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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>Turning point clouds into information with open science</title>
<meta name="description" content="Turning point clouds, surfaces, and their time series into information in a framework of open geospatial science">
<meta name="author" content="Vaclav Petras">
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</head>
<body>
<div class="reveal">
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section>
<h2>
Turning
point clouds, surfaces,
<br>
and their time series
into information
<br>
in a framework of open geospatial science
</h2>
<h3 style="margin-top: 0.5em">
Vaclav Petras</h3>
<p class="title-foot">
<a href="http://www.ncsu.edu/" title="North Carolina State University">NCSU</a>
<a href="http://geospatial.ncsu.edu/osgeorel/" title="NCSU GeoForAll Lab">GeoForAll Lab</a>
at
<a href="http://geospatial.ncsu.edu/" title="Center for Geospatial Analytics">Center for Geospatial Analytics</a>
<br>
</p>
<p>March 17, 2017</p>
</section>
<!-- intro -->
<section>
<h2>Topics</h2>
<ul>
<li>Lidar and UAV point clouds
<li>Getting consistent products fast
<small>in two dimensions</small>
<li>Describing terrain changes
<small>using time-series of surfaces</small>
<li>Describing vegetation using lidar point cloud
<small>in three dimensions</small>
<!-- TODO: put this to styles -->
<li style="position: relative;">
<span class="fragment fade-out" style="position: absolute; margin-left: auto; margin-right: auto; left: 0; right: 0;" data-fragment-index="0">Publish or perish</span>
<span class="fragment fade-in" style="position: absolute; margin-left: auto; margin-right: auto; left: 0; right: 0;" data-fragment-index="0">Share or your work will perish</span>
</ul>
</section>
<!-- decimation -->
<section>
<h2>Chapter 1</h2>
<h3>Efficient processing of point clouds<br>with variable density</h3>
</section>
<section>
<h3>Point density for airborne lidar</h3>
<img class="stretch" src="img/count_lidar.png">
<br>
<small>raster resolution 1.5 m</small>
</section>
<section>
<h3>Point density for ground-based lidar</h3>
<img class="stretch" src="img/count_ground.png">
<br>
<small>raster resolution 0.5 m, red color used for 80 to 18 thousand points per cell</small>
</section>
<section>
<h3>Point density for UAV imagery SfM point cloud</h3>
<img class="stretch" src="img/count_uav.png">
<br>
<small>raster resolution 0.5 m</small>
</section>
<section>
<h3>Point density for Kinect point cloud</h3>
<img class="stretch" src="img/count_kinect.png">
<br>
<small>0.37 m × 0.35 m, raster resolution 0.002 m</small>
</section>
<section>
<h3>Decimation</h3>
<ul>
<li>decimation ~ thinning ~ sampling
<li>makes the point cloud smaller, more manageable
<li>may remove variations in point density
<li>grid-based decimation ~ binning
</ul>
<br>
<img style="width: 40%;" src="img/decimation_full.png">
<img style="width: 40%;" src="img/decimation_preserve.png">
</section>
<section>
<h3>Questions</h3>
<ul>
<li>
Which decimation performs better for topography and micro-topography?
How this changes with the point cloud acquisition method?
<li>
Is the simplest decimations enough?
Or do we need to use slower but more sophisticated techniques?
<li>
How to derive maximum raster resolution for point cloud with variable density?
Is the resolution estimate for point cloud valid when density anomalies are present?
