{"id":1185082,"date":"2021-04-07T09:45:54","date_gmt":"2021-04-07T16:45:54","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1185082"},"modified":"2021-04-14T07:51:44","modified_gmt":"2021-04-14T14:51:44","slug":"devsummit-2021-kuwaits-paci-using-arcgis-learn","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit-2021-kuwaits-paci-using-arcgis-learn","title":{"rendered":"Dev Summit 2021: Kuwait&#8217;s PACI using arcgis.learn"},"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":[186132,759812,35661,24341],"industry":[],"product":[36841,36561],"class_list":["post-1185082","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-deep-learning","tag-dev-summit-2021-demo","tag-machine-learning","tag-python","product-api-python","product-arcgis-pro"],"acf":{"short_description":"Maher Abdel Karim from PACI shows how they used an AI model to keep their GIS data (roads, buildings, parking lots) updated using arcgis.learn.","flexible_content":[{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">In this demonstration, <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">Maher Abdel Karim from The Public Authority for Civil Information (PACI) in Kuwait<\/span><\/span> <span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun CommentStart SCXW181413920 BCX2\">showed <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">us how PACI used machine learning and deep learning <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">to modernize their GIS data, <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">to<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> support the Kuwait Vision 2035. <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">Kuwait<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> is focusing on major infrastructu<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">re growth as part of the Kuwait Vision 2035, by implementing huge infrastructure projects <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">across the country. <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">PACI is responsible for recording data about people, addresses and businesses<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> and have created a comprehensive <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 SCXW181413920 BCX2\">basemap<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> for<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> the<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> whole State of Kuwait<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">.<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> The rapid infrastructure growth makes it challenging<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> for PACI<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> to keep<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> t<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">h<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">e<\/span><\/span> <span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 SCXW181413920 BCX2\">basemap<\/span><\/span> <span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">up to date<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> as it<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\"> is used by more than 170 different organizations<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">.<\/span><\/span> <span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">Their goal was to automate updates to their <\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 SCXW181413920 BCX2\">basemap<\/span><\/span><span class=\"TextRun SCXW181413920 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW181413920 BCX2\">.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185122,"id":1185122,"title":"paci-1-small","filename":"paci-1-small.png","filesize":981795,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit-2021-kuwaits-paci-using-arcgis-learn\/paci-1-small","alt":"paci-basemap","author":"7461","description":"","caption":"Airbus 2018 baseline image","name":"paci-1-small","status":"inherit","uploaded_to":1185082,"date":"2021-04-07 03:09:30","modified":"2021-04-07 03:10:37","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":1092,"height":465,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","medium-width":464,"medium-height":198,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","medium_large-width":768,"medium_large-height":327,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","large-width":1092,"large-height":465,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","1536x1536-width":1092,"1536x1536-height":465,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","2048x2048-width":1092,"2048x2048-height":465,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small-826x352.png","card_image-width":826,"card_image-height":352,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-1-small.png","wide_image-width":1092,"wide_image-height":465}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"youtube","start_time":"0","end_time":"","youtube_video_url":"<iframe title=\"AI User Story: Road Extraction by PACI\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/pkL2PU60hTo?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><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">In order to do this, PACI used data from <\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">their app, <\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">Kuwait Finder<\/span><\/span> <span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">and satellite imagery.<\/span><\/span> <span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">Kuwait Finder<\/span><\/span> <span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">is a GIS powered search application w<\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">hich serves more than 1.2 million users, including citizens and government officials. <\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">They used GPS logs from Kuwait Finder and satellite imagery to find gaps in their street network and GIS data<\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">.<\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\"> As we see <\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">above<\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">, they <\/span><\/span><span class=\"TextRun BCX2 SCXW260045326\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW260045326\">selected Airbus 2018 <span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">image as baseline and created the ground truth dat<\/span><\/span><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">a<\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\"> covering three different classes \u2013 building footprints, <\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">s<\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">treets and parking lots. They collected this ground <\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 SCXW84139600 BCX2\">truth<\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\"> data in<\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\"> several <\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">regions distributed <\/span><\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange SCXW84139600 BCX2\"><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">across <\/span><\/span><\/span><span class=\"TextRun SCXW84139600 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW84139600 BCX2\">the country; it is the part of their demo where they show lots of rectangles throughout their country. Doing this helps get diversity in the training data and helps create a more generalizable model. They proceeded to use the <strong>arcgis.learn module of the ArcGIS API for Python\u00a0<\/strong>to train the model. <span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW84139600\">As a first step, they exported their data, prepared it for the model and visualized this prepared data to validate it. Using <\/span><\/span><strong><span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SpellingErrorV2 BCX2 SCXW84139600\">arcgis.learn<\/span><\/span><span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW84139600\">.