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Spatial Thoughts
India
เข้าร่วมเมื่อ 2 ม.ค. 2020
Spatial Thoughts's mission is to enable everyone to learn and master modern geospatial technologies. This channel contains full length courses, tutorials, tips, interviews and lectures to help learners everywhere. All our videos are ad-free so you can learn without distractions.
Introduction and Course Overview - End-to-End GEE
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html
View the presentation at docs.google.com/presentation/d/1q8HRDTqgQEp3Hmi8IG0T7djPLTC1wRig3jXrwFTmoVE/edit?usp=sharing
Video Content:
00:00 Introductions
02:11 Introduction to Google Earth Engine
10:28 Course Overview
13:00 Javascript vs. Python
Note: Certification and Support are only available for participants in our paid instructor-led classes.
View the presentation at docs.google.com/presentation/d/1q8HRDTqgQEp3Hmi8IG0T7djPLTC1wRig3jXrwFTmoVE/edit?usp=sharing
Video Content:
00:00 Introductions
02:11 Introduction to Google Earth Engine
10:28 Course Overview
13:00 Javascript vs. Python
Note: Certification and Support are only available for participants in our paid instructor-led classes.
มุมมอง: 1 641
วีดีโอ
Module 1: Earth Engine Basics - End-to-End GEE
มุมมอง 70219 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html See the Module 1 content at courses.spatialthoughts.com/end-to-end-gee.html#module-1-earth-engine-basics Video Contents: 00:00 Hello World 21:06 ImageCollections 31:40 Filtering ImageCollections 48:27 Mosaics and Composites 1:05:02 FeatureCollection...
Module 2: Earth Engine Intermediate - End-to-End GEE
มุมมอง 31419 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html View the Presentation on Map/Reduce Programming Concepts at docs.google.com/presentation/d/10qOyxhubkwnsAVjniW54ETgwUHq3DXYKo3HGb6Gemi0/edit?usp=sharing See the Module 2 content at courses.spatialthoughts.com/end-to-end-gee.html#module-2-earth-engin...
Module 3: Machine Learning and Supervised Classification - End-to-End GEE
มุมมอง 34219 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html View the Presentation at docs.google.com/presentation/d/19L1b5vsxb38xS8GlHNKOjvPZ0IGqDhv93681btMEL5w/edit?usp=sharing See the Module 3 content at courses.spatialthoughts.com/end-to-end-gee.html#module-3-supervised-classification Video Contents: 00:0...
Module 4: Change Detection - End-to-End GEE
มุมมอง 16119 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html View the Presentation at docs.google.com/presentation/d/1vdFTWJ61yDuVfbfhpnumQ8zuMPGwGcHpHsBTRgo_o5I/edit?usp=sharing See the Module 4 content at courses.spatialthoughts.com/end-to-end-gee.html#module-4-change-detection Video Contents: 00:00:00 Intr...
Module 5: Earth Engine Apps - End-to-End GEE
มุมมอง 25719 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html View the Presentation at docs.google.com/presentation/d/1u4Q91OqT9_OS4m1OWMm3uRUgu_oseqDUxHV-8mpzGz4/edit?usp=sharing See the Module 5 content at courses.spatialthoughts.com/end-to-end-gee.html#module-5-earth-engine-apps Video Contents: 00:00:00 Int...
Module 6: Google Earth Engine Python API - End-to-End GEE
มุมมอง 15419 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html View the Presentation at docs.google.com/presentation/d/1hPVRnxp2Vp1VHXBtu36SH_UtEOjPz70KcDV-zGIin3U/edit?usp=sharing See the Module 6 content at courses.spatialthoughts.com/end-to-end-gee.html#module-6-google-earth-engine-python-api Video Contents:...
Guided Projects and Learning Resources - End-to-End GEE
มุมมอง 12619 ชั่วโมงที่ผ่านมา
This video is part of our End-to-End Google Earth Engine course. Access the full course material at courses.spatialthoughts.com/end-to-end-gee.html See the guided projects at courses.spatialthoughts.com/end-to-end-gee.html#guided-projects Note: Certification and Support are only available for participants in our paid instructor-led classes.
