ArcGIS enables you to work with a wide variety of imagery acquired from different sources. These image management workflows provide best practices for managing large collections of imagery to make the imagery quickly and efficiently accessible. The image management workflows are described in the Image Management Guidebook which begins with a general overview of all aspects related to image management, followed by workflow-specific sections regarding best practices for all workflows listed below.
The following additional components unique to each workflow are referenced below:
Following are the workflows that are currently being documented. If you have questions, please do check out the corresponding FAQs and post questions (and answers) to Forums, and alternatively let our team know by sending email to ImageManagementWorkflows@esri.com.
Manage and share collections of high resolution satellite data.
This workflow is for imagery from high resolution satellites (resolution < 5m) such as Ikonos, GeoEye, QuickBird, Worldview, Pleiades, etc. This data is typically multispectral, resulting in different band combinations, such as pan-sharpening, RGB, false color IR, and NDVI. This workflow addresses the basic satellite products (typically "1B") which require georeferencing using a rational polynomial coefficient (RPC) based sensor model. The multispectral aspect of the imagery is important and users often utilize the additional bands to extract information.
where you will find user-modifiable python geoprocessing tools for managing and sharing high resolution satellite data.
This workflow is for large archives of historical aerial imagery, often spanning many decades, and takes into account aspects such as the varied metadata and imprecise image location, often extracted from hardcopy photo indices. These images have significant value for change monitoring when they become accessible and are georeferenced for comparison to newer imagery.
Preprocessed orthophoto imagery generally exists as sets of processed tiled images, either 3-band color or color infrared imagery, with more recent collections including 4 spectral bands (RGB + Near IR). The "preprocessed" name implies the imagery is ready to use (orthorectified and typically color corrected) without further processing, but workflows are still required for managing and sharing the data with end users. Organizations faced with managing this data often have multiple image collections, with different resolutions and multiple dates, which may also be stored in different projections and data formats. This workflow discusses the different formats that are most typical, and provides examples for managing multi-year collections, in two different modes (i.e. "Always show most recent data on top" as well as "Use time slider to select imagery by year").
This workflow addresses management of imagery from medium (10-30m) and low resolution (>30m) satellites, such as Landsat and Sentinel-2. This imagery is commonly used for regional or country-wide assessment and monitoring. Typical applications include agricultural or environmental monitoring. A significant value of images from these satellites are the multispectral bands that support a wide range of applications, which rely on analysis utilizing the different spectral bands. Aspects such as atmospheric correction are of higher importance. By following this workflow, the image services with imagery from these satellites can be used for fast visualization and analysis.
This workflow is focused on managing image files that represent scanned hardcopy maps (topographic & geologic maps, engineering drawings, etc.), addressing the unique challenges of data in this format. Issues include different methods for georeferencing the scanned images, ingesting metadata which may come in a variety of different formats, and viewing/hiding data in the borders of the map.
This workflow is designed for managing massive collections of images, most applicable to companies that are data providers (satellite or aerial) with a need to efficiently catalog and browse thumbnails and metadata, and select images to be retrieved from long term storage.
This workflow describes organization and sharing of elevation data from different sources in multiple resolutions, and will need to be maintained over time as new data (e.g. from Lidar) is acquired. Derived visualizations such as hillshades may be generated on the fly.
Elevation or terrain data is used for many applications—in some cases to directly extract information, such as computing viewsheds, contours and profiles—and also for visualizing topography and as input for other processes, