/sci/ - Science & Math » Thread #11802634

3MiB, 640x360, lidar.webm

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Anonymous Mon 15 Jun 22:40:34 2020 No.11802634 View Reply Original Report

Quoted By: >>11802660 >>11804637

How could you infer the most probable arrangement of trees in a forest given LIDAR scans that only show you the canopy?

Anonymous

Anonymous Mon 15 Jun 2020 22:46:14 No.11802660 Report

Quoted By: >>11804769

>>11802634
machine learning

Anonymous

Anonymous Tue 16 Jun 2020 14:00:42 No.11804637 Report

Quoted By: >>11804769

>>11802634

Minimize and maximize the number of trunks, root systems. Your answer is btwn the two answers, dependent on other factors such as lad/long, altitude, etc.

Anonymous

Anonymous Tue 16 Jun 2020 15:00:32 No.11804769 Report

Quoted By: >>11805594

>>11802660
no.
what makes people think that any problem can be magically solved just invoking google! and machine learning! deep learning!

>>11804637
also, some info on the expected tree species would be helpful to tame the huge degeneracies of the problem

Anonymous

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Anonymous Tue 16 Jun 2020 15:52:12 No.11804902 Report

Quoted By:

https://vimeo.com/228391688

I saw this video on biologically inspired procedural generation of tree 3D models for games/VFX stuff a while ago. I wonder if it's possible to leverage and expand on this somehow. Bayesian statistical modelling is essentially about creating a generative model of your data which these procedural generation tools do. Maybe you can do Bayesian inference by creating a conditional distribution over the possible forests given the canopy structure which you can then sample from with MCMC or something like that. I'd imagine it'd be very computationally expensive though.

Anonymous

Anonymous Tue 16 Jun 2020 19:05:27 No.11805594 Report

Quoted By:

>>11804769
yes just feed in correct arrangements of trees based on lidar scans and have it learn from those. You are the one talking about magic dunning-kruger.

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