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<p><strong>About This Course</strong></p>
<p>The course "Analysis of Urban Transformation Processes: Python and Earth Observation Data" introduces participants to the use of Python and Earth Observation data to analyze and visualize various urban transformation processes. Participants will learn to filter and select feature and image collections, perform needed analytical metrics, and interpret spatial data using Google Earth Engine (GEE). Key topics include flood detection with Sentinel-1 data, nighttime light trend analysis, and air quality measurement using Sentinel-5p data. Through practical examples and exercises, participants will gain hands-on experience in urban analysis and environmental monitoring <a href="https://git.rwth-aachen.de/nfdi4earth/edutrain/content/extern/analysis-of-urban-transformation-processes" target="_blank" rel="noopener">(Access this course on GitLab)</a>.</p>
<p><strong>Level</strong></p>
<p>Intermediate, Advanced</p>
<p><strong>Requirements</strong></p>
<p>Basic knowledge of Python, digital image processing, and Geographic Information Systems is required.</p>
<p><strong>Course Curriculum</strong></p>
<p><strong>Lesson 1:</strong> Introduction and Preparation</p>
<p><strong>Lesson 2:</strong> Feature Collection Basics</p>
<p><strong>Lesson 3:</strong> Image Basics &amp; Filters: Landuse Extraction</p>
<p><strong>Lesson 4:</strong> Landcover Extraction</p>
<p><strong>Lesson 5:</strong> Create Timelapse GIFs from Landsat Satellite Data</p>
<p><strong>Lesson 6:</strong> Flood Detection</p>
<p><strong>Lesson 7:</strong> Air Quality Assessment</p>
<p><strong>Lesson 8:</strong> Nighttime Light Trends</p>
<p><strong>Resources</strong></p>
<p>EduPilot "The future is urban, the data is smart" by <a href="https://orcid.org/0000-0003-3893-3298" target="_blank" rel="noopener">Andreas Rienow</a> and <a href="https://orcid.org/0009-0000-3924-5230" target="_blank" rel="noopener">Lars Tum</a></p>
<p><strong>Administration</strong></p>
<p><a href="mailto:farzaneh.sadeghi@hs-bochum.de">Farzaneh Sadeghi</a></p>
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<meta name="author" content="Andreas Rienow, Lars Tum"/>
<meta name="subjectArea" content="Land System Science, Geography, Remote Sensing, Earth Observation, GI Science, Urban Planning"/>
<section class="about" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>About This Course</h2>
<p>The course "Analysis of Urban Transformation Processes: Python and Earth Observation Data" introduces participants to the use of Python and Earth Observation data to analyze and visualize various urban transformation processes. Participants will learn to filter and select feature and image collections, perform needed analytical metrics, and interpret spatial data using Google Earth Engine (GEE). Key topics include flood detection with Sentinel-1 data, nighttime light trend analysis, and air quality measurement using Sentinel-5p data. Through practical examples and exercises, participants will gain hands-on experience in urban analysis and environmental monitoring <a href="https://git.rwth-aachen.de/nfdi4earth/edutrain/content/extern/analysis-of-urban-transformation-processes" target="_blank">(Access this course on GitLab)</a>.</p>
</section>
<section class="difficulty" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Level</h2>
<p>Intermediate, Advanced</p>
</section>
<section class="prerequisites" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Requirements</h2>
<p>Basic knowledge of Python, digital image processing and Geographic Information Systems is required.</p>
</section>
<section class="subjectArea" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Subject Area</h2>
<p>Land System Science, Geography, Remote Sensing, Earth Observation, GI Science, Urban Planning</p>
</section>
<section class="content-overview" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>What You Will Learn</h2>
<ul>
<li>Introduction and Preparation</li>
<li>Feature Collection Basics</li>
<li>Image Basics & Filters: Landuse Extraction</li>
<li>Landcover Extraction</li>
<li>Create Timelapse GIFs from Landsat Satellite Data</li>
<li>Flood Detection</li>
<li>Air Quality Assessment</li>
<li>Nighttime Light Trends</li>
</ul>
</section>
<section class="credits" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Resources</h2>
