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update item ids and urls in samples
1 parent 3a9663c commit 155dbda

15 files changed

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land_parcel_extraction_using_edge_detection_deep_learning_model.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"training_area_raster = gis.content.get('8202ffe4fcaf4ba9bfefe2154f98e7b8')\n",
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"training_area_raster = gis.content.get('4f9d56b58e354aac9ed043355a4fa870')\n",
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"training_area_raster"
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]
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"test_area_raster = gis.content.get('481f2ba520274f0fabfe470d2c8def39')\n",
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"test_area_raster = gis.content.get('ed2951a19296485d9334460f343c5a0c')\n",
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"test_area_raster"
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]
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},

samples/04_gis_analysts_data_scientists/calculate_impervious_surfaces_from_spectral_imagery.ipynb

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"text/html": [
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"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
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" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=eb9fe459cc26486eb4a733b2a2def44f' target='_blank'>\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=5cfa05015d2c43b4a00da529eb0e5411' target='_blank'>\n",
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" <img src='https://pythonapi.playground.esri.com/portal/portalimages/desktopapp.png' class=\"itemThumbnail\">\n",
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" </a>\n",
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" </div>\n",
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"\n",
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" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=eb9fe459cc26486eb4a733b2a2def44f' target='_blank'><b>RFT_Reclassify_RasterFunction</b>\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=5cfa05015d2c43b4a00da529eb0e5411' target='_blank'><b>RFT_Reclassify_RasterFunction</b>\n",
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" </a>\n",
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" <br/>RFT_Reclassify_RasterFunction<img src='https://pythonapi.playground.esri.com/portal/home/js/jsapi/esri/css/images/item_type_icons/layers16.png' style=\"vertical-align:middle;\">Raster function template by api_data_owner\n",
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" <br/>Last Modified: June 07, 2021\n",
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}
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],
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"source": [
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"rft_to_reclassify = gis.content.get('eb9fe459cc26486eb4a733b2a2def44f')\n",
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"rft_to_reclassify = gis.content.get('5cfa05015d2c43b4a00da529eb0e5411')\n",
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"rft_to_reclassify"
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]
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},
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"text/html": [
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"<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
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" <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=7e00802c927a40cbbdc36cefdb2928e4' target='_blank'>\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=b6ea95a68bdd415f85296fefa2eeef6c' target='_blank'>\n",
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" <img src='https://pythonapi.playground.esri.com/portal/portalimages/desktopapp.png' class=\"itemThumbnail\">\n",
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" </a>\n",
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" </div>\n",
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"\n",
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" <div class=\"item_right\" style=\"float: none; width: auto; overflow: hidden;\">\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=7e00802c927a40cbbdc36cefdb2928e4' target='_blank'><b>Land Cover of Kent County Delaware</b>\n",
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" <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=b6ea95a68bdd415f85296fefa2eeef6c' target='_blank'><b>Land Cover of Kent County Delaware</b>\n",
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" </a>\n",
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" <br/>KENT_10001_us8bit_tif<img src='https://pythonapi.playground.esri.com/portal/home/js/jsapi/esri/css/images/item_type_icons/imagery16.png' style=\"vertical-align:middle;\">Imagery Layer by api_data_owner\n",
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" <br/>Last Modified: June 08, 2021\n",
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}
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],
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"source": [
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"classified_raster = gis.content.get('7e00802c927a40cbbdc36cefdb2928e4')\n",
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"classified_raster = gis.content.get('b6ea95a68bdd415f85296fefa2eeef6c')\n",
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"classified_raster"
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]
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},

samples/04_gis_analysts_data_scientists/calculate_post_fire_landslide_risk.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.0"
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"version": "3.11.8"
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},
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"toc": {
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"base_numbering": 1,

samples/04_gis_analysts_data_scientists/detecting_swimming_pools_using_satellite_image_and_deep_learning.ipynb

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samples/04_gis_analysts_data_scientists/extracting_features_and_land_use_land_cover_using_panoptic_segmentation.ipynb

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samples/04_gis_analysts_data_scientists/feature_categorization_using_satellite_imagery_and_deep_learning.ipynb

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samples/04_gis_analysts_data_scientists/flood_inundation_mapping_using_sar_data_and_deep_learning.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"raster_for_inferencing = gis.content.get('c6abe978dc854c20bf0664f6f7b43290')\n",
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"raster_for_inferencing = gis.content.get('05a5c9ffd2044d2583ec1ae2a5712d54')\n",
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"raster_for_inferencing"
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]
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},

samples/04_gis_analysts_data_scientists/generating_rgb_imagery_from_digital_surface_model_using_pix2pix.ipynb

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samples/04_gis_analysts_data_scientists/land_cover_classification_using_unet.ipynb

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samples/04_gis_analysts_data_scientists/landcover_classification_using_hyperspectral_imagery_and_deep_learning.ipynb

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