{"id":2960778,"date":"2026-03-20T10:00:11","date_gmt":"2026-03-20T17:00:11","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2960778"},"modified":"2026-04-08T21:26:11","modified_gmt":"2026-04-09T04:26:11","slug":"geoai-basics-for-urban-and-landscape-design","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design","title":{"rendered":"GeoAI Basics for Urban and Landscape Design"},"author":56081,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[615021,37151,22931],"tags":[758311,665211,557702,780944,756352],"industry":[],"product":[36581,36561],"class_list":["post-2960778","blog","type-blog","status-publish","format-standard","hentry","category-aec","category-design-planning","category-imagery","tag-ai","tag-geoai","tag-imagery-and-remote-sensing","tag-landscape-architecture","tag-urban-design","product-arcgis-living-atlas","product-arcgis-pro"],"acf":{"short_description":"Basic GeoAI workflows for urban and landscape design analysis and research","flexible_content":[{"acf_fc_layout":"content","content":"

There is a lot of excitement around AI and Deep Learning these days, and the world of GIS is no exception. Geospatial AI (GeoAI<\/a>) can support planners, urban designers, and landscape architects with workflows such as tracing building footprints, identifying land use patterns, and understanding land cover change. GeoAI workflows are especially helpful when there is an absence of existing data for the site.<\/p>\n

There are various deep learning models in Living Atlas for identifying and extracting information from data such as satellite imagery. These pre-trained models can be downloaded and used directly in ArcGIS Pro. They can also be further trained by the user.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960779,"id":2960779,"title":"LivingAtlas_DeepLearningModels2","filename":"LivingAtlas_DeepLearningModels2.gif","filesize":35942786,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/livingatlas_deeplearningmodels2","alt":"","author":"56081","description":"","caption":"Living Atlas pre-trained deep learning models","name":"livingatlas_deeplearningmodels2","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 03:35:13","modified":"2026-03-20 08:05:32","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1728,"height":1080,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","medium-width":418,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","medium_large-width":768,"medium_large-height":480,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","large-width":1728,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2-1536x960.gif","1536x1536-width":1536,"1536x1536-height":960,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","2048x2048-width":1728,"2048x2048-height":1080,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2-744x465.gif","card_image-width":744,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/LivingAtlas_DeepLearningModels2.gif","wide_image-width":1728,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Forest Loss Analysis with Pre-Trained Model<\/h3>\n

Let\u2019s take for example a GeoAI workflow for identifying and extracting land cover data from satellite imagery. I used a deep learning model<\/a> from Living Atlas to analyze forest changes from timber logging in Oregon. The land cover classification model comes with a step by step guide<\/a> and works with Landsat 8 imagery.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960780,"id":2960780,"title":"Blog Images-01","filename":"Blog-Images-01-scaled.jpg","filesize":1047060,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images-01","alt":"","author":"56081","description":"","caption":"True Color Composite Images of Oregon region for 2015 (left), and 2025 (right)","name":"blog-images-01","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 03:40:54","modified":"2026-03-20 08:05:37","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-scaled.jpg","medium-width":464,"medium-height":238,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-scaled.jpg","medium_large-width":768,"medium_large-height":394,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-scaled.jpg","large-width":1920,"large-height":986,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-1536x789.jpg","1536x1536-width":1536,"1536x1536-height":789,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-2048x1051.jpg","2048x2048-width":2048,"2048x2048-height":1051,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-826x424.jpg","card_image-width":826,"card_image-height":424,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-01-1920x986.jpg","wide_image-width":1920,"wide_image-height":986}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

