I have a large set of photographs I made while running. They are geotagged, as I took them with my phone camera. The compass direction is completely unreliable, but lat/lon is more trustworthy. I thought it would be an interesting experiment to extract greenery like grass and trees from these photographs. It can be a useful addition for creating routes that are more pleasant to walk, since the eye-level point of view is not available in OSM. As this is based on my personal photographs, it has the additional benefit of recommending routes that I tend to use. The first challenge I encountered is that out of a few thousand photographs, only a handful were taken during the daytime. After deduplicating and dropping all photos that contain no greenery, this becomes a relatively small set of waypoints. I decided not to extrapolate additional points along OSM ways to keep the dataset small and avoid adding misleading info. The greenery detection works well enough with the SegFormer model, although it is somewhat slow locally. My plan is to select waypoints from this dataset before calling OSRM. This way I get routes that are more enjoyable to walk and run, but are generally longer than the default shortest route. You can find my dataset on Kaggle.

For a while, I was interested in understanding what makes one pedestrian OSM way better than another. I wanted to know if there is some generic way to identify good walking routes from OSM data. I looked at Garmin and Strava heatmaps at first. Then I checked Strava segments and their proximity to points of interest such as rivers, ponds, and parks. Then I thought to look at my running pace along OSM ways to separate good and not-so-good walking routes. My idea was simple — a good walking route means a smooth running pace. There are fewer stops, less waiting at intersections, etc. Of course, my pace depends on many factors, such as how far I have to run to get to a certain place. So it cannot be a simple cutoff, but rather the distribution of paces along a given segment. This turned out to be a reasonably good approximation of how good or bad I perceive each route to be. I created this Kaggle dataset as an illustration. This relies on my personal GPX data, so it does not scale, but it captures the kind of local knowledge that I find hard to share in any other way.

I mainly edit OSM based on GPX files from my runs. I use JOSM. I created a simple script to extract a buffered area around my GPX files to make sure I don’t accidentally edit parts of OSM I don’t intend to. See this Github Gist for the source.

I find that the width of OSM ways is a useful property for determining how good a pedestrian route is. However, it is often missing from OSM. As an experiment, I decided to use my running activities from Strava to estimate the width of a single OSM way that I use often. The specific way ID I used is in a relatively open area, meaning GNSS error is minimized. I also have collected over 100 traces of me running that single way ID over ~1.5 years. Given all this, how accurate can the estimate of the width be? I got the median width to be in the range of 11 meters. The actual width as measured with Google Maps satellite imagery is 13 meters. It’s close. I am happy with the result. I don’t have nearly as many traces for any other segment on the OSM map, so it’s a limited experiment, but the potential is promising. See the code on Github.

I was visiting Sa Pa, Vietnam, and navigating with Organic Maps. I was looking for a street that would bring me back to the city center. I could not see any on OSM or Google Maps. I walked for a while and was able to see a street that led in the right direction. It turns out that it connected to another street that brought me where I wanted to go. This made me realize how much of the useful information in maps depends on people walking, running, or commuting through those streets. You cannot see these kinds of streets from satellite images. You can only know them, but knowing them, you may not use GPS tracking to record them. I think this leaves only runners and anyone who likes walking to discover most of the streets that are not currently on the map.