Christopher Beddow

Beyond LiDAR and automated sensors, human photography captures the "why" of a place. This talk draws inspiration from Panoramio and Flickr, questioning whether human-captured imagery remains a vital layer for modern mapping and exploring what this looks like today.

Overture Maps Foundation provides millions of points of interest (POIs) as open map data. This gives OpenStreetMap users a chance to improve the map using open source geospatial tools in Python, along with various open APIs, to compare and validate the data.

Collaborative mapping is a valuable approach to creating or updating map data at scale. Whether simply improving the general data quality in an area, reacting to a crisis, or focusing on a specific data class, using the Tasking Manager and Rapid make the process a breeze.

Mapillary is an open platform for street-level imagery and map data that began in 2013. Since then around 1.8 billion images have been contributed from around the world.

Mapillary is the platform that makes street-level images and map data available to scale and automate mapping. There are many tools available within Mapillary’s ecosystem, as well as many real world use cases where Mapillary can have an impact.

Mapping is time-consuming and requires a high volume of a workforce when it comes to keep maps up to date periodically. This brings the need of finding alternative approaches to keep maps up to date.

RapiD is the OpenStreetMap editor that enables users to access a wide variety of shared open data and a machine learning generated predictions that can bring new detail to the map.

RapiD is the OpenStreetMap editor that enables users to access a wide variety of shared open data and a machine learning generated predictions that can bring new detail to the map.

Christopher Beddow takes us on a journey of how to map a small town using a variety of tools. The goal was to test and prove how much OpenStreetMap can be enriched using RapiD buildings and roads, and Mapillary map features, traffic signs, and imagery.

How useful are map features automatically extracted from street-level images? Can they be trusted? These are some of the questions we tried to answer through community campaigns and student-led research in 2019.