Christopher Beddow
Christopher Beddow is map analyst focusing on Mapillary and OpenStreetMap at Meta Reality Labs. He has worked on the OSM, GIS, and map data products at Mapillary since 2016. He codes in Python, SQL, and JavaScript, and enjoys traveling widely while editing OpenStreetMap. Chris lives in Switzerland and enjoys meeting people from every corner of the global FOSS4G and mapping community.
Sessions
The MapWithAI RapiD editor for OpenStreetMap offers a variety of open data to improve OpenStreetMap. This web-based map editor presents the user with various sources of open data to validate and add to OpenStreetMap, including MapWithAI roads, Microsoft buildings, and various open datasets shared via Esri.
In addition to these past data offerings, the user can now validate and add sidewalks and crosswalks derived from both Mapillary street-level imagery, as well as derived from various organizations who provide footway open data. Finally, Mapillary point data derived from imagery can also now be verified and directly converted into map data, thanks to a more efficient and rapid workflow.
We will explore all that open data available in the RapiD editor, with a specific focus on how footways are generated from Mapillary, validated from open datasets, conflated against existing OpenStreetMap data, and presented to the user for improved maps of pedestrian walkability.
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. Mobile mapping is the process of collecting geospatial data from a mobile vehicle using a 360º camera, laser scanner, GPS/IMU positioning system, and other sensors.
Many devices now include a geotag for every photo captured, and GPS accuracy can have major effects on the quality of street-level imagery and derived data. Join us in an exploration of the different accuracy levels of GPS-enabled cameras, where we will take a look at how different devices compare, and what varied levels of GPS accuracy look like both for image location and for data extracted using computer vision and structure from motion.
Understanding the differences between devices is an important step in planning street-level imagery capture, as it will align your expectations with the advantages and limitations of the hardware you use. We tested various devices and will share the results of our investigation, with the aim of equipping you to capture street-level imagery with the tools and methods that fit your needs.
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. In this talk, we will give an overview of the state of the Mapillary platform in 2022. This will include a look at compatible camera devices, upload methods, data and imagery management, download methods, integrations, and stories about users who apply Mapillary to solve a challenge.
You should walk away from this talk knowing how you want to use Mapillary to improve maps important to you, and what tools you need to get started.
If you are interested in improving OpenStreetMap, contributing to open data, capturing imagery in your community, or leveraging Mapillary street-level imagery and GIS data into your professional work, this talk is for you. No coding or technical experience is necessary, and the tools and features available can be adapted to any skill level. Join us!