{"id":2959916,"date":"2026-03-11T01:37:07","date_gmt":"2026-03-11T08:37:07","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2959916"},"modified":"2026-03-12T23:50:10","modified_gmt":"2026-03-13T06:50:10","slug":"turning-imagery-into-action-with-geoai","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/turning-imagery-into-action-with-geoai","title":{"rendered":"Turning Imagery into Action with GeoAI"},"author":207622,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[770712,23341,22931],"tags":[43101,197722,26121,665211],"industry":[],"product":[36561],"class_list":["post-2959916","blog","type-blog","status-publish","format-standard","hentry","category-geoai","category-analytics","category-imagery","tag-arcgis-living-atlas-of-the-world","tag-disaster-recovery","tag-disaster-response","tag-geoai","product-arcgis-pro"],"acf":{"authors":[{"ID":207622,"user_firstname":"Aawaj","user_lastname":"Joshi","nickname":"Aawaj Joshi","user_nicename":"ajoshi","display_name":"Aawaj Joshi","user_email":"ajoshi@esri.com","user_url":"","user_registered":"2021-03-25 20:59:02","user_description":"Aawaj is a Product Engineer on the ArcGIS Enterprise team.","user_avatar":""},{"ID":348302,"user_firstname":"Priyanka","user_lastname":"Tuteja","nickname":"Priyanka Tuteja","user_nicename":"ptuteja","display_name":"Priyanka Tuteja","user_email":"ptuteja@esri.com","user_url":"","user_registered":"2023-11-02 15:56:33","user_description":"Principal Product Engineer in the GeoAI team at Esri R&D Center, New Delhi, India.","user_avatar":""}],"short_description":"GeoAI can rapidly turn imagery into operational intelligence, helping responders make informed decisions when every minute matters.","flexible_content":[{"acf_fc_layout":"content","content":"

When a major disaster strikes, every minute matters. Roads may be blocked, buildings damaged, and infrastructure disrupted, yet aid must still reach hospitals and affected communities as quickly as possible.<\/p>\n

At this year\u2019s Developer and Technology Summit plenary, Priyanka Tuteja demonstrates how GeoAI<\/a> can turn post-disaster imagery into operational insight, rapidly mapping roads, assessing damage, generating optimized response routes, and accelerating decision-making.<\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_v5qptp65","time":true,"start":"223","stop":"477"},{"acf_fc_layout":"content","content":"

 <\/p>\n

To illustrate this workflow, Priyanka focuses on a critical response task: delivering medical aid from a helicopter landing zone to two priority locations\u2014a hospital and a relief tent in Westmoreland\u2014while navigating a transportation network disrupted by Hurricane Melissa, a Category 5 storm that swept through Jamaica.<\/p>\n"},{"acf_fc_layout":"content","content":"

 <\/p>\n

Reconstructing the Road Network from Imagery<\/h2>\n

With pre-disaster data often unreliable, Priyanka shows how GeoAI allows responders to reconstruct the transportation network directly from post-disaster imagery. She uses a road extraction model from ArcGIS Living Atlas of the World<\/a> to automatically detect roads from imagery captured after the hurricane. The resulting network reflects the real-world conditions on the ground. <\/p>\n

However, detection alone isn’t enough, as responders need to know which roads are usable.<\/p>\n"},{"acf_fc_layout":"content","content":"

 <\/p>\n

Classifying Road Conditions with a Vision-Language Model<\/h2>\n

Once roads are extracted, Priyanka uses the Classify Objects Using Deep Learning tool<\/a> to run the Vision Language Context-Based Classification pretrained model<\/a>, which categorizes each road segment based on post-disaster conditions:<\/p>\n