</ul>
</section>
<section>
<h3>Evaluating level of detail</h3>
<ul>
<li>local relief model (LRM)
<li>micro-topography, features other than trend
</ul>
<br>
<img style="width: 40%;" src="img/gully_shaded_relief.png">
<img style="width: 40%;" src="img/gully_lrm.png">
<br>
<small>
30-60cm wide, 30cm deep, 60m long gully (resolution 30cm)
<!-- 294 rows, 325 cols (88.2m x 97.5m) -->
</small>
</section>
<section>
<h3>Influence of grid-based decimation resolution</h3>
<img style="width: 20%;" src="img/uav_grid_points_res_0_1_shaded_elevation.png">
<img style="width: 20%;" src="img/uav_grid_points_res_0_3_shaded_elevation.png">
<img style="width: 20%;" src="img/uav_grid_points_res_0_9_shaded_elevation.png">
<img style="width: 20%;" src="img/uav_grid_points_res_1_5_shaded_elevation.png">
<img style="width: 20%;" src="img/uav_grid_points_res_0_1_lrm_shaded.png">
<img style="width: 20%;" src="img/uav_grid_points_res_0_3_lrm_shaded.png">
<img style="width: 20%;" src="img/uav_grid_points_res_0_9_lrm_shaded.png">
<img style="width: 20%;" src="img/uav_grid_points_res_1_5_lrm_shaded.png">
<small>
grid size: 0.1 m → 0.3 m → 0.9 m → 1.5 m
<br>
<small>
(points removed: 0 % → 81 % → 98 % → 99 %)
</small>
</small>
</section>
<section>
<h3>Removing points</h3>
<div class="left">
<h4>Airborne lidar</h4>
<ul>
<li>count-based and grid-based decimations are equivalent
</ul>
<br>
<img style="width: 100%;" src="img/lrm_grid_count_lidar.png">
</div>
<div class="right">
<h4>Ground-based lidar</h4>
<ul>
<li>grid-based decimation performs better
</ul>
<br>
<img style="width: 100%;" src="img/lrm_grid_count_ground.png">
</div>
</section>
<section>
<h3>Publication</h3>
Petras, V., A. Petrasova, J. Jeziorska, and H. Mitasova (2016). <em>Processing UAV
and lidar point clouds in GRASS GIS</em>. In: ISPRS-International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 945–952
<!-- <small>[438 reads on ResearchGate, March 16, 2017 <small>(secondary PDF location)</small>]</small> -->
<br>
<img style="width: 50%" src="img/papers/petras2016processing.png">
</section>
<section>
<h3>Software</h3>
<ul>
<li>extended GRASS GIS module for binning (r.in.lidar)
<li>created GRASS GIS module for binning in 3D (r3.in.lidar)
<li>created module for count- and grid-based decimation (v.decimate)
<li>extended GRASS GIS module for point cloud import (v.in.lidar)
<li>local relief model implementation for GRASS GIS (r.local.relief)
<li>point cloud transect (v.profile.points)
<li>under development: tool which performs the tests done in the paper
</ul>
<br>
<img class="stretch" src="img/v_profile_points.png">
</section>
<section>
<h3>Planned publication</h3>
<em>
Efficient dense point cloud processing and correction
<br>
of density anomalies using decimations
</em>
<p>
...
</section>
<!-- gradients -->
<section>
<h2>Chapter 2</h2>
<h3>Mapping gradient fields of landform migration</h3>
</section>
<section>
<h3>Migrating landform</h3>
<img class="stretch" src="img/jr_anim.gif">
<small>Jockey's Ridge, 1974 - 2012</small>
</section>
<section>
<h3>Questions</h3>
<ul>
<li>
How to represent landform changes in time?
<li>
How to communicate the temporal changes correctly?
<li>
How to interpolate the missing data in a time-series?
</ul>
</section>
<section>
<h3>Simple experiment</h3>
Series of DEMs for tests created using Tangible Landscape
<img class="left" src="img/tangible_1.jpg">
<img class="right" src="img/tangible_4.jpg">
</section>
<section>
<h3>Contours, year 2001, z = 110m</h3>
<img class="stretch" src="img/grad_st1_cont.png">
</section>
<section>
<h3>Contours, year 2005, z = 110m</h3>
<img class="stretch" src="img/grad_st2_cont.png">
</section>
<section>
<h3>Contours, year 2008, z = 110m</h3>
<img class="stretch" src="img/grad_st3_cont.png">
</section>
<section>
<h3>Contours, year 2009, z = 110m</h3>
<img class="stretch" src="img/grad_st4_cont.png">
</section>
<section>
<h3>Define migration areas</h3>
<p class="small">Mask areas outside the range of 110m contour migration</p>
<img class="stretch" src="img/grad_st1234_coreenvmask2_110m.png">
</section>
<section>
<h3>Assign time attribute</h3>
<p class="small">Each 110m contour is assigned a time [year] attribute</p>
<img class="stretch" src="img/grad_st1234_110m.png">
</section>
<section>
<h3>Interpolate temporal surface</h3>
<p class="small" >Temporal surface is interpolated from a time series of 110m contours </p>
<img class="stretch" src="img/grad_st1234_surfmasked_110m.png">
</section>
<section>
<h3>Migration gradient field</h3>
<p class="small">Gradient lines over time and vectors over migration rates</p>
<img src="img/gradlines_overyear.png" style="width: 49%;">
<img src="img/gradarrows_overrates.png" style="width: 49%;">
</section>
<section>
<h3>Dynamic visualization of the gradient field</h3>
<p>Shows spatial pattern of mass concentration and dispersal over time</p>
<video class="stretch" data-autoplay muted loop>
<source src="img/comets.mp4" type="video/mp4">
</video>
<br>
<small class="credit">
Inspired by Tokyo Wind Speed application by Cameron Beccario.
