Comments (8)
My home lap contains a 5 km long monotonous ascend in a valley, which does highlight the limitations of the SRTM data:
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The barometer data was obtained from the average of 10 trips (5 in each direction) along the route:
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Hi Sonny,
yes updating the eleavation model is definitely on the agenda. It's not space savings that prevented updating to 1'' srtm data so far. It's just that I could not prove a significant effect on the results. Switching to lidar also requires some QA to look for edge effects. But I'm sure it will be an improvement.
Thanx for providing the aggregated data, really helpful.
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@abrensch
It seems you have some ideas for testing the data. Great. I have already prepared an update, but it will take some days for preparation.
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I generated segments using Sonny's 3asec DTM for germany and a (slow) routing instance is available at https://brouter.knid.net/
@quaelnix Can you share this route and generate a plot which also includes the lidar data?
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It's not space savings that prevented updating to 1'' srtm data so far. It's just that I could not prove a significant effect on the results.
Unless I've done something wrong, this seems to apply to the LiDAR data as well, which honestly surprises me a bit.
I'm sure it will be an improvement.
It seems like a huge improvement (and it also greatly improves the accuracy of travel time estimation):
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@abrensch, I've been using the LiDAR elevation data extensively over the past few weeks, and I have to say it's amazing.
However, depending on what elevation data we use, this seems to require different thresholds:
brouter/brouter-core/src/main/java/btools/router/RoutingEngine.java
Lines 840 to 845 in ef73d46
5 m instead of 10 m apparently does the trick for the LiDAR data.
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My winter commute along two rivers with several bridges and many underpasses:
@rkflx, I'm having a hard time finding even one place where the LIDAR data (1asec) fails.
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