{"id":1185422,"date":"2021-04-07T10:03:29","date_gmt":"2021-04-07T17:03:29","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1185422"},"modified":"2021-04-13T11:48:32","modified_gmt":"2021-04-13T18:48:32","slug":"devsummit2021-ai-infused-solutions-and-apps-in-arcgis","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit2021-ai-infused-solutions-and-apps-in-arcgis","title":{"rendered":"Dev Summit 2021: AI infused solutions and apps in ArcGIS"},"author":7461,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[174212,32631,186132,759812],"industry":[],"product":[36841,36621,36691],"class_list":["post-1185422","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-artificial-intelligence","tag-community-engagement","tag-deep-learning","tag-dev-summit-2021-demo","product-api-python","product-appstudio","product-survey123"],"acf":{"short_description":"Nick O\u2019Day shows us how deep learning capabilities of ArcGIS with ArcGIS AppStudio helped them build an AI driven mobile app to map local trees","flexible_content":[{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">In this demonstration, Nick O\u2019Day from the City of John\u2019s Creek, GA shows us <\/span><span data-contrast=\"auto\">how the deep learning capabilities of ArcGIS helped them with their <\/span><span data-contrast=\"auto\">goal of building a comprehensive tree inventory. They w<\/span><span data-contrast=\"auto\">anted to build a smart phone application that would allow the citizens of John\u2019s Creek <\/span><span data-contrast=\"auto\">to map trees as points as well as <\/span><span data-contrast=\"auto\">to <\/span><span data-contrast=\"auto\">classify them.<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185462,"id":1185462,"title":"aiapps1-small","filename":"aiapps1-small.png","filesize":58330,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit2021-ai-infused-solutions-and-apps-in-arcgis\/aiapps1-small","alt":"","author":"7461","description":"","caption":"A smart phone app that allows citizens to map as well as classify trees","name":"aiapps1-small","status":"inherit","uploaded_to":1185422,"date":"2021-04-07 04:47:15","modified":"2021-04-07 04:48:04","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1086,"height":610,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","medium_large-width":768,"medium_large-height":431,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","large-width":1086,"large-height":610,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","1536x1536-width":1086,"1536x1536-height":610,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","2048x2048-width":1086,"2048x2048-height":610,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small-826x465.png","card_image-width":826,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps1-small.png","wide_image-width":1086,"wide_image-height":610}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"youtube","start_time":"0","end_time":"","youtube_video_url":"<iframe title=\"AI User Story: AI on the Edge by City of Johns Creek, GA\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/Mwi5geN05uQ?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>"},{"acf_fc_layout":"content","content":"<p>&nbsp;<\/p>\n<p>T<span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">o begin with, they installed ArcGIS Pro along with the <\/span><\/span><a class=\"Hyperlink BCX2 SCXW105938394\" href=\"https:\/\/github.com\/esri\/deep-learning-frameworks\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun BCX2 SCXW105938394\" data-ccp-charstyle=\"Hyperlink\">deep learning frameworks<\/span><\/span><\/a><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> (including the ArcGIS API for Python, the <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">F<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">eature<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">C<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">lass<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">i<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">fier<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">, <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">TensorFlow, Fast.ai, <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW105938394\">PyTorch<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">)<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">.<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> Usi<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">ng the information curated by the organization <\/span><\/span><a class=\"Hyperlink BCX2 SCXW105938394\" href=\"https:\/\/www.treesatlanta.org\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun BCX2 SCXW105938394\" data-ccp-charstyle=\"Hyperlink\">Trees Atlanta<\/span><\/span><\/a><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> they could fetch a list of the top 50 tree species specific to their area and found half their training samples from <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">the <\/span><\/span><a class=\"Hyperlink BCX2 SCXW105938394\" href=\"https:\/\/www.imageclef.org\/lifeclef\/2017\/plant\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun BCX2 SCXW105938394\" data-ccp-charstyle=\"Hyperlink\">PlantCLEF dataset<\/span><\/span><\/a><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">.<\/span><\/span> <span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">After curating the rest of the data, they implemented a key step in ensuring that they had <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">an equal number of image chips (100) for each class (tree species) so that they wouldn\u2019t under or over train on any single class, <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">preventing <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">the model <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">from<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> mak<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">ing<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> errors in identifying and classifying a <\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">tree sample<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\"> correctly<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">.