Derived from <code>air.js</code> source code.
Uses HTML, CSS, JavaScript and D3.js library.
</small>
</section>
<section>
<div class="left">
<h3>Features</h3>
<ul>
<li>visual and quantitative technique
<li>magnitude and direction of change
<li>spatial distribution of rate of change
<li>highlighting unexpected changes
</ul>
<img style="width: 100%" src="img/jr_contours_all_clipped.jpg">
</div>
<div class="right">
<h3>Use cases</h3>
<ul>
<li>
analysis of 3D monitoring data or model calibration
<ul>
<li>migrating landforms
<li>evolving shorelines and islands
<li>fire spread
<li>disease spread
<li>glacier melting
</ul>
</li>
</ul>
</div>
</section>
<section>
<h3>Publication</h3>
Petras, V., H. Mitasova, and A. Petrasova (2015).
<em>Mapping gradient fields of landform migration</em>.
In: Geomorphometry for Geosciences. Ed. by Jasiewicz, J.,
Z. Zwolinski, H. Mitasova, and T. Hengl
<small>
[Best Paper Award at Geomorphometry 2015,
1062 PDF hits on March 6, 2017]
</small>
<img class="stretch" src="img/papers/petras2015mapping.png">
</section>
<section>
<h3>Software</h3>
<ul>
<li>can be implemented in any GIS-like software
<li>
<a href="https://github.com/ncsu-geoforall-lab/spatio-temporal-contour-evolution">
github.com/ncsu-geoforall-lab/spatio-temporal-contour-evolution
</a>
</li>
<li>GNU GPL <small>(code can be copied and changed)</small></li>
<li>
depends only on GRASS GIS
<small>(anybody can have what is needed)</small>
</li>
<li>GRASS GIS module <small>(convenient to get, ready to use)</small></li>
</ul>
<p>
<img src="img/logos/grass_gis.png" style="width: 10%;">
<img src="img/logos/open_source.png" style="width: 10%;">
<img src="img/logos/gnu_gpl.svg" style="width: 17%;">
<img src="img/logos/github.png" style="width: 10%;">
</section>
<section>
<div class="left">
<h3>Future work</h3>
<ul>
<li>description and interpretation of second order parameters
<li>integrating the fields across several elevation contours
<li>spatio-temporal interpolation of migrating landforms
<li>implementation and tests of the above
</ul>
</div>
<div class="right">
<h3>Planned publication</h3>
<em>
Description and interpolation of migrating landforms
using gradient fields based on spatio-temporal contours
</em>
</div>
</section>
<!-- 3D fragmentation -->
<section>
<h2>Chapter 3</h2>
<h3>Lidar-based 3D fragmentation index</h3>
</section>
<section>
<h3>Point cloud</h3>
<img class="stretch" src="img/points.png">
</section>
<section>
<h3>Questions</h3>
<ul>
<li>
How to derive and describe 3D structure captured in lidar point clouds?
<li>
Is a 2D landscape index extensible and applicable to 3D vegetation structure?
<li>
Is 3D raster representation appropriate for lidar data analysis?
</ul>
</section>
<section>
<h3>3D index of 3D raster</h3>
<img class="stretch" src="img/raster3d.png">
</section>
<section>
<h3>Profile of 3D raster</h3>
<img class="stretch" src="img/profile3d.png">
</section>
<section>
<h3>Point presence and index profiles</h3>
<img class="stretch" src="img/profiles.png">
</section>
<section>
<h3>As 2D raster</h3>
<img class="stretch" src="img/comparison_ortho.png">
</section>
<section>
<h3>Publication</h3>
Petras, V., D. J. Newcomb, and H. Mitasova.