Locating A New Bicycle Parking Station using Multicriteria Overlay Analysis - Advanced QGIS
มุมมอง 468วันที่ผ่านมา
This video is part of our Advanced QGIS course. Access the full course material at courses.spatialthoughts.com/advanced-qgis.html Download the GeoPackage containing the dataset and model at drive.google.com/uc?export=download&id=1uRvug3DV9eEffbSvRNE82e2JjnY0i9tG Learn about Multicriteria Overlay Analysis in our step-by-step tutorial at www.qgistutorials.com/en/docs/3/multi_criteria_overlay.html...
Creating Animations with ImageMagick - Advanced QGIS
มุมมอง 179วันที่ผ่านมา
This video is part of our Advanced QGIS course. Access the full course material at courses.spatialthoughts.com/advanced-qgis.html Access the content covered in the video at courses.spatialthoughts.com/advanced-qgis.html#animation-using-imagemagick Note: Certification and Support are only available for participants in our paid instructor-led classes.
Publishing Apps with Streamlit Cloud - Mapping and Data Visualization with Python
มุมมอง 25221 วันที่ผ่านมา
This video is part of our Mapping and Data Visualization with Python course. Access the full course material at courses.spatialthoughts.com/python-dataviz.html Access the course material for publishing apps: courses.spatialthoughts.com/python-dataviz.html#publishing-apps-with-streamlit-cloud Note: Certification and Support are only available for participants in our paid instructor-led classes.
Building Mapping Apps with Leafmap and Streamlit - Mapping and Data Visualization with Python
มุมมอง 31221 วันที่ผ่านมา
This video is part of our Mapping and Data Visualization with Python course. Access the full course material at courses.spatialthoughts.com/python-dataviz.html View the App Demo: mapping-dashboard.streamlit.app/ Access the course material for creating the app: courses.spatialthoughts.com/python-dataviz.html#create-a-mapping-dashboard Note: Certification and Support are only available for partic...
Building a Simple Geocoder App with Streamlit - Mapping and Data Visualization with Python
มุมมอง 25221 วันที่ผ่านมา
This video is part of our Mapping and Data Visualization with Python course. Access the full course material at courses.spatialthoughts.com/python-dataviz.html View the Demo App: simplegeocoder.streamlit.app/ Access the course material for creating the app: courses.spatialthoughts.com/python-dataviz.html#create-a-simple-geocoder-app Note: Certification and Support are only available for partici...
Building a Simple Dashboard App with Streamlit - Mapping and Data Visualization with Python
มุมมอง 40928 วันที่ผ่านมา
This video is part of our Mapping and Data Visualization with Python course. Access the full course material at courses.spatialthoughts.com/python-dataviz.html See the demo app: simpledashboard.streamlit.app/ Access the course material for the app: courses.spatialthoughts.com/python-dataviz.html#streamlit-basics Note: Certification and Support are only available for participants in our paid ins...
Streamlit Basics - Mapping and Data Visualization with Python
มุมมอง 40228 วันที่ผ่านมา
This video is part of our Mapping and Data Visualization with Python course. Access the full course material at courses.spatialthoughts.com/python-dataviz.html Access the Presentation shown in the video at docs.google.com/presentation/d/1aHJPScvjx4ioGkBUSBm2od8FoxKwARdgBJiJ2TSfySs/edit?usp=sharing Note: Certification and Support are only available for participants in our paid instructor-led cla...
