How good (bad) is water represented in OpenSteetMap?

Posted by GenaD on 22 April 2015 in English (English)

A a part of my PhD research, related to the setup of coupled hydrological/hydraulic models using open data at sub-100m resolution and at a global scale, I'm validating how good / bad is water represented in OpenStreetMap. At present, my study area is limited to Murray-Darling River Basin, but eventually it will be rolled-out to the whole Earth. I use Google Earth Engine and a bunch of open-source tools, like PCRaster, GDAL, Fiona. The idea is to perform most of the analysis in the Cloud.

The methodology includes the use of SRTM 30m, Height Above the Nearest Drainage (HAND), 30m resolution, HydroBASINS and LANDSAT 8 datasets.

For more information see my PPT presentation from European Geoscience Union 2015. Abstract: DOI:10.13140/RG.2.1.1514.9601.

Daily routine usually looks like this:


Supported by: Deltares, Earth2Observe and Google.

Location: Wallaroo, Yass Valley Council, New South Wales, 2618, Australia

Comment from pizzaiolo on 22 April 2015 at 19:04

Very interesting! Looking forward to hearing more about it.

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Comment from imagico on 22 April 2015 at 20:24

Nice to see waterbody mapping and use of OSM waterbody data gathering attention outside OSM itself.

Looking at your presentation it seems however your methodology has quite a few issues, both in detail and en large - some of them will likely 'bite you in the back' when you try to use that approach in other areas.

The most basic one probably is that the techniques you compare do very different things. In OpenStreetMap we map bodies of water, either standing or flowing. When analyzing Landsat images you map surface water and when analyzing elevation data you map potential drainage lines. It should be obvious that these three methods - even if all of them work perfectly - will produce diverging results.

Your approach to Landsat/SRTM reminds me of something i did several years ago. You are lucky you do not have snow and glaciers or frozen lakes in Australia...

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Comment from GenaD on 22 April 2015 at 21:52

Thanks for the link, comments and imagico, very useful!

There certainly will be more issues and you're right about the 'very different things'. However, all 3 datasets still indicate places with the maximum likelihood of water. With a proper understanding - a nice data fusion should be possible. In any case, I want to analyse the differences pairwise between different dataset after splitting them into areas where different approach can be used to do the analysis. For example:

  1. If you can observe water from LANDSAT - probably that will be the most precise estimate (horizontally +/- 15-30m). Then comparing OSM with LANDSAT water centerline probably makes sense. It will be also nice to check how good SRTM looks like in these areas and I certainly won't use SRTM to define centerlines for these rivers, actually I plan to burn SRTM for the areas where it does not match.

... OpenStreetMap water based on 1m imagery is still better, but we don't know that. From what I saw, water centerline for wide rivers is not always a centerline in OSM. I'm not sure if there is an agreement on that, most probably if differs from place to place. For modeling applications it would be actually good to have a geometric centerline and then thalweg.

  1. For small rivers located in hilly areas SRTM might be the best guess, but again, if OSM is edited manually - theoretically it should be much better, especially when there is a riparian vegetation present (SRTM will be noisy) :).

Based on flow accumulation area it will be possible to estimate approximately how big the river ~actually is. Maybe based on the landuse can estimate the SRTM noise.

  1. For urban areas it will be a mess, probably LANDSAT (for large waterbodies) and then OSM is the best guess.

About applicability to other areas, I expect the following dataset to come out somewhere soon: Peckel, 2014, so my focus is not to "detect water in the best way using LANDSAT" but rather to focus on the methodology to compare different vector/raster river datasets given watermask based on LANDSAT.

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