<em>Generalized 3D fragmentation index derived from lidar point clouds</em>.
In: Open Geospatial Data, Software and Standards
[submitted Dec 07, 2016, minor reviews Mar 9, 2017]
<img class="stretch" src="img/papers/petras2017generalized.png">
</section>
<section>
<h3>Software</h3>
<ul>
<li>3D fragmentation index module (r3.forestfrag)
<li>revised 2D fragmentation index module (r.forestfrag)
<li>count categories in vertical direction (r3.count.categories)
<li>3D scatter plot of 3D raster (r3.scatterplot)
<li>3D scatter plot of 2D raster (r.scatterplot)
</ul>
<br>
<img class="stretch" src="img/r3_scatterplot.png">
</section>
<!-- open geospatial science -->
<section>
<h2>Chapter 4</h2>
<h3>A framework for open geospatial science</h3>
</section>
<section>
<h3>The case for sharing code</h3>
<blockquote style="width: 100%; margin: 0.1em;">
<p>
Software [...] developed as part of novel methods is as important
for the method's implementation [...]
Such software [...] must be made available to readers upon publication.
</p>
<p>
—Nature Methods - 4, 189 (2007)
</p>
</blockquote>
<img src="img/open/open_science.png" style="width: 50%;">
<p class="credit">Image credit: <a href="https://opensource.com/">opensource.com</a></p>
</section>
<section>
<h3>The case for using open source</h3>
<div class="left">
<blockquote style="width: 100%;">
<p>
Symbolics went bankrupt; Macsyma business was
continued by Macsyma Inc. [...] the company was sold [...]
new owner stopped Macsyma development [...]
All efforts spent on improving this branch of Macsyma
are irreversibly lost.
</p>
</blockquote>
</div>
<div class="right">
<blockquote style="width: 100%;">
<p>
Fortunately, this was not the only branch.
[...]
Professor William Schelter
ported DOE Macsyma [1982 version] to Common Lisp
[...] and developed this version
<!-- https://en.wikipedia.org/wiki/Maxima_(software) -->
until he died in 2001. This version was
called Maxima [...] under GPL
[...] is an active free software project [...]
</p>
</blockquote>
</div>
<small>Grozin, A. (2014). Introduction to MATHEMATICA for Physicists (p. 252). Springer</small>
<small>Latest release of Maxima: December 2016 <small>(last checked March 2017)</small></small>
<br>
<img src="img/logos/maxima.svg" style="width: 15%;">
</section>
<section>
<h3>Too easy to delete a GitHub repository</h3>
<blockquote>
<p>
[...] deleting a repository on GitHub takes only a few seconds
and can be done (accidentally or intentionally) by the user
who created the repository.
</p>
<p>
—Casey Bergman (2012). On the Preservation of Published Bioinformatics Code on GitHub
<!-- https://caseybergman.wordpress.com/2012/11/08/on-the-preservation-of-published-bioinformatics-code-on-github/ -->
</p>
</blockquote>
<p>
<img src="img/logos/github.png" style="width: 10%;">
</section>
<section>
<h3>More than a link to GitHub</h3>
<blockquote>
<p>
Thanks for the GitHub link… What does it do?
</p>
<p>
—Rachel Nabors (2016). Design is not a bug ticket. All Things Open 2016. Keynote
<!-- https://www.slideshare.net/CrowChick/design-is-not-a-bug-ticket-all-things-open-2016-keynote -->
</p>
</blockquote>
<p>
<img src="img/logos/github.png" style="width: 10%;">
</section>
<section>
<h3>Software as part of research</h3>
<ul>
<li>is open source
<li>is based on open source
<li>has stable place for code
<li>is more than code
</ul>
</section>
<section>
<h3>Integrating code into GRASS GIS</h3>
<ul>
<li>Do I want to create new open source project?