Visualizing Large Vector Datasets with Lonboard - Mapping and Data Visualization with Python
มุมมอง 336หลายเดือนก่อน
Visualizing Large Vector Datasets with Lonboard - Mapping and Data Visualization with Python
Downloading and Visualizing OSM Data with LeafMap - Mapping and Data Visualization with Python
มุมมอง 241หลายเดือนก่อน
Downloading and Visualizing OSM Data with LeafMap - Mapping and Data Visualization with Python
Leafmap Basics - Mapping and Data Visualization with Python
มุมมอง 450หลายเดือนก่อน
Leafmap Basics - Mapping and Data Visualization with Python
Multi-layer Interactive Maps - Mapping and Data Visualization with Python
มุมมอง 333หลายเดือนก่อน
Multi-layer Interactive Maps - Mapping and Data Visualization with Python
Interactive Maps with Folium - Mapping and Data Visualization with Python
มุมมอง 555หลายเดือนก่อน
Interactive Maps with Folium - Mapping and Data Visualization with Python
Assignment (Creating a Colorized River Basin Map) - Mapping and Data Visualization with Python
มุมมอง 401หลายเดือนก่อน
Assignment (Creating a Colorized River Basin Map) - Mapping and Data Visualization with Python
Visualizing Rasters - Mapping and Data Visualization with Python
มุมมอง 339หลายเดือนก่อน
Visualizing Rasters - Mapping and Data Visualization with Python
Creating a Globe Visualization - Mapping and Data Visualization with Python
มุมมอง 290หลายเดือนก่อน
Creating a Globe Visualization - Mapping and Data Visualization with Python
Mapping Gridded Datasets - Mapping and Data Visualization with Python
มุมมอง 304หลายเดือนก่อน
Mapping Gridded Datasets - Mapping and Data Visualization with Python
Visualizing Monthly Median Composites with XArray - Mapping and Data Visualization with Python
มุมมอง 306หลายเดือนก่อน
Visualizing Monthly Median Composites with XArray - Mapping and Data Visualization with Python
XArray Basics - Mapping and Data Visualization with Python
มุมมอง 457หลายเดือนก่อน
XArray Basics - Mapping and Data Visualization with Python
Introduction to XArray - Mapping and Data Visualization with Python
มุมมอง 501หลายเดือนก่อน
Introduction to XArray - Mapping and Data Visualization with Python
Using Basemaps - Mapping and Data Visualization with Python
มุมมอง 664หลายเดือนก่อน
Using Basemaps - Mapping and Data Visualization with Python
Creating Maps - Mapping and Data Visualization with Python
มุมมอง 944หลายเดือนก่อน
Creating Maps - Mapping and Data Visualization with Python
Using Matplotlib Themes - Mapping and Data Visualization with Python
มุมมอง 623หลายเดือนก่อน
Using Matplotlib Themes - Mapping and Data Visualization with Python
The map projections comic s pretty funny if you get a chance. Turns out I am guilty of Winkel-Tripel..maybe because as a kid my parents used to get National Geographic.
Yes. I love xkcd too!
is it possible to export images for different years separately?
Yes. You can export eaxh image in the image collection. See reference script at courses.spatialthoughts.com/end-to-end-gee-supplement.html#exporting-imagecollections
This course is amazing. Well structured and easy to understand. Thank you
Thank you so much for the tutorial, great information and presentation. Thank you for your generosity.
tell us how to find these data MODIS/006/MOD09Q1 in GEE
It is now available as version 6.1 at developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1
Amazing!
My GEE plugin always error. It can't execute my project in GEE cloud.
This is a common problem due to problem with authentication. Follow these steps - Make sure you have connected your GEE account to a cloud project. If not, follow our instructions courses.spatialthoughts.com/gee-sign-up.html - Setup the GEE Python API and complete authentication. courses.spatialthoughts.com/install-gee-python-api.html Once you complete this, the plugin with work fine. If you still get error, try with a new profile th-cam.com/video/i6L84WMVka4/w-d-xo.html
# Define the bounding box for San Francisco lat_min, lat_max = 37.71, 37.82 lon_min, lon_max = -122.53, -122.36 fig,ax = plt.subplots(figsize = (8,8)) tracts.plot(ax = ax, edgecolor = 'black', facecolor = 'none', linewidth = 0.5) # Optionally, set the axis limits to focus on the San Francisco area ax.set_xlim(lon_min, lon_max) ax.set_ylim(lat_min, lat_max) this is the code for this exercise
Thank you for everything❤
please could you sent your email wan to contact you have some question
❤
❤
thank you for making quality material.
Top please active translation
Done.
If you were master , you would better to creat pie chart for your land cover for export
This is part of the course material courses.spatialthoughts.com/end-to-end-gee-supplement.html#calculating-area-by-class
thank you sir❤
Very informative.. Thank u sir
I really found it comprehensive for basic of RS! I had an entire semester course and got more from this one hour course than my course. Thank you for your time and sharing with us.
Give me code VCI ,TCI,VHI
Great content sir. Simple to understand and very crisp videos. Have subscribed to your channel
Thank you for sharing this
Adding scale to the map is a problem. When one zooms in on a region, the scale can be displayed but for the world as a whole the scale does not show (error). How do we fix this problem?
Recommended for Flood Mapping ✅. Covers almost all of the issues in flood mapping.
When I use the clean command to remove topology errors, the errors like gaps are resolved, but some features are also removed. I want the errors (gaps) to be filled, and all polygons to be retained. What should I do to achieve this?
Do those features have empty geometries? That maybe the cause of the removal. -allow-empty will prevent their removal. See github.com/mbloch/mapshaper/wiki/Command-Reference#-clean
What is the input type for QML Style input?
