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Satellite Data and Super-Resolution to enhance a Slope Soaring Simulator by Schalk Heunis

Satellite Data and Super-Resolution to enhance a Slope Soaring Simulator by Schalk Heunis

Imagine soaring the slopes of el-Capitan in Yosemite, Rainbow mountain in Peru or even replicating the wingsuit glide path of Jeb Corliss! And doing it under lockdown

Slope soaring requires one to go outside and spend time with friends which was not possible during lockdown, so the next best thing is a simulator.

This talk will cover how a 10 year old, abandoned open source simulator (SSS - http://www.rowlhouse.co.uk/sss/index.html) was enhanced using Python with:

* Digital Elevation Models from Google Earth Engine
Enhancing Elevation Model Super Resolution using Deep Learning
* Satellite imagery as scenery from anywhere in the world courtesy of Bing
* Multi-player networking

The result is stunning scenery and the ability to fly any slope you can dream of.

The talk will cover:

* Introduction to the slope soaring simulator (SSS) and motivation for the wanting to use this under COVID'19 lockdown
* Multi-player networking using Python cstruct and socket libraries
* Extracting digital elevation models (DEM) and satelite data using Google Earth Engine Python API
* Downloading aerial photography from Bing using Python and mapping it to the DEM
* Downloading Hi-res LIDAR point cloud data and processing as target for Super Resolution using PDAL Python API
* Enhancing the elevation model using Deep Learning Super Resolution from 30m to 5m accuracy
* Adding skybox scenery to complete the picture

This talk is for anyone interested in working with satellite data, super-resolution and slope soaring simulations

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Pycon ZA

October 09, 2020
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Transcript

  1. Satellite data & Super resolution to enhance a Slope Soaring

    Simulator Schalk Heunis
  2. Previous Pycon Talks • 2019 -Edge Computing at the Edge

    of the World
  3. None
  4. None
  5. Amphitheatre – Climb & Fly 12 April

  6. Slope Soaring Simulator

  7. Slope Soaring Simulator

  8. None
  9. Multi Two player

  10. Two player à Multi player Python Server SSS SSS SSS

    SSS struct
  11. cstruct • pip install cstruct

  12. None
  13. Import DEM • Add slide with orog documention

  14. Digital Elevation Models

  15. Digital Elevation Model

  16. Digital Elevation Model

  17. Digital Elevation Model - Python • pip install rasterio

  18. Digital Elevation Model - Python

  19. https://touchterrain.geol.iastate.edu

  20. Satellite Data

  21. Google Earth Engine • pip install earthengine-api

  22. Google Earth Engine

  23. None
  24. None
  25. Higher Resolution Data: LiDAR

  26. LiDAR

  27. https://usgs.entwine.io/

  28. Super Resolution Learn transform Low Res (30m) High Res (5m)

  29. Super Resolution • GAN • Unsupervised • Hard to train

    • Cool • Supervised • Simple • Fast to train
  30. Fast.AI

  31. Fast.ai • conda install -c fastai -c pytorch -c anaconda

    fastai gh anaconda • Lesson 7: Resnets from scratch; U-net; Generative (adversarial) networks • https://course19.fast.ai/ • https://github.com/fastai/course- v3/blob/master/nbs/dl1/lesson7-superres.ipynb
  32. U-Net: Convolutional Networks for Biomedical Image Segmentation 41m parameters Fast

    AI: Pretrained Resnet34
  33. Super Resolution – fast.ai – UNet

  34. LiDAR - Python • pip install PDAL

  35. PDAL : resource • https://usgs-lidar-public.s3-us-west-2.amazonaws.com/{resource}/ept.json • resource:

  36. Training data • 16 446 tiles • Each tile 128x128

    @ 5m • 0.4 km2 • 6 736 km2
  37. Train & Validate Low Res (30m) Predicted Hi Res (5m)

  38. Amphitheatre Low Res (30m) Predicted (5m)

  39. Amphitheatre Low Res (30m) Predicted (5m)

  40. Add ground texture • Bing Maps • https://github.com/llgeek/Satellite-Aerial-Image-Retrieval

  41. Ampitheatre

  42. None
  43. None
  44. None
  45. El-Capitan Yosemite

  46. Rainbow Mountain Peru

  47. Glider & cockpit view

  48. Jeb Corliss – “Grinding The Crack”

  49. None
  50. None