{"id":2089472,"date":"2024-01-10T00:55:50","date_gmt":"2024-01-10T08:55:50","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2089472"},"modified":"2025-12-22T13:51:16","modified_gmt":"2025-12-22T21:51:16","slug":"enhanced-object-detection-using-drones-and-ai","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai","title":{"rendered":"Enhanced Object Detection using Drones and AI"},"author":254042,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[770712,22931],"tags":[186132,757211,665211,515302,771832],"industry":[],"product":[36561],"class_list":["post-2089472","blog","type-blog","status-publish","format-standard","hentry","category-geoai","category-imagery","tag-deep-learning","tag-drone","tag-geoai","tag-object-detection","tag-pixel-space","product-arcgis-pro"],"acf":{"short_description":"We will demonstrate how drone images and AI provide improved object detection achieved through Pixel Space to Map Space transformation.","flexible_content":[{"acf_fc_layout":"content","content":"<p>One of the tasks that <a href=\"https:\/\/www.esri.com\/en-us\/artificial-intelligence\/overview\">artificial intelligence<\/a> excels at is Object Detection. This computer vision technique allows us to identify and locate objects within images and videos. It is an invaluable tool for mapping our world, creating digital twins, and gaining a quantitative understanding of our environment. These, in turn, fuel data-driven decisions for smart cities, urban planning, resource management, environment protection, and disaster response.<\/p>\n<p><span class=\"ui-provider ed awh awi awj awk awl awm awn awo awp awq awr aws awt awu awv aww awx awy awz axa axb axc axd axe axf axg axh axi axj axk axl axm axn axo\" dir=\"ltr\">Orthoimages are often used for object detection, but they are not always the best <\/span> because of the following reasons:<\/p>\n<p>&nbsp;<\/p>\n"},{"acf_fc_layout":"content","content":"<ul>\n<li><strong>Limited details<\/strong>: Orthoimages can make objects like fire hydrants and poles appear as dots or circles. However, oblique images captured from drones or street-view cameras provide more details of the objects, making them easier to detect.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":2214232,"id":2214232,"title":"Limited details in Ortho Images","filename":"Limited-Details-e1704748664607.png","filesize":961333,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/limited-details","alt":"Ortho images (left) limit object details, like poles and hydrants appearing as dots or circle, while oblique images (right) offer more details","author":"254042","description":"Ortho images (left) limit object details, like poles and hydrants appearing as dots or circle, while oblique images (right) offer more details","caption":"Ortho images (left) limit object details, like poles and hydrants appearing as dots or circle, while oblique images (right) offer more details","name":"limited-details","status":"inherit","uploaded_to":2089472,"date":"2024-01-02 17:31:23","modified":"2024-01-02 17:42:59","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":400,"height":235,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","medium-width":400,"medium-height":235,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","medium_large-width":400,"medium_large-height":235,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","large-width":400,"large-height":235,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","1536x1536-width":400,"1536x1536-height":235,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","2048x2048-width":400,"2048x2048-height":235,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-791x465.png","card_image-width":791,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Limited-Details-e1704748664607.png","wide_image-width":400,"wide_image-height":235}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<ul>\n<li><strong>Blind spots<\/strong>: Tree canopies in orthoimages can completely conceal objects. \u202fThe left image shows the location of an asset that we cannot see because of the tree canopies. The right image shows the same asset in a picture taken from the street.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":2214252,"id":2214252,"title":"Blind spots in Ortho Images","filename":"Blind-Spots-e1704749004566.png","filesize":1107306,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/blind-spots","alt":"Tree canopies conceal an asset in the ortho image (left), while the street-view image reveals it (right)","author":"254042","description":"Tree canopies conceal an asset in the ortho image (left), while the street-view image reveals it (right)","caption":"Tree canopies conceal an asset in the ortho image (left), while the street-view image reveals it (right)","name":"blind-spots","status":"inherit","uploaded_to":2089472,"date":"2024-01-02 17:37:39","modified":"2024-01-02 20:50:32","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":399,"height":235,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","medium-width":399,"medium-height":235,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","medium_large-width":399,"medium_large-height":235,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","large-width":399,"large-height":235,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","1536x1536-width":399,"1536x1536-height":235,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","2048x2048-width":399,"2048x2048-height":235,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-728x465.png","card_image-width":728,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Blind-Spots-e1704749004566.png","wide_image-width":399,"wide_image-height":235}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<ul>\n<li><strong>Limited models and datasets<\/strong>: Open-source training datasets such as COCO (Common Objects in Context) dataset and pre-trained models are predominantly available for non-ortho images. They do not work as well on orthoimages.