<\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange BCX2 SCXW84139600\"><span class=\"FieldRange BCX2 SCXW84139600\"><span class=\"TextRun Underlined BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun BCX2 SCXW84139600\" data-ccp-charstyle=\"Hyperlink\">DeepLab<\/span><\/span><\/span><\/span> <\/strong><span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW84139600\">with <strong>ResNet152<\/strong> as the backbone<\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange BCX2 SCXW84139600\"><span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW84139600\">,<\/span><\/span><\/span><span class=\"TextRun BCX2 SCXW84139600\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW84139600\"> they trained the model using multiple iterations, each time starting from where they last stopped to increase its accuracy.\u00a0<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185132,"id":1185132,"title":"paci-2","filename":"paci-2.png","filesize":134468,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit-2021-kuwaits-paci-using-arcgis-learn\/paci-2","alt":"paci-model","author":"7461","description":"","caption":"The model trained by PACI","name":"paci-2","status":"inherit","uploaded_to":1185082,"date":"2021-04-07 03:17:16","modified":"2021-04-07 03:17:41","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":1729,"height":879,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","medium-width":464,"medium-height":236,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","medium_large-width":768,"medium_large-height":390,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","large-width":1729,"large-height":879,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2-1536x781.png","1536x1536-width":1536,"1536x1536-height":781,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","2048x2048-width":1729,"2048x2048-height":879,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2-826x420.png","card_image-width":826,"card_image-height":420,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-2.png","wide_image-width":1729,"wide_image-height":879}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">Spanning an area of over 600 square <\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">kilometers<\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">, <\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">they identified 111,563 building footprints, 78,689 street signals and 2600 parking lots. <\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">Manually digitizing these structures normally takes about 74 days. But <\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">all this process t<\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">ook was 1.5 hours of processing time and about 2 days for quality assurance and quality control, with a <\/span><\/span><span class=\"TrackChangeTextInsertion TrackedChange BCX2 SCXW157505364\"><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">massive <\/span><\/span><\/span><span class=\"TextRun BCX2 SCXW157505364\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun BCX2 SCXW157505364\">900% increase in efficiency. By using the deep learning capabilities of ArcGIS, PACI is <span class=\"TextRun SCXW242858511 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun AdvancedProofingIssueV2 SCXW242858511 BCX2\">able to<\/span><\/span><span class=\"TextRun SCXW242858511 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW242858511 BCX2\"> provide timely and accurate data to their citizens with improved <\/span><\/span><span class=\"TextRun SCXW242858511 BCX2\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW242858511 BCX2\">efficiency.<\/span><\/span><\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1185142,"id":1185142,"title":"paci-3-small","filename":"paci-3-small.png","filesize":956285,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/devsummit-2021-kuwaits-paci-using-arcgis-learn\/paci-3-small","alt":"paci3-output","author":"7461","description":"","caption":"Result of using the AI capabilities in ArcGIS to automate updates of the basemap","name":"paci-3-small","status":"inherit","uploaded_to":1185082,"date":"2021-04-07 03:21:23","modified":"2021-04-07 03:22:21","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":1092,"height":465,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","medium-width":464,"medium-height":198,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","medium_large-width":768,"medium_large-height":327,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","large-width":1092,"large-height":465,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","1536x1536-width":1092,"1536x1536-height":465,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","2048x2048-width":1092,"2048x2048-height":465,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small-826x352.png","card_image-width":826,"card_image-height":352,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-3-small.png","wide_image-width":1092,"wide_image-height":465}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<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:\/\/www.esri.com\/arcgis-blog\/products\/api-python\/analytics\/deep-learning-models-in-arcgis-learn\/\"><span data-contrast=\"none\">Deep learning models in <\/span><span data-contrast=\"none\">arcgis.learn<\/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:\/\/developers.arcgis.com\/python\/guide\/geospatial-deep-learning\/\"><span data-contrast=\"none\">Geospatial deep learning with <\/span><span data-contrast=\"none\">arcgis.learn<\/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=\"3\" data-aria-level=\"1\"><a href=\"https:\/\/developers.arcgis.com\/python\/api-reference\/arcgis.learn.toc.html\"><span data-contrast=\"none\">API reference for <\/span><span data-contrast=\"none\">arcgis.learn<\/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. Or in other words, a (Data, Maps, Analyses, Python, Books) Nerd.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/03\/me_cropped-213x200.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":"","card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2021\/04\/paci-banner.jpg","wide_image":false},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Dev Summit 2021: Kuwait&#039;s PACI using arcgis.learn<\/title>\n<meta name=\"description\" content=\"Maher Abdel Karim from PACI shows how they used an AI model to keep their GIS data (roads, buildings, parking lots) up to date using arcgis.learn.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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