Checklist

Please go through each entry in the below checklist and mark an 'X' if that condition has been met. Every entry should be marked with an 'X' to be get the Pull Request approved.

[ ] All imports are in the first cell?
- [ ] First block of imports are standard libraries
- [ ] Second block are 3rd party libraries
- [ ] Third block are all arcgis imports? Note that in some cases, for samples, it is a good idea to keep the imports next to where they are used, particularly for uncommonly used features that we want to highlight.
[ ] All GIS object instantiations are one of the following?
- gis = GIS()
- gis = GIS('home') or gis = GIS('pro')
- gis = GIS(profile="your_online_portal")
- gis = GIS(profile="your_enterprise_portal")
[ ] If this notebook requires setup or teardown, did you add the appropriate code to ./misc/setup.py and/or ./misc/teardown.py?
[ ] If this notebook references any portal items that need to be staged on AGOL/Python API playground, did you coordinate with a Python API team member to stage the item the correct way with the api_data_owner user?
[ ] If the notebook requires working with local data (such as CSV, FGDB, SHP, Raster files), upload the files as items to the Geosaurus Online Org using api_data_owner account and change the notebook to first download and unpack the files.
[ ] Code simplified & split out across multiple cells, useful comments?
[ ] Consistent voice/tense/narrative style? Thoroughly checked for typos?
[ ] All images used like <img src="base64str_here"> instead of <img src="https://some.url">? All map widgets contain a static image preview? (Call mapview_inst.take_screenshot() to do so)
[ ] All file paths are constructed in an OS-agnostic fashion with os.path.join()? (Instead of r"\foo\bar", os.path.join(os.path.sep, "foo", "bar"), etc.)
[x] Is your code formatted using Jupyter Black? You can use Jupyter Black to format your code in the notebook.
[x] IF YOU WANT THIS SAMPLE TO BE DISPLAYED ON THE DEVELOPERS.ARCGIS.COM WEBSITE, ping @ mohi9282 so he can add it to the list for the next deploy

Sep 14 '22 11:09 sdash77

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Sep 14 '22 11:09 review-notebook-app[bot]

Hi @sdash77 @anushriyaj

I built this branch locally to preview - it looks great but just needs @anushriyaj to review for technical content
The title text might need adjusted for capitalization:

Nov 07 '22 21:11 jyaistMap

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priyankatuteja commented on 2022-12-27T04:36:11Z ----------------------------------------------------------------

Change the notebook file name to : cloud-removal-using-deep-learning-and-raster-function

Change notebook heading to: Cloud removal using pretrained deep learning model and raster function

Dec 27 '22 04:12 review-notebook-app[bot]

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priyankatuteja commented on 2022-12-27T04:36:12Z ----------------------------------------------------------------

please note that passwords are not expected to be exposed

Dec 27 '22 04:12 review-notebook-app[bot]

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priyankatuteja commented on 2022-12-27T04:36:12Z ----------------------------------------------------------------

raster and layer package must be published on python api playground portal using api data owner account. FYI deldevs is not an enterprise portal. It's an Online org.

Dec 27 '22 04:12 review-notebook-app[bot]

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priyankatuteja commented on 2022-12-27T04:36:13Z ----------------------------------------------------------------

do not keep hyphen in the word pretrained

Dec 27 '22 04:12 review-notebook-app[bot]

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BP-Ent commented on 2022-12-28T18:51:55Z ----------------------------------------------------------------

Clouds often create obstructions in satellite imagery that can hinder results in processes like land cover classification and time series analysis. In this notebook, we will detect and replace cloud contaminated regions of satellite imagery by combining a pre-trained deep learning model, available in living atlas, and an image processing technique, available in raster functions.