelessly seeks solutions to ensure more high-quality data are gathered, with low cost and extensive local involvement. One standout initiative involves a customized tricycle, known as bajaj, which is cost-effective, enabling access to streets of varying conditions. Equipped with an affordable street view camera, it collects images used to automate generation of vector data attributes to enrich OpenStreetMap. This session aims to share the experience and process, inspiring other communities to consider similar adaptations.
Over the years, OMDTZ and other volunteers have spearheaded a number of initiatives aimed at updating OpenStreetMap through various projects and mapathons in Tanzania. These efforts have added countless buildings, roads, and amenities, impacting social well-being and lifesaving initiatives. As technology has advanced, methods for data creation and updating have evolved, leveraging machine learning, AI, deep learning, and virtual reality to streamline processes. In line with these advancements, OMDTZ has invested in methods to ensure efficient and high-quality data capture. One such method involves using Mapillary to capture street view imagery, which is then utilized to automate the generation of vector data and attributes such as road surface, traffic signs, and drainage coverage.
Additionally, OMDTZ has customized Bajaj, equipping it with navigation and a GoPro Max camera for data collection. This approach offers cost-effectiveness compared to traditional vehicles, increased mobility in intricate urban areas, and fosters community involvement by leveraging local knowledge. Combining these technologies with local resources revolutionizes street-level data collection, contributing to the continuous improvement of OpenStreetMap in Dar es Salaam