This year the weather has been quite awful in Milan, and Sunday 20th it was raining once again. However, later during the day it was nice and even sunny, so I decided to go for another trip.

This time I planned in advance and went to Villaggio dei giornalisti, an area that was not particularly covered by previous footage. It is an area of architectural interest and particularly nice.

After that, I went back to the Parco Nord, covering more bike paths in the park.

This time I configured the camera correctly to record a time-lapse, getting around 4 GB of videos for a 2h trip, a much more reasonable file size. The battery also lasted longer, more than 2h and could have gone for much longer. I had to end my trip because it was getting dark.

Another thing I learned from the previous time was to use a fitness tracker to track my trip, so now I can see the calories and speed overall, and observe the trip later on Strava. I also could produce a GPX track, but they are already abundant on OSM in this area.

I still have to figure out an effective way to micromap based on these pictures, iD integrates with Mapillary but the interface is a bit cumbersome for large changes, currently I find easier to open another window with the footage only.

In addition to OSM, I occasionally take pictures of the areas I visit to upload them on Mapillary.

There are a few reasons I enjoy contributing to Mapillary:

• it constitutes an historical archive of pictures over time, seeing how a corner changed brings up memories and documents changes. I personally lived in two cities (Milan and Berlin) that changed a lot over the years and like to document it and observe these changes over time
• in Milan it can be used to document the space used by vehicles, including the irregular one, which is a detail not present in OSM tags
• it’s suitable for machine learning. I for one have a second job as a teacher for Data Science Retreat and a few students used this data to classify the quality of bike paths. Mapillary already automatically identifies, geolocate and segment common elements like street signs and manholes
• it’s suitable (sometimes) for 3D reconstructions for artistic purposes, like games (example here, it’s not based on ad-hoc images but mapillary could reasonably be used for it too. Mapillary calculates and provides point clouds too). I think this is an area where new approaches like gaussian splatting can do a lot
• it can, of course, be used for micromapping in OSM, these images show shop names, street etymology, fences and other details not easy to spot from satellite data and hard to add on the spot using StreetComplete or similar. Images from Mapillary are accessible directly in the iD editor.

For these and other reasons I contribute to Mapillary since years, using my phone with the Mapillary app whenever I visit a new or interesting place.

Imagine then my surprise when, a few months ago, I was contacted by Meta’s Reality Lab. They saw my contributions in Milan and offered me to borrow their GoPro MAX 360 which was not used anymore by another Mapillary contributor, to contribute as I see fit.

… See full entry

• Sparse OSM tools: osm.org/user/ivanbranco/diary/401437
• OSM Links from another user diary: osm.org/user/GrapeMapping/diary/401815
• Quick interactive analysis of the map stats and how they evolve over time (e.g. “how many trees are in the map in Italy”), includes estimation of completeness https://dashboard.ohsome.org/
• Gallery of cartography projects, mainly about Berlin https://hanshack.com/
• 30 Day Map challenge: https://30daymapchallenge.com/
• Even more beautiful maps: https://tjukanov.org/