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Remote Sensing, GIS, Ecology, and Oddball Techniques

FOSS4G Korea 2014, poor GPS photos, and mapillary

Posted by smathermather on August 31, 2014

As I have been moving around, whether traveling to Seoul or within Seoul, I have taken a lot of pictures. Some have GPS and I’ve processed to sent to Mapillary, like a few hundred I took on a day wandering Seoul:

Screen shot of Mapillary overview of SeoulI’ve taken a lot of strange videos too. I took a couple videos of my feet in the subway train just to get the music that plays to notify passengers of an approaching stop. Walking around Bukhansan National Park, I have taken many sign pictures. As I work for a park district, how signage and wayfinding are handled here is facinating, both from what I can understand, i.e. choice of material, color, frequency, how the letters are carved, to those elements that I cannot yet understand, i.e. exactly how the Korean Language wayfinding portions work.

But mostly I have been cataloging as much as I can in order to give my children a sense and feel for the city. I am realizing this imperative has given me a child-like view of the city. (Of course, my enthusiasm for the mundane does get me the occasional funny look from locals… . But hey! What could feel more like home than people thinking I am a little strange.)

This blog wouldn’t be mine without a technical twist to the narrative, so let’s dive into some geographic problems worth solving: The camera I have has built in GPS and compass, which makes it seemingly ideal for mapillary uploads. Except the GPS isn’t that accurate, doesn’t always update from photo to photo, struggles in urban areas in general, etc. etc.. And so it is that I am working on a little solution for that problem. First let me illustrate the problem better.

A sanity check on the GPS of the data can easily be done in QGIS using the Photo2Shape plugin:

Screen snapshot of photo2shape plugin install screen

Screenshot of distribution of camera GPS points in QGIS

Let’s do two things to improve our map. For old-time sake, we’ll add a little red-dot-fever, and use one of the native tile maps, Naver, via the TMS for Korea plugin.

Naver map with photo locations overlayed as red dots

We can see our points are both unevenly distributed and somewhat clumped. How clumped? Well, according to my fellow GeoHipsters on twitter, hex bin maps so 2013, so instead we’ll just use some feature blending (multiply) plus low saturation on our red (i.e. use pink) to show intensity of overlap:

Capture of map showing overlap of points with saturation of color increasing where overlaps exist.

Ok, that’s a lot of overlaps for pictures that were taken in a straight line series. Also, note the line isn’t so straight. Yes, I was sober. No, not even with soju can I walk though so many buildings.

Like all problems when I’m obsessed with a particular technology: “The solution here is to use <particular technology with which I am currently obsessed>”. In this case, we substitute <particular technology with which I am currently obsessed> with ‘Structure from Motion’ or OpenDroneMap. ODM would give us the correct relative locations of the original photos. Combined with the absolute locations (as bad as they are) perhaps we could get a better estimate. Here’s a start (confession — mocked up in Agisoft Photoscan. Sssh. Don’t tell) showing in blue the correct relative camera positions:

Image of sparse point cloud and relative camera positions

See how evenly spaced the camera positions are? You can also see the sparse point cloud which hints at the tall buildings of Gangnam and the trees in the boulevard.

  • Next step: Do this in OpenDroneMap.
  • Following Step: Find appropriate way to correlate with GPS positions.
  • Then: Correct model to match real world.
  • Finally: Update GPS ephemeris in original photos.

So, Korea has inspired another multi-part series. Stay tuned.

2 Responses to “FOSS4G Korea 2014, poor GPS photos, and mapillary”

  1. Very cool blog, thanks for sharing, and really cool to see the point clouds!

  2. […] FOSS4G Korea 2014, poor GPS photos, and mapillary […]

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