<li>New methods as new GRASS GIS modules or improvements of existing ones
<li>Preprocessing, visualization, and user interface included
<li>Well integrated with existing analytical tools
</ul>
</section>
<section>
<h3>Reproducibility</h3>
<ul>
<li>reproducibility ~ repeatability ~ replicability ~ recomputability
<li>methods as GRASS GIS modules
<small style="color: gray;">(C and Python)</small>
<li>scripts to perform the analyses
<small style="color: gray;">(Bash and Python)</small>
<li>data for the study area
<small style="color: gray;">(open formats)</small>
<!-- <small style="color: gray;">(small enough)</small> -->
<li>details about all dependencies
<small style="color: gray;">(Dockerfile)</small>
<li>code repository → complete runtime environment
<small style="color: gray;">(Git, GitHub, Linux, Docker)</small>
<li>continuous integration service
<small style="color: gray;">(Travis-CI)</small>
</ul>
<small>
Petras, V., D. J. Newcomb, and H. Mitasova.
<em>Generalized 3D fragmentation index derived from lidar point clouds</em>.
In: Open Geospatial Data, Software and Standards
[minor reviews Mar 9, 2017]
</small>
</section>
<section>
<h3>Testing framework for GRASS GIS</h3>
<img class="stretch" src="img/papers/petras2014testing.png">
<p>
<small>
Petras, V. and Gebbert, S. (2014).
<em>Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing</em>.
In: AGU Fall Meeting
Abstracts. Vol. 1, p. 3758
</small>
</section>
<section>
<h3>How code in GRASS GIS develops over time</h3>
<img class="stretch" src="img/papers/petras2017how.png">
<p>
<small>
Petras, V., Y. Chemin, M. Landa, T. Leppelt, P. Zambelli, L. Delucchi, M. Di
Leo, S. Gebbert, and M. Neteler (2017).
<em>How innovations thrive in GRASS GIS</em>.
NCGIS2017, Raleigh, NC, USA.
<br>
<small>Also: Petras (2015) and Chemin (2015) at EGU</small>
</small>
</section>
<section>
<h3>Planned publication</h3>
<em>
A framework for geospatial open science
<br>
as implemented in GRASS GIS,
<br>
a geospatial research platform
</em>
</section>
<section>
<h3>Questions</h3>
<ul>
<li>What are the differences between software tool and research platform?
<li>Who are the authors of significant additions to the code?
<ul>
<li>Are they the same as the maintainers of the code?
<li>Are they the same as the original authors?
</ul>
<li>How much original research is in the software?
<ul>
<li>What is the ratio of original research code and implementations of existing methods?
<li>How many researches are contributing the original research?
</ul>
<li>What makes software and code suitable for research and for practitioners?
</ul>
</section>
<!-- appendix -->
<section>
<h2>Appendix</h2>
<h3>Selected additional projects</h3>
<ul>
<li>Urban growth model – FUTURES
<li>Disease spread model – SOD
<li>Seamless desktop and remote computations
<li>Tangible Landscape for QGIS
<li>Course: Tools for open geospatial science
</ul>
</section>
<!-- modeling -->
<section>
<h2>FUTURES PGA – before</h2>
<ul>
<li>configuration file based interface
</ul>
<br>
<img class="stretch" src="img/futures_config.png">
</section>
<section>
<h2>FUTURES PGA – before</h2>
<ul>
<li>preprocessGISData.cpp
</ul>
<img class="stretch" src="img/futures_code_constants.png">
</section>
<section>
<h2>FUTURES PGA – after</h2>
<ul>
<li>GUI, command line, Python
</ul>
<img class="stretch" src="img/futures_pga_gui.png">
</section>
<section>
<h2>FUTURES PGA – after</h2>
<ul>
<li>faster <small>input/output, efficient memory usage</small>
<ul>
<li>binary input and put reduces time spent by I/O operations
<li>only memory which is needed is used
</ul>
<li>flexible <small>inputs, interface</small>
<ul>
<li>resolution and extent can be changed arbitrarily
<li>easy to add new parameters
<li>control over stochastic outcomes
</ul>
<li>fixed <small>memory management, edge cases</small>
<ul>
<li>all memory operations are done correctly (no failures or random results)
<li>handling of cases such as unresolved items from previous iteration
</ul>
</ul>
</section>
<section>
<h2>Sudden oak death model</h2>
<ul>
<li>hardcoded parameters → GUI + CLI (GRASS GIS)
</ul>
<img class="stretch" src="img/sod_screenshot.png">