First, I download Landsat 8 imagery from USGS Earth Explorer<\/a> near Eugene, OR for 2015 and 2025. I used the imagery to create two multiband <\/em>(Bands 1 \u2013 7) composite images in ArcGIS Pro. Next, I ran the Classify Pixels Using Deep Learning<\/a> tool under Image Analysis Tools>Deep Learning for 2015 and for 2025. The tool extracted from the multiband images a classified land cover raster dataset for each year.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960939,"id":2960939,"title":"Blog ImagesED-02","filename":"Blog-ImagesED-02-scaled.jpg","filesize":1466293,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-imagesed-02","alt":"","author":"56081","description":"","caption":"Land Cover of Oregon region for 2015 (left), and 2025 (right)","name":"blog-imagesed-02","status":"inherit","uploaded_to":2960778,"date":"2026-03-20 05:11:12","modified":"2026-03-20 08:05:42","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-scaled.jpg","medium-width":464,"medium-height":238,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-scaled.jpg","medium_large-width":768,"medium_large-height":394,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-scaled.jpg","large-width":1920,"large-height":986,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-1536x789.jpg","1536x1536-width":1536,"1536x1536-height":789,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-2048x1051.jpg","2048x2048-width":2048,"2048x2048-height":1051,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-826x424.jpg","card_image-width":826,"card_image-height":424,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-ImagesED-02-1920x986.jpg","wide_image-width":1920,"wide_image-height":986}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

The land cover raster datasets were further processed to map and measure forest loss from 2015 to 2025. The methodology I followed started with reclassifying the land cover raster datasets. I assigned to forest land cover a value of \u201c2\u201d, and to everything else a value of \u201c1\u201d. To map forest change, I calculated the difference between the two raster datasets with the Raster Calculator, under Spatial Analyst Tools > Map Algebra . To measure forest loss, I converted the forest change raster to a polygon dataset. In the attribute table, I calculated the area of each shape in a new field and summarized the field’s statistics.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960782,"id":2960782,"title":"Blog Images-03","filename":"Blog-Images-03-scaled.jpg","filesize":2027079,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images-03","alt":"","author":"56081","description":"","caption":"Forest loss (red) and gain (green) for Oregon region between 2015 and 2025","name":"blog-images-03","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 03:42:48","modified":"2026-03-20 06:25:45","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1906,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-scaled.jpg","medium-width":351,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-scaled.jpg","medium_large-width":768,"medium_large-height":572,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-scaled.jpg","large-width":1451,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-1536x1144.jpg","1536x1536-width":1536,"1536x1536-height":1144,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-2048x1525.jpg","2048x2048-width":2048,"2048x2048-height":1525,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-625x465.jpg","card_image-width":625,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-03-1451x1080.jpg","wide_image-width":1451,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

City Growth Analysis with Self-Trained Model<\/h3>\n

Similarly to tracking forest loss from logging, GeoAI workflows can be leveraged for analyzing how cities evolve. In this example, I used land cover classification on Landsat 8 imagery to analyze the growth of Lagos, Nigeria between 2013 and 2024.<\/p>\n

For this workflow, instead of leveraging a Living Atlas deep learning model, I trained my own classification model in ArcGIS Pro. First, I created false color <\/em>or color infrared<\/em> composite images for each year, using Landsat 8 imagery from USGS Earth Explorer.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960783,"id":2960783,"title":"Blog Images-04","filename":"Blog-Images-04-scaled.jpg","filesize":1098992,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images-04","alt":"","author":"56081","description":"","caption":"False Color Composite Images (Bands 7,6,4) of Lagos for 2013 (left), and 2024 (right)","name":"blog-images-04","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 03:54:39","modified":"2026-03-20 08:05:52","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-scaled.jpg","medium-width":464,"medium-height":238,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-scaled.jpg","medium_large-width":768,"medium_large-height":394,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-scaled.jpg","large-width":1920,"large-height":986,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-1536x789.jpg","1536x1536-width":1536,"1536x1536-height":789,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-2048x1051.jpg","2048x2048-width":2048,"2048x2048-height":1051,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-826x424.jpg","card_image-width":826,"card_image-height":424,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-04-1920x986.jpg","wide_image-width":1920,"wide_image-height":986}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"image","image":{"ID":2960784,"id":2960784,"title":"Blog Images-10","filename":"Blog-Images-10-scaled.jpg","filesize":1102800,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images-10","alt":"","author":"56081","description":"","caption":"Color Infrared Composite Images (Bands 5,4,3) of Lagos for 2013 (left), and 2024 (right)","name":"blog-images-10","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 03:55:49","modified":"2026-03-20 08:05:57","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-scaled.jpg","medium-width":464,"medium-height":238,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-scaled.jpg","medium_large-width":768,"medium_large-height":394,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-scaled.jpg","large-width":1920,"large-height":986,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-1536x789.jpg","1536x1536-width":1536,"1536x1536-height":789,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-2048x1051.jpg","2048x2048-width":2048,"2048x2048-height":1051,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-826x424.jpg","card_image-width":826,"card_image-height":424,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-10-1920x986.jpg","wide_image-width":1920,"wide_image-height":986}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