<\/li>\n<\/ul>\n"},{"acf_fc_layout":"image","image":{"ID":2138872,"id":2138872,"title":"coco-examples","filename":"coco-examples-e1704748920425.jpg","filesize":96787,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/coco-examples","alt":"Virtually all images in the COCO dataset are oblique","author":"254042","description":"Virtually all images in the COCO dataset are oblique","caption":"Virtually all images in the COCO dataset are oblique","name":"coco-examples","status":"inherit","uploaded_to":2089472,"date":"2023-11-02 18:47:47","modified":"2024-01-02 20:57:14","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":600,"height":182,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","medium-width":464,"medium-height":141,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","medium_large-width":600,"medium_large-height":182,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","large-width":600,"large-height":182,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","1536x1536-width":600,"1536x1536-height":182,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","2048x2048-width":600,"2048x2048-height":182,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","card_image-width":600,"card_image-height":182,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/11\/coco-examples-e1704748920425.jpg","wide_image-width":600,"wide_image-height":182}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>One solution to these challenges is utilizing oblique imagery from drones or street-view cameras. We can detect the objects in the Pixel Space of the oblique images and transform the detections to the Map Space. Map space utilizes a map-based coordinate reference system, while Pixel Space references the raw image space without rotations or distortions. When a model with Pixel Space reference system is used with an orthorectified image collection that contains images with camera information, first inferencing happens in Pixel Space and then the inferenced geometries are transformed to Map Space using the frame and camera information.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2214282,"id":2214282,"title":"Pixel Space and Map Space","filename":"Pixel-Space-and-Map-Space-e1704749070519.png","filesize":71178,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/pixel-space-and-map-space-2","alt":"Location of an object in Pixel Space and Map Space","author":"254042","description":"Location of an object in Pixel Space and Map Space","caption":"Location of an object in Pixel Space and Map Space","name":"pixel-space-and-map-space-2","status":"inherit","uploaded_to":2089472,"date":"2024-01-02 17:42:15","modified":"2024-01-02 17:42:31","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":400,"height":295,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","medium-width":354,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","medium_large-width":400,"medium_large-height":295,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","large-width":400,"large-height":295,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","1536x1536-width":400,"1536x1536-height":295,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","2048x2048-width":400,"2048x2048-height":295,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","card_image-width":400,"card_image-height":295,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Pixel-Space-and-Map-Space-e1704749070519.png","wide_image-width":400,"wide_image-height":295}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"ui-provider ed awh awi awj awk awl awm awn awo awp awq awr aws awt awu awv aww awx awy awz axa axb axc axd axe axf axg axh axi axj axk axl axm axn axo\" dir=\"ltr\">In this blog, we will use drone images taken from a new commercial development called the Packing House district, located in Redlands, California.<\/span> We will build a model to detect parked cars. It can potentially help the decision makers understand the traffic in this new development. Our goal is to offer a workflow template and showcase its application through an example.<\/p>\n<p>We need the following two for our workflow:<\/p>\n<ol>\n<li>A model that is trained to work with drone images. The model should use Pixel Space as a coordinate reference system.<\/li>\n<li>An orthorectified image collection that contains drone images with camera information including focal length and other sensor characteristics. GPS accuracy is important, include if available.<\/li>\n<\/ol>\n<p>Now let us look at the steps.