</section>
<section>
<h2>Sudden oak death model</h2>
<ul>
<li>multiple stochastic runs which run in parallel
<ul>
<li>OpenMP
<li>week-based simulation but threads created for
chunks of weeks to use less resources
(in comparison for one thread for each week of every run)
<li>inputs shared over threads
</ul>
</ul>
<img class="stretch" src="img/sod_screenshot.png">
</section>
<section>
<h2>Sudden oak death model</h2>
<ul>
<li>optimization
<ul>
<li>inlining (reduces function call overheads)
<li>C++11 move semantics (avoids copy operations)
</ul>
<li>undefined behavior → correct memory management
<li>tested and versioned <!-- issues of testing (2 bugs) -->
</ul>
<img class="stretch" src="img/sod_screenshot.png">
</section>
<!-- g.remote -->
<section>
<h2>Hybrid desktop-server workflow</h2>
<ul>
<li>locally: parts of processing, test runs on smaller data
<li>remotely: big data storage and processing
</ul>
<pre class="bash" style="font-size: x-large;"><code>g.region "small"
# v.surf.rst runs locally
v.surf.rst input="points" elevation="terrain_surface"
g.region "large"
# v.surf.rst runs on a remote machine
g.remote server="example.ncsu.edu" ... \
grassdata="grassdata" location="nc_spf_gremote_2" mapset="practice1" \
vector_input="points" raster_output="terrain_surface" \
--exec \
v.surf.rst input="points" elevation="terrain_surface"</code></pre>
<img class="stretch" src="img/g_remote.svg">
</section>
<!-- Tangible Landscape -->
<section>
<h2>Tangible Landscape for QGIS</h2>
<ul>
<li>QGIS used regularly to teach GIS on basic level
<li>Tangible Landscape suitable for teaching geospatial concepts
<li>Deploy both together
<li>GRASS GIS as processing backend
</ul>
<br>
<img class="stretch" src="img/logos/qgis.png">
</section>
<!-- open science course -->
<section>
<h2>Course<br>Tools for open geospatial science</h2>
Petras, V., A. Petrasova, B. Harmon, R. K. Meentemeyer, and H. Mitasova (2015).
<em>Integrating free and open source solutions into geospatial science education</em>.
In: ISPRS International Journal of Geo-Information 4.2, pp. 942–956
[1582 full-text views, MPDI on March 6, 2017]
<br>
<img class="stretch" src="img/papers/petras2015integrating.png">
<p><small>
Also:
Rocchini, D., V. Petras, A. Petrasova, N. Horning,
L. Furtkevicova, M. Neteler, B. Leutner, and M. Wegmann.
<em>Open-access and open-source for remote sensing training in ecology and conservation</em>
[submitted to Ecological Informatics, Jan 20, 2017]
</small>
</section>
<section>
<h2>Course<br>Tools for open geospatial science</h2>
<ul>
<li>Collaborative writing of scientific papers
<li>Revision control systems and wiki technologies
<li>QGIS + GRASS GIS
<li>command line, remote access, Linux
<li>Interactive notebooks
<li>Publishing code as part of an open source project
<li>Publishing data on web
<li>Reproducible computational environments
</ul>
<br>
<img class="stretch" src="img/open/open_pens.png">
<p class="credit">Image credit: <a href="https://opensource.com/">opensource.com</a></p>
</section>
<section>
<h2>Course<br>Tools for open geospatial science</h2>
<div class="left">
<h4>What it is</h4>
<ul>
<li>a course with research focus
<li>a course with extension to industry
<ul>
<li>tools like Git are daily bread in many companies
</ul>
</ul>
<img style="width: 100%;" src="img/open/open_books.png">
<p class="credit">Image credit: <a href="https://opensource.com/">opensource.com</a></p>
</div>
<div class="right">
<h4>What it is not</h4>
<ul>
<li>a course limited to geospatial topics
<ul>
<li>authoring, management, computer knowledge needed as well
</ul>
<li>the complete & comprehensive open science course
<ul>
<li>this one is focused on software tools
<li>not data, open access, ...
</ul>
<li>the course where you learn open source GIS
<ul>
<li>this should happen in all other courses
</ul>
</ul>
</div>
</section>
<section>
<h2>Jupyter Notebook</h2>
<ul>
<li>interactive document with text, code, and figures
</ul>
<br>
<img class="stretch" src="img/jupyter.png">
</section>
</div> <!-- slides -->
</div> <!-- reveal -->
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