{"id":1362872,"date":"2021-10-04T18:00:16","date_gmt":"2021-10-05T01:00:16","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=1362872"},"modified":"2022-11-29T13:31:46","modified_gmt":"2022-11-29T21:31:46","slug":"guide-to-streaming-imagery","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/guide-to-streaming-imagery","title":{"rendered":"Your Guide to Sharing Imagery & Raster Data"},"author":6651,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[22931],"tags":[760312,762082,759992,222142,762152],"industry":[],"product":[767992,36951,36551],"class_list":["post-1362872","blog","type-blog","status-publish","format-standard","hentry","category-imagery","tag-arcgis-image-for-arcgis-online","tag-dynamic-imagery","tag-hosted-imagery","tag-tile-cache","tag-tiled-imagery-layers","product-arcgis-image-for-arcgis-online","product-image-server","product-arcgis-online"],"acf":{"short_description":"Understand the difference between map tile cache, tiled imagery, and dynamic imagery.","flexible_content":[{"acf_fc_layout":"content","content":"

Imagery is so much more than a pretty picture, right? We purchase, collect and acquire imagery with the goal of extracting information from it, whether through visualization or analysis. However, it must be shared before you or anyone in your organization can take advantage of imagery. Once you find a place to host your imagery<\/a>, it can be challenging to decide how to share it.\u00a0<\/span><\/p>\n

This article aims to equip you with the information you need when sharing imagery and rasters. Within ArcGIS, you have three ways to share (or “stream”) your imagery and raster data: map tile cache, tiled imagery, and dynamic imagery. But if you’re new to imagery, you’re probably wondering what the differences are, the benefits and disadvantages of each and, which one is best for you. So, let’s dive in\u2026<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"

What is Map Tile Cache?<\/h1>\n

Think of map tile cache as a collection of images tiled together to create a pretty picture, often a basemap. This picture is optimized for performance and designed to scale. What makes this tile service fast is that pre-processing and compression are performed before it\u2019s served. Because each tile is cached and optimized for display, the tiles are drawn immediately as you pan and zoom around the map.\u00a0\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":1366232,"id":1366232,"title":"world-imagery-arcgis-living-atlas-of-the-world","filename":"world-imagery-arcgis-living-atlas-of-the-world.gif","filesize":4731073,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/imagery\/guide-to-streaming-imagery\/world-imagery-arcgis-living-atlas-of-the-world","alt":"Map zooming into the Sydney Opera House.","author":"6651","description":"","caption":"","name":"world-imagery-arcgis-living-atlas-of-the-world","status":"inherit","uploaded_to":1362872,"date":"2021-10-04 23:24:19","modified":"2021-10-04 23:24:41","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":750,"height":419,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","medium-width":464,"medium-height":259,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","medium_large-width":750,"medium_large-height":419,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","large-width":750,"large-height":419,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","1536x1536-width":750,"1536x1536-height":419,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","2048x2048-width":750,"2048x2048-height":419,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","card_image-width":750,"card_image-height":419,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/09\/world-imagery-arcgis-living-atlas-of-the-world.gif","wide_image-width":750,"wide_image-height":419}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/www.arcgis.com\/apps\/mapviewer\/index.html?layers=10df2279f9684e4a9f6a7f08febac2a9"},{"acf_fc_layout":"content","content":"

An example of a map tile cache is the World Imagery Service<\/a> accessible from ArcGIS Living Atlas of the World. <\/span><\/p>\n

Benefit<\/strong><\/p>\n

Map tile cache is highly performant, making it ideal for visualization. When imagery or elevation data is solely intended to serve as a backdrop or basemap, we recommend streaming your imagery as map tile cache. <\/span><\/p>\n

Disadvantages<\/strong><\/p>\n

Although map tile cache receives high marks for performance, please note that what you’re really streaming are “dumb” compressed image tiles. The rich information typically contained within imagery is lost. The tile caching process automatically saves your imagery as 8 bit and projects it to one projection\u2013 typically Web Mercator. What does this mean? Minimal analysis can be performed. A map tile cache doesn’t provide access to spectral bands, image metadata, or the ability to reproject the imagery. That said, if you need to empower stakeholders to do more with imagery than basic visualization, we recommend streaming tiled imagery or dynamic imagery layers.\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"

What are Tiled Imagery Layers?<\/h1>\n

Like map tile cache, tiled imagery layers bring a collection of images together to create a single service or carpet of imagery. However, unlike a map tile cache, the images do not have to be compressed or projected, so more information from the original imagery stays with the streaming service. <\/span><\/p>\n

Benefits<\/strong><\/p>\n

Tiled imagery provides an experience that’s “just right” for visualization and analysis. It’s fast and performant, like map tile cache \u2013 with tiles rendering on the client-side, no server compute required. And, the rich content within the imagery you purchased, collected, or acquired is preserved \u2013 no pixel fidelity is lost. This means you don’t limit the number of bands you can stream; the bit depth stays true to the originating image, and you can obtain pixel values for any location. This lets you perform analysis, set renderings, and visualize different band combinations using a single analysis-ready imagery layer.<\/span><\/p>\n

\u00a0<\/span>Disadvantages<\/strong><\/p>\n

This option is not ideal if you have overlapping, multiresolution, or multitemporal imagery. As mentioned above, tiled imagery layers treat your data like a carpet of imagery with a pre-set projection and pixel size. If you want to stitch imagery together in real-time from different sensors or on different days for a given area, we recommend streaming dynamic imagery layers.<\/span><\/p>\n"},{"acf_fc_layout":"content","content":"

What are Dynamic Imagery Layers?<\/h1>\n

Dynamic imagery is an image collection that’s dynamically mosaicked\/stitched together on-the-fly by the server. As you pan and zoom the map, imagery for an entire area is fetched from the server to cover the specific extent required. It works as follows: for the area of interest, a call is made to the server to pick all the imagery in that area, process them if required, stitch them together and then send those back to the client’s view. The result gives you a “dynamic” view based on the mosaic rules<\/a> you’ve specified, extracted directly from the source imagery.\u00a0<\/span><\/p>\n

Benefits<\/strong><\/p>\n

You can use dynamic imagery layers to analyze overlapping and multitemporal imagery. This image service is great for use cases that require you to step through time and dynamically sort imagery based on attributes, such as cloud cover, date of acquisition, or nadir angle.<\/p>\n

For scenarios where cloud cover is an issue, dynamically choose available imagery without clouds. Or, if performing a change detection workflow, use a dynamic imagery layer to query two images from different dates.<\/p>\n

And if that wasn’t enough, dynamic imagery layers empower you to process the imagery on the server using raster functions before streaming the pixels back to the client. Juhan Yoon showed an example of this during the FedGIS conference.<\/p>\n"},{"acf_fc_layout":"youtube","start_time":"253","end_time":"","youtube_video_url":"