<p>
EduPilot "The future is urban, the data is smart" by <a href="https://orcid.org/0000-0003-3893-3298" target="_blank">Andreas Rienow</a> and <a href="https://orcid.org/0009-0000-3924-5230" target="_blank">Lars Tum</a><br>
</p>
<h2>Administration</h2>
<p><a href="mailto:farzaneh.sadeghi@hs-bochum.de">Farzaneh Sadeghi</a></p>
</section>
<footer style="color: black !important; text-align: justify !important; font-size: 0.7em !important; font-family: Calibri, Arial, Helvetica, sans-serif !important;">
<hr style="border-top: 2px solid #ccc; width: 50%; margin-left: 0;">
<p>
This content is based on the EduPilot "The future is urban, the data is smart" by <a href="https://orcid.org/0000-0003-3893-3298" target="_blank">Andreas Rienow</a> and <a href="https://orcid.org/0009-0000-3924-5230" target="_blank">Lars Tum</a>, which is licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" style="color: black !important; text-decoration: underline !important;">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>. This edited content is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" style="color: black !important; text-decoration: underline !important;">CC BY-NC-SA 4.0</a>.
</p>
</footer>
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Lesson 2_Feature_Collections_Basics.ipynb
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%% Cell type:markdown id:f42c0ae8 tags:
### 📖Feature Collection Basics
In this chapter, you will learn how to filter / select features of a feature collection in Google Earth Engine using Python in Jupyter Notebook.
%% Cell type:markdown id:6410c599 tags:
Import necessary modules and authenticate the google-access (with a token, if needed).
%% Cell type:code id:cc3faf61 tags:
``` python
!mamba env create -f smart_data.yml
```
%% Cell type:code id:ea742ffb tags:
``` python
import ee
import geemap
```
%% Output
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[3], line 1
----> 1 import ee
2 import geemap
ModuleNotFoundError: No module named 'ee'
%% Cell type:code id:692c99df tags:
``` python
import ee
ee.Authenticate()
```
%% Cell type:code id:ca054848 tags:
``` python
ee.Authenticate()
ee.Initialize()
```
%% Cell type:code id:b7ad7048 tags:
``` python
Map = geemap.Map() # sets a variable to open the GEE Map
```
%% Cell type:code id:5aaa8fa6 tags:
``` python
Map # opens the interactive map
```
%% Cell type:markdown id:f3006eb4 tags:
Get some data to display
The "ee.FeatureCollection('TIGER/2018/States')" is a dataset, which lies on the google servers. See Google datacatalog for more data: https://developers.google.com/earth-engine/datasets
%% Cell type:code id:980d31a9 tags:
``` python
states = ee.FeatureCollection('TIGER/2018/States') # defining a "states" variable
Map.addLayer(states, {}, "US States") # add the layer of the variable
```
%% Cell type:code id:4840f85c tags:
``` python
tx = states.filter(ee.Filter.eq("NAME", "Texas")) # filter the feature collection "states" for a state: Texas
Map.addLayer(tx, {}, "Texas") # add the layer of the filtered feature
```
%% Cell type:code id:d1023e53 tags:
``` python
ca = states.filter(ee.Filter.eq("NAME", "California")) # do it again for another state
Map.addLayer(ca, {}, "California") # add the layer of another feature
```
%% Cell type:markdown id:6512cac0 tags:
Before executing the next 3 cells, **use one of the drawing tools from the left side bar ("Draw a Polygon", "Draw a Rectangle" etc.) and draw a shape on the map.**
The command "Map.user_roi" will select your drawn shape
%% Cell type:code id:6f6525fa tags:
``` python
roi = Map.user_roi # setup a region of interest which you would like to filter features of
```
%% Cell type:code id:ada1c28b tags:
``` python
selected = states.filterBounds(roi) # define a new variable to add the layer of the filtered features
```
%% Cell type:code id:4806b60c tags:
``` python
Map.addLayer(selected, {}, "US South") # add the feature-layer
```
%% Cell type:code id:8ded2205 tags:
``` python
west = states.filter(ee.Filter.inList("NAME", ["Utah", "Colorado"])) # choose some features to display them at once
```
%% Cell type:code id:e03950c3 tags:
``` python
Map.addLayer(west, {}, "US West") # add the layer displaying the western states of the US
```
%% Cell type:markdown id:67395e43 tags:
If you run everything, you can adjust the opacity and the general display of every layer in the top right corner.