<\/p>\n"},{"acf_fc_layout":"content","content":"<h2><span data-contrast=\"none\">Step 1: Build a model<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"none\">Build the model in three steps (visual guide in video):<\/span><\/p>\n<ol>\n<li><b><span data-contrast=\"none\">Label images<\/span><\/b><span data-contrast=\"none\">: label using the <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/3.2\/help\/analysis\/image-analyst\/label-objects-for-deep-learning.htm\"><span data-contrast=\"none\">Label Objects for Deep Learning<\/span><\/a><span data-contrast=\"none\"> pane with Image Collection and Pixel Space options.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li><b><span data-contrast=\"none\">Export image chips<\/span><\/b><span data-contrast=\"none\">: next, use <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/3.2\/tool-reference\/image-analyst\/export-training-data-for-deep-learning.htm\"><span data-contrast=\"none\">Export Training Data for Deep Learning<\/span><\/a><span data-contrast=\"none\"> tool with Reference System set to Pixel Space to export out the image chips.<\/span><\/li>\n<li><b><span data-contrast=\"none\">Train model<\/span><\/b><span data-contrast=\"none\">: finally, use <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/3.2\/tool-reference\/image-analyst\/train-deep-learning-model.htm\"><span data-contrast=\"none\">Train Deep Learning Model tool<\/span><\/a><span data-contrast=\"none\">. Output model will have &#8220;ImageSpaceUsed&#8221;: &#8220;PIXEL_SPACE&#8221; in Esri Model Definition (emd) file.<\/span><\/li>\n<\/ol>\n"},{"acf_fc_layout":"kaltura","video_id":"1_y0ut00z7","time":false,"start":0,"stop":""},{"acf_fc_layout":"content","content":"<h2><span data-contrast=\"none\">Step 2: Create an orthorectified image collection<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"none\">Create an orthorectified image collection in three steps (visual guide in video):<\/span><\/p>\n<ol>\n<li><b><span data-contrast=\"auto\">Create workspace<\/span><\/b><span data-contrast=\"auto\">: use <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/help\/data\/imagery\/create-an-ortho-mapping-workspace.htm\"><span data-contrast=\"none\">New Ortho Mapping Workspace wizard.<\/span><\/a><\/li>\n<li><b><span data-contrast=\"auto\">Block Adjust<\/span><\/b><span data-contrast=\"auto\">: use <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/help\/data\/imagery\/block-adjustment.htm\"><span data-contrast=\"none\">tools in the Adjust and Refine<\/span><\/a><span data-contrast=\"auto\"> groups to calculate the orientation of each image. If available, add GCPs and Tie Points.<\/span><\/li>\n<li><strong>Compute Depth Map<\/strong>: use the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/compute-depth-map.htm\">Compute Depth Map<\/a> (Data Management) tool for better detection transformation accuracy.<\/li>\n<li><b><span data-contrast=\"auto\">Limit duplicate Images<\/span><\/b><span data-contrast=\"auto\">: use <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/data-management\/compute-mosaic-candidates.htm\"><span data-contrast=\"none\">Compute Candidate Items<\/span><\/a><span data-contrast=\"auto\"> tool to <\/span><span data-contrast=\"none\">identify the image candidates that best represent the mosaic area.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ol>\n"},{"acf_fc_layout":"kaltura","video_id":"1_nycbjg4v","time":false,"start":0,"stop":""},{"acf_fc_layout":"content","content":"<p>&nbsp;<\/p>\n<h2><span data-contrast=\"none\">Steps 3: Detect Objects<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">Use the <\/span><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/latest\/tool-reference\/image-analyst\/detect-objects-using-deep-learning.htm\"><span data-contrast=\"none\">Detect Objects Using Deep Learning<\/span><\/a><span data-contrast=\"auto\"> tool with the model and the image collection as inputs and the Processing Mode parameter set to the <\/span><b><i><span data-contrast=\"auto\">Process candidate items only<\/span><\/i><\/b><span data-contrast=\"auto\"> and run the tool. This processing mode will make sure that detections first happen in the raw Pixels Space of each image, and then they are transformed into to Map Space of the Image Collection.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2212482,"id":2212482,"title":"Detect Objects Using Deep Learning tool","filename":"Detect-Objects-Using-Deep-Learning.png","filesize":27031,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/detect-objects-using-deep-learning-8","alt":"Detect Objects Using Deep Learning tool","author":"254042","description":"Detect Objects Using Deep Learning tool","caption":"Detect Objects Using Deep Learning tool","name":"detect-objects-using-deep-learning-8","status":"inherit","uploaded_to":2089472,"date":"2023-12-28 22:23:32","modified":"2024-01-02 17:44:01","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":406,"height":601,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","medium-width":176,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","medium_large-width":406,"medium_large-height":601,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","large-width":406,"large-height":601,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","1536x1536-width":406,"1536x1536-height":601,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","2048x2048-width":406,"2048x2048-height":601,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning-314x465.png","card_image-width":314,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detect-Objects-Using-Deep-Learning.png","wide_image-width":406,"wide_image-height":601}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"none\">For a direct Pixel Space vs. Pixel Space-to-Map Space detection comparison, we ran the tool on both: a single drone image (Pixel Space) and the image collection (Pixel Space-to-Map Space).