\u00a0<\/span><\/span><span class=\"TextRun BCX2 SCXW105938394\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW105938394\">\u00a0<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185472,"id":1185472,"title":"aiapps2","filename":"aiapps2.png","filesize":226521,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit2021-ai-infused-solutions-and-apps-in-arcgis\/aiapps2","alt":"ai-apps2","author":"7461","description":"","caption":"Feature classification using arcgis.learn","name":"aiapps2","status":"inherit","uploaded_to":1185422,"date":"2021-04-07 04:49:25","modified":"2021-04-07 04:50:22","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1920,"height":1080,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","medium_large-width":768,"medium_large-height":432,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","large-width":1920,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2-1536x864.png","1536x1536-width":1536,"1536x1536-height":864,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","2048x2048-width":1920,"2048x2048-height":1080,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2-826x465.png","card_image-width":826,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps2.png","wide_image-width":1920,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">Having collected<\/span><span data-contrast=\"auto\"> their training data, the<\/span><span data-contrast=\"auto\">ir next step was <\/span><span data-contrast=\"auto\">to train their model using the <\/span><b><span data-contrast=\"auto\">Jupyter<\/span><\/b><b><span data-contrast=\"auto\"> Notebooks environment within ArcGIS Pro<\/span><\/b><span data-contrast=\"auto\">.<\/span><span data-contrast=\"auto\"> In order to prepare the data for the model, they specified a fixed batch and chip size and<\/span><span data-contrast=\"auto\"> fed it to the <\/span><b><span data-contrast=\"auto\">FeatureClassifier<\/span><\/b> <span data-contrast=\"auto\">model<\/span><span data-contrast=\"auto\"> available in <\/span><b><span data-contrast=\"auto\">arcgis.learn<\/span><\/b><span data-contrast=\"auto\">.<\/span><span data-contrast=\"auto\"> Training their model across 50 classes (tree species<\/span><span data-contrast=\"auto\">)<\/span><span data-contrast=\"auto\"> and 100 samples for each, took about 6 hours and <\/span><span data-contrast=\"auto\">once satisfied with its performance, <\/span><span data-contrast=\"auto\">they saved the <\/span><span data-contrast=\"auto\">model <\/span><span data-contrast=\"auto\">as a <\/span><span data-contrast=\"auto\">tflit<\/span><span data-contrast=\"auto\">e<\/span><span data-contrast=\"auto\"> file<\/span><span data-contrast=\"auto\">.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185482,"id":1185482,"title":"aiapps3","filename":"aiapps3.png","filesize":358275,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit2021-ai-infused-solutions-and-apps-in-arcgis\/aiapps3","alt":"ai-apps3","author":"7461","description":"","caption":"Survey built using the smart camera template within Survey123 Connect","name":"aiapps3","status":"inherit","uploaded_to":1185422,"date":"2021-04-07 04:52:13","modified":"2021-04-07 04:53:34","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1920,"height":1080,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","medium_large-width":768,"medium_large-height":432,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","large-width":1920,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3-1536x864.png","1536x1536-width":1536,"1536x1536-height":864,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","2048x2048-width":1920,"2048x2048-height":1080,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3-826x465.png","card_image-width":826,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/aiapps3.png","wide_image-width":1920,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>A<span data-contrast=\"auto\">s a final step, they used <\/span><b><span data-contrast=\"auto\">Survey123 Connect <\/span><\/b><span data-contrast=\"auto\">and <\/span><b><span data-contrast=\"auto\">App<\/span><\/b><b><span data-contrast=\"auto\">Studio<\/span><\/b> <span data-contrast=\"auto\">to package this as <\/span><span data-contrast=\"auto\">both Android and iOS<\/span> <span data-contrast=\"auto\">mobile app<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\"> for the use of their citizens. They built a custom survey using the smart camera template within Survey123 <\/span><span data-contrast=\"auto\">Connect and uploaded it to <\/span><b><span data-contrast=\"auto\">ArcGIS Online. <\/span><\/b><span data-contrast=\"auto\">They used the Survey123 template app within <\/span><span data-contrast=\"auto\">AppStudio<\/span><span data-contrast=\"auto\"> to <\/span><span data-contrast=\"auto\">build an app that would use the survey that they had created as input. This resulted in lightweight, deep learning enabled <\/span><span data-contrast=\"auto\">smart phone application<\/span><span data-contrast=\"auto\">s<\/span><span data-contrast=\"auto\">,<\/span><span data-contrast=\"auto\"> enabling the citizens of John\u2019s Creek to <\/span><span data-contrast=\"auto\">collect both tree locations and their species.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<p>App development frameworks like Survey123 and AppStudio allow you to create custom apps and infusing these apps with AI enables you to take AI to the edge and solve problems in innovative ways. This has been the approach that we at Esri are taking with our Solutions as well. For instance, the\u00a0<a href=\"https:\/\/doc.arcgis.com\/en\/arcgis-solutions\/reference\/introduction-to-3d-basemaps.htm\">3D\u00a0basemaps\u00a0solution\u00a0<\/a>has been enhanced\u00a0to use the\u00a0tree\u00a0point classification model\u00a0to\u00a0extract trees from\u00a0lidar data. This\u00a0helps\u00a0users\u00a0create\u00a0more realistic\u00a03D\u00a0basemaps\u00a0for\u00a0their\u00a0community.<\/p>\n<p><b><span data-contrast=\"auto\">Additional Resources:<\/span><\/b><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><a href=\"https:\/\/developers.arcgis.com\/python\/sample-notebooks\/train-a-tensorflow-lite-model-for-identifying-plant-species\/\">Example notebook for Plant species identification using a Tensorflow-Lite model within mobile devices<\/a><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><a href=\"https:\/\/community.esri.com\/t5\/3d-blog\/deep-learning-in-3d-basemaps-solution\/ba-p\/1000938\"><span data-contrast=\"none\">Deep learning in 3D <\/span><span data-contrast=\"none\">basemaps<\/span><\/a><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><a href=\"https:\/\/doc.arcgis.com\/en\/arcgis-solutions\/reference\/introduction-to-3d-basemaps.htm\"><span data-contrast=\"none\">Documentation for introduction to 3D <\/span><span data-contrast=\"none\">Basemaps<\/span><\/a><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n"}],"authors":[{"ID":7461,"user_firstname":"Manushi","user_lastname":"Majumdar","nickname":"Manushi Majumdar","user_nicename":"mmajumdar_dcdev","display_name":"Manushi Majumdar","user_email":"MMajumdar@esri.com","user_url":"","user_registered":"2018-03-21 18:21:20","user_description":"Product Engineer - Applied Data Science with ArcGIS API for Python. 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