There also is the inspector feature for more information.
%% Cell type:code id:33046abe tags:
``` python
# Remove not selected features or layers from the map
Map.remove_drawn_features()
Map.remove_ee_layer("US States")
```
......
README.md 0 → 100644
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<meta name="author" content="Farzaneh Sadeghi, Hydrogeology Modeling Group (KIT)"/>
<meta name="subjectArea" content="Geoinformatics, Climate Science, Earth System Science, Hydrology"/>
<section class="about" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>About This Course</h2>
<p>The course "Analysis of Urban Transformation Processes: Python and Earth Observation Data" introduces participants to the use of Python and Earth Observation data to analyze and visualize various urban transformation processes. Participants will learn to filter and select feature and image collections, perform needed analytical metrics, and interpret spatial data using Google Earth Engine (GEE). Key topics include flood detection with Sentinel-1 data, nighttime light trend analysis, and air quality measurement using Sentinel-5p data. Through practical examples and exercises, participants will gain hands-on experience in urban analysis and environmental monitoring</p>
</section>
<section class="difficulty" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Level</h2>
<p>Intermediate, Advanced</p>
</section>
<section class="prerequisites" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Requirements</h2>
<p>Basic knowledge of Python, digital image processing and Geographic Information Systems is required.</p>
</section>
<section class="subjectArea" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Subject Area</h2>
<p>Land System Science, Geography, Remote Sensing, Earth Observation, GI Science, Urban Planning</p>
</section>
<section class="content-overview" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>What You Will Learn</h2>
<ul>
<li>Introduction and Preparation</li>
<li>Feature Collection Basics</li>
<li>Image Basics & Filters: Landuse Extraction</li>
<li>Landcover Extraction</li>
<li>Create Timelapse GIFs from Landsat Satellite Data</li>
<li>Flood Detection</li>
<li>Air Quality Assessment</li>
<li>Nighttime Light Trends</li>
</ul>
</section>
<section class="credits" style="font-family: Calibri, Arial, Helvetica, sans-serif; text-align: justify;">
<h2>Resources</h2>
<p>
EduPilot "The future is urban, the data is smart" by <a href="https://orcid.org/0000-0003-3893-3298" target="_blank">Andreas Rienow</a> and <a href="https://orcid.org/0009-0000-3924-5230" target="_blank">Lars Tum</a><br>
</p>
<h2>Administration</h2>
<p><a href="mailto:farzaneh.sadeghi@hs-bochum.de">Farzaneh Sadeghi</a></p>
</section>
<footer style="color: black !important; text-align: justify !important; font-size: 0.7em !important; font-family: Calibri, Arial, Helvetica, sans-serif !important;">
<hr style="border-top: 2px solid #ccc; width: 50%; margin-left: 0;">
<p>
This content is based on the EduPilot "The future is urban, the data is smart" by <a href="https://orcid.org/0000-0003-3893-3298" target="_blank">Andreas Rienow</a> and <a href="https://orcid.org/0009-0000-3924-5230" target="_blank">Lars Tum</a>, which is licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" style="color: black !important; text-decoration: underline !important;">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>. This edited content is licensed under <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" style="color: black !important; text-decoration: underline !important;">CC BY-NC-SA 4.0</a>.
</p>
</footer>
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