With either the false color<\/em> or the color infrared<\/em> composite images, I ran a supervised, pixel-based land cover classification, under Imagery> Classification Wizard<\/a>. I manually trained the classification model on what pixels represent water, development, agriculture, etc.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960976,"id":2960976,"title":"SelfTrained_DL_Small","filename":"SelfTrained_DL_Small.gif","filesize":41251599,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/selftrained_dl_small","alt":"","author":"56081","description":"","caption":"Training the Image Classification model on the false color composite image","name":"selftrained_dl_small","status":"inherit","uploaded_to":2960778,"date":"2026-03-20 08:04:32","modified":"2026-03-20 08:06:10","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1728,"height":1080,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","medium-width":418,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","medium_large-width":768,"medium_large-height":480,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","large-width":1728,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small-1536x960.gif","1536x1536-width":1536,"1536x1536-height":960,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","2048x2048-width":1728,"2048x2048-height":1080,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small-744x465.gif","card_image-width":744,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/SelfTrained_DL_Small.gif","wide_image-width":1728,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Once I trained the model on a composite image, I ran the remaining steps of the Imagery Classification Wizard. The tool generated a classified land cover raster for each year.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960786,"id":2960786,"title":"Blog Images-05","filename":"Blog-Images-05-scaled.jpg","filesize":1834941,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images-05","alt":"","author":"56081","description":"","caption":"Land Cover of Lagos for 2013 (left), and 2024 (right)","name":"blog-images-05","status":"inherit","uploaded_to":2960778,"date":"2026-03-19 04:00:42","modified":"2026-03-20 08:07:03","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1314,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-scaled.jpg","medium-width":464,"medium-height":238,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-scaled.jpg","medium_large-width":768,"medium_large-height":394,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-scaled.jpg","large-width":1920,"large-height":986,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-1536x789.jpg","1536x1536-width":1536,"1536x1536-height":789,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-2048x1051.jpg","2048x2048-width":2048,"2048x2048-height":1051,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-826x424.jpg","card_image-width":826,"card_image-height":424,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images-05-1920x986.jpg","wide_image-width":1920,"wide_image-height":986}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Following a similar methodology as in the example of forest logging, I used the two land cover raster datasets to map and measure Lago\u2019s city growth.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2960973,"id":2960973,"title":"Blog Images2-06","filename":"Blog-Images2-06-scaled.jpg","filesize":1705950,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-scaled.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\/blog-images2-06","alt":"","author":"56081","description":"","caption":"Expansion of developed land in Lagos from 2013 to 2024 (orange)","name":"blog-images2-06","status":"inherit","uploaded_to":2960778,"date":"2026-03-20 07:25:28","modified":"2026-03-20 08:07:09","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":2560,"height":1906,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-scaled.jpg","medium-width":351,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-scaled.jpg","medium_large-width":768,"medium_large-height":572,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-scaled.jpg","large-width":1451,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-1536x1144.jpg","1536x1536-width":1536,"1536x1536-height":1144,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-2048x1525.jpg","2048x2048-width":2048,"2048x2048-height":1525,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-625x465.jpg","card_image-width":625,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Blog-Images2-06-1451x1080.jpg","wide_image-width":1451,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

These are just two examples of using GeoAI workflows for data extraction from imagery. Both of these examples focused on land cover changes. There are many other deep learning models in Living Atlas for identifying and extracting data such as building footprints, roads, and parcels. Have fun exploring them!<\/p>\n

