{"id":2259322,"date":"2024-04-24T14:00:13","date_gmt":"2024-04-24T21:00:13","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2259322"},"modified":"2024-04-25T06:18:44","modified_gmt":"2024-04-25T13:18:44","slug":"harnessing-the-power-of-imagery-a-programmatic-approach","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/developers\/imagery\/harnessing-the-power-of-imagery-a-programmatic-approach","title":{"rendered":"Harnessing the Power of Imagery: A Programmatic Approach"},"author":333592,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[738191,22931],"tags":[],"industry":[],"product":[36841,36831,36561,36601],"class_list":["post-2259322","blog","type-blog","status-publish","format-standard","hentry","category-developers","category-imagery","product-api-python","product-js-api-arcgis","product-arcgis-pro","product-developers"],"acf":{"authors":[{"ID":333592,"user_firstname":"Yuri","user_lastname":"Potawsky","nickname":"Yuri Potawsky","user_nicename":"ypotawsky","display_name":"Yuri Potawsky","user_email":"YPotawsky@esri.com","user_url":"","user_registered":"2023-02-23 21:40:44","user_description":"Product Manager for Imagery with a focus on Deep Learning, Python, and Analytics.","user_avatar":""},{"ID":309962,"user_firstname":"Alexandra","user_lastname":"Wynn","nickname":"Alexandra Wynn","user_nicename":"awynn","display_name":"Alexandra Wynn","user_email":"awynn@esri.com","user_url":"","user_registered":"2022-05-11 18:42:45","user_description":"Alexandra is a Product Marketing Manager on the Imagery & Remote Sensing team at Esri. She is now working on changing Esri's image analysis story for products like ArcGIS Image Analyst, ArcGIS Excalibur, and features like GeoAI and raster functionality across ArcGIS. Before Esri, Alexandra specialized in CRM, content, and email marketing strategies for top tech companies.","user_avatar":""}],"short_description":"A Developer approach to imagery.","flexible_content":[{"acf_fc_layout":"content","content":"

In a previous blog entry<\/a>, we provided a bird’s eye view upon the large ecosystem of developer-centric tools within ArcGIS for working with Imagery. Building upon that framework, we will further the notion of Imagery as an important medium for Developers.\u00a0 But, where do we start?\u00a0\u00a0 What form would a potential workflow take? With so many different ways to consume and analyze imagery-based data is one environment better than the other (Desktop, Web, or Enterprise)? How do we get from A to Z?\u00a0 Let\u2019s work through it.<\/p>\n

The wider technology community has been moving toward a cloud-native holistic web approach. Within this model client applications connect to data and leverage cloud platforms for data processing, creation, and presentation. We want to essentially decouple the code, compute, storage, and analytics.\u00a0 Here, I outline a potential Esri framework for creating analysis pipelines represented through a series of environments and tools at the developers disposal.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2256022,"id":2256022,"title":"Blog1","filename":"Blog1.png","filesize":30140,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/developers\/imagery\/harnessing-the-power-of-imagery-a-programmatic-approach\/blog1-13","alt":"","author":"333592","description":"","caption":"Creating a Raster Collection from a Stac Resource.","name":"blog1-13","status":"inherit","uploaded_to":2259322,"date":"2024-02-26 20:06:21","modified":"2024-04-03 17:41:49","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":1106,"height":376,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","medium-width":464,"medium-height":158,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","medium_large-width":768,"medium_large-height":261,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","large-width":1106,"large-height":376,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","1536x1536-width":1106,"1536x1536-height":376,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","2048x2048-width":1106,"2048x2048-height":376,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1-826x281.png","card_image-width":826,"card_image-height":281,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog1.png","wide_image-width":1106,"wide_image-height":376}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Starting the Process<\/span><\/h2>\n

Imagine an environment where you open a web browser and have the entire ArcGIS Imagery stack at your disposal.\u00a0 You could visualize imagery from a comprehensive catalog such as the living atlas or a 3rd party API driven one through stac (spatio-temporal asset catalog).\u00a0 The focus is adaptable, ranging from analyzing global scale data with Landsat or regionally utilizing NAIP or Sentinel.\u00a0 You also might determine that a need to search and submit a tasking order to a data provider is needed.\u00a0 This can all be achieved directly within ArcGIS.\u00a0 If we rewind a bit, workflows of this nature traditionally follow a pattern:\u00a0 Search \u2013> Analyze \u2013> Create \u2013> Visualize.\u00a0 Let’s begin by searching for data from a variety of locations.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2256112,"id":2256112,"title":"Blog2","filename":"Blog2.png","filesize":140243,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/developers\/imagery\/harnessing-the-power-of-imagery-a-programmatic-approach\/blog2-6","alt":"","author":"333592","description":"","caption":"Filtering Services Based Imagery Data","name":"blog2-6","status":"inherit","uploaded_to":2259322,"date":"2024-02-26 20:23:09","modified":"2024-04-08 20:11:39","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":1220,"height":745,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","medium-width":427,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","medium_large-width":768,"medium_large-height":469,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","large-width":1220,"large-height":745,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","1536x1536-width":1220,"1536x1536-height":745,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","2048x2048-width":1220,"2048x2048-height":745,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2-761x465.png","card_image-width":761,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/04\/Blog2.png","wide_image-width":1220,"wide_image-height":745}},"image_position":"right","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

In a traditional desktop or web client, tools can be used to search or discover data that adheres to specific requirements. Think about only viewing images with less than 10% cloud cover, from a specific sensor platform, that were collected on a specific date in time.\u00a0 Accessing data with a code-first approach allows us to define a query that ensures the returned data meets these conditions.\u00a0 This can also be extended to the confines of an area of interest. All with a few lines of code!<\/p>\n"},{"acf_fc_layout":"content","content":"

Analyze and Create<\/span><\/h2>\n

Once you have accessed this data via a services first approach, dozens of raster functions can be applied in-memory to quickly understand band information, carry out band math, or run vegetation indices. This I\/O can all be done on-the-fly without installing software based upon your need and use case.\u00a0 Not only can you quickly study and visualize imagery-based data, but you can also pair that with compute resources in Esri\u2019s cloud.\u00a0 This allows you to run dozens of processes against that imagery data while leveraging Raster Analytics. Some compelling methods available via this path are:<\/p>\n