It is a 'File/Folder' input.
Do you know difference between Index formula and regression formula for calculating pollution? ❤😂
I faced an issue recently when I downloaded the vector population data and added it to qgis over my study area shape file they were also mismatched. Is it possible to solve this issue ?
The 'mismatch' you refer to could be due to bad georeferencing, datasets digitized at different scales, or data from different time periods. Conflating different datasets like this is a hard problem and there are no automated solutions.
@SpatialThoughts I used sample data from a reputed site and country shape files from GitHub and realised this issue.
so helpful
Dear Ujaval, again I would like to sincerely appreciate the effort put in by the both of you in each of these topics and exercises. It is pretty well explained and never did I find any bottleneck in solving the exercises. Loads of thanks!
Thank you so much for your positive feedback!
In the tutorial, you identified the layers in the file with Qgis. Is there a way to identify all the layers in a gpkg file using python? Thank you.
import fiona layers = fiona.listlayers(path) print(layers)
@@SpatialThoughts Thank you very much.
Thank you
Hello. I want in join to group of GEE Do you know group??
groups.google.com/forum/#!forum/google-earth-engine-developers
Thank you. This learning is very helpful and informative. Awesome!
How to remove deploy, github and 3 dots while deploying streamlit app
When you are signed in to your account, you will see additional menus for app management. Check the app in incognito tab. Certain links like github are there in all apps hosted on streamlit cloud.
Can anyone post the code for this exercise please? I don't know what I'm doing wrong but my output is different than what should be.
All the solutions are availablein thus repository for you to compare your answer and learn github.com/spatialthoughts/courses/tree/master/code/python_dataviz/solutions
i am also stuck and hoping someone can share their solution
See the solution at the end of this notebook colab.research.google.com/github/spatialthoughts/courses/blob/master/code/python_dataviz/solutions/04_using_basemaps.ipynb
classification_kwds = {"bins": [0, 0.25, 0.5, 0.75, 1.0]} # Plot with color based on 'Obscur' column using 'User_Defined' classification penumbra_reprojected.plot( ax=ax, column='Obscur', # This uses the values in 'Obscur' for coloring cmap='Grays', # A colormap for the colors edgecolor='black', # Black edge color legend=False, # Show legend for classification classification_kwds=classification_kwds, # Apply classification with custom bins alpha=0.1 # Transparency level (optional) )
There are minor mistakes in the video like at 17:29 when it says f.setProgress(progress); that is not possible. By looking at the documentation it gets clarified. It is feedback.setProgress(progress). Similarly, at 15:38 save_options were also not defined. Since the document is super descriptive everything is pretty explained.
Thanks for reporting. You are correct. I was coding from memory and there are some bugs. As you mention - the course material has the correct and working code so please use that instead.
Thank you so much for these videos and resources, they have been most helpful. The topics are also very well taught, detailed, and relevant.
Thank you so much for these videos and resources, they have been most helpful. The topics are also very well taught, detailed, and relevant.
Thank you for making this public!
thank you sir! your toturials are very helpfull.
Can we shape area and length into lat long. Plz help
Your question is not clear. For general questions not pertaining to the topic of the video - it is best to ask them on gis.stackexchange.com/
Hi, thanks for sharing this! Is it possible to filter down for a state with the same dataset?
No. This is a country level dataset. For states, get the Admin1 dataset from Natural Earth, GADM or GeoBoundaries.
if i want to make dashboard in webpage and make online GIS for display then will this work?
Since it is supported by leafmap, I think you can use it on a streamlit app. You can can try it once you learn about streamlit on the next few videos.
My solution: aggregate( layer:=@layer, aggregate:='count', expression:="fid", filter:=touches($geometry, geometry(@parent)) )
print(latitude[:2]) print(latitude [4:6]) print(latitude [-8:-1])
or easier: print(latitude[:2] + ' ' + latitude[4:6] + ' ' + latitude[-8:-1])
this was a cool assignment, i used pypalettes and ended up with some cool graphics.
Cool 👍. Pypalettes is quite nice.
Really very helpful. Thank you very much.
thank you for your toturial all videos are very helpfull.
Hi great course, can you provide answers to exercises? it would help a lot to check our answers as I cannot seem to reproduce some details in the given maps/figures
You can see the model solutions to exercises at github.com/spatialthoughts/courses/tree/master/code/python_dataviz/solutions
Horaaaaaa 🎉 google colab