-Renia<\/p>\n"}],"authors":[{"ID":56081,"user_firstname":"Renia","user_lastname":"Kagkou","nickname":"Renia Kagkou","user_nicename":"rkagkou","display_name":"Renia Kagkou","user_email":"rkagkou@esri.com","user_url":"https:\/\/renia-kagkou.com\/","user_registered":"2020-06-25 23:40:55","user_description":"Renia is a Senior Solution Engineer at Esri's AEC (Architecture, Engineering, Construction) team. At Esri, she develops workflows that integrate geospatial analysis, scenario testing, and impact assessment, for decision-making in planning and designing the built environment. \r\n\r\nRenia is from Athens, Greece and she holds a Master of Architecture in Urban Design (MAUD) and a Master of Design Studies in Urbanism, Landscape, and Ecology (MDes ULE) from Harvard GSD (2018), and a Bachelor of Architecture from Pratt Institute, New York (2014). \r\n\r\nPrior to joining Esri, she worked as an architect in New York, and as a GIS specialist for Urban Planning firms and University projects in Cambridge and Boston, MA. Renia has also taught Mapping, Cartography, and GIS courses at SCI-Arc (Los Angeles), and for the Master Program on Architecture, European Urbanization and Globalization, at the University of Luxembourg (Belval).","user_avatar":""}],"related_articles":[{"ID":2945812,"post_author":"254042","post_date":"2025-11-18 09:45:06","post_date_gmt":"2025-11-18 17:45:06","post_content":"","post_title":"What's New for GeoAI in the ArcGIS Pro 3.6 Image Analyst Extension","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"whats-new-for-geoai-in-the-arcgis-pro-3-6-image-analyst-extension","to_ping":"","pinged":"","post_modified":"2025-12-22 13:44:27","post_modified_gmt":"2025-12-22 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12:48:07","post_content":"","post_title":"Building Change Detection in the Region of Catalu\u00f1a","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"building-change-detection-in-the-region-of-cataluna","to_ping":"","pinged":"","post_modified":"2024-06-11 05:48:30","post_modified_gmt":"2024-06-11 12:48:30","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2364712","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":2951566,"post_author":"56081","post_date":"2025-12-15 10:00:02","post_date_gmt":"2025-12-15 18:00:02","post_content":"","post_title":"GIS Workflows for 3D Terrain in Rhino","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"gis-workflows-for-3d-terrain-in-rhino","to_ping":"","pinged":"","post_modified":"2026-01-12 13:01:18","post_modified_gmt":"2026-01-12 21:01:18","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2951566","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":2953668,"post_author":"56081","post_date":"2026-01-12 10:00:29","post_date_gmt":"2026-01-12 18:00:29","post_content":"","post_title":"Tips and Tricks for Moving 2D Data between ArcGIS Pro and Rhino","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"tips-and-tricks-for-moving-2d-data-between-arcgis-pro-and-rhino","to_ping":"","pinged":"","post_modified":"2026-01-22 15:13:25","post_modified_gmt":"2026-01-22 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03:02:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2956290","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/03\/Thumbnail-01-scaled-e1773989467916.jpg","wide_image":false},"yoast_head":"\nGeoAI Basics for Urban and Landscape Design<\/title>\n<meta name=\"description\" content=\"Introduction to Geospatial AI (GeoAI) workflows for urban designers, landscape architects, and planners\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/aec\/geoai-basics-for-urban-and-landscape-design\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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Kagkou","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/image\/","url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/12\/g650437-staff-portrait-0360-213x200.jpg","contentUrl":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/12\/g650437-staff-portrait-0360-213x200.jpg","caption":"Renia Kagkou"},"description":"Renia is a Senior Solution Engineer at Esri's AEC (Architecture, Engineering, Construction) team. At Esri, she develops workflows that integrate geospatial analysis, scenario testing, and impact assessment, for decision-making in planning and designing the built environment. Renia is from Athens, Greece and she holds a Master of Architecture in Urban Design (MAUD) and a Master of Design Studies in Urbanism, Landscape, and Ecology (MDes ULE) from Harvard GSD (2018), and a Bachelor of Architecture from Pratt Institute, New York (2014). Prior to joining Esri, she worked as an architect in New York, and as a GIS specialist for Urban Planning firms and University projects in Cambridge and Boston, MA. 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