<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2212502,"id":2212502,"title":"Detections in the Pixel Space of one of the raw drone images","filename":"Detections-in-the-Pixel-Space-of-one-of-the-raw-drone-images.jpg","filesize":79434,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Detections-in-the-Pixel-Space-of-one-of-the-raw-drone-images.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/geoai\/enhanced-object-detection-using-drones-and-ai\/detections-in-the-pixel-space-of-one-of-the-raw-drone-images","alt":"Detections in the Pixel Space of one of the raw drone images","author":"254042","description":"Detections in the Pixel Space of one of the raw drone images","caption":"Detections in the Pixel Space of one of the raw drone images","name":"detections-in-the-pixel-space-of-one-of-the-raw-drone-images","status":"inherit","uploaded_to":2089472,"date":"2023-12-28 22:32:12","modified":"2023-12-28 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18:20:02","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":480,"height":349,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","medium-width":359,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","medium_large-width":480,"medium_large-height":349,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","large-width":480,"large-height":349,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","1536x1536-width":480,"1536x1536-height":349,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","2048x2048-width":480,"2048x2048-height":349,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","card_image-width":480,"card_image-height":349,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/Detections-in-the-Map-Space-of-the-image-collection-2.png","wide_image-width":480,"wide_image-height":349}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h2><span data-contrast=\"none\">Conclusion<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;201341983&quot;:0,&quot;335559738&quot;:40,&quot;335559739&quot;:0,&quot;335559740&quot;:259}\">\u00a0<\/span><\/h2>\n<p>In the blog, you gained knowledge about the benefits of oblique images in improving object detection. You explored the steps using a drone image set; however, you can apply these steps and concepts to images captured by Street-view cameras. Additionally, the workflow can be adapted for pixel classification tasks.<\/p>\n<p><span data-contrast=\"none\">Did you find this blog insightful? We would love to hear your thoughts in the comments.<\/span><\/p>\n"}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/card-1.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/01\/banner.png","authors":[{"ID":254042,"user_firstname":"Pavan","user_lastname":"Yadav","nickname":"Pavan Yadav","user_nicename":"pyadav","display_name":"Pavan Yadav","user_email":"PYadav@esri.com","user_url":"","user_registered":"2021-07-20 16:55:01","user_description":"Pavan Yadav is a Senior Software Product Engineer at Esri's Imagery team, leveraging AI to extract valuable insights from imagery data and contributing to the development of geospatial AI (GeoAI).","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/12\/Pavan-Yadav-213x200.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":[{"ID":1302112,"post_author":"178852","post_date":"2021-08-02 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ArcGIS","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"repurposing-deep-learning-models-using-transfer-learning-in-arcgis","to_ping":"","pinged":"","post_modified":"2023-07-17 10:30:58","post_modified_gmt":"2023-07-17 17:30:58","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1956882","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"3","filter":"raw"},{"ID":2138992,"post_author":"254042","post_date":"2023-12-12 02:00:18","post_date_gmt":"2023-12-12 10:00:18","post_content":"","post_title":"Tips for labeling images for object detection models","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"tips-for-labeling-images-for-object-detection-models","to_ping":"","pinged":"","post_modified":"2023-12-19 10:46:42","post_modified_gmt":"2023-12-19 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Learning","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"colorizing-historic-black-and-white-aerial-imagery-using-deep-learning","to_ping":"","pinged":"","post_modified":"2022-01-13 15:04:59","post_modified_gmt":"2022-01-13 23:04:59","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=1319782","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"show_article_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>Enhanced Object Detection using Drones and AI<\/title>\n<meta name=\"description\" content=\"We will demonstrate how drone images and AI provide improved object detection through Pixel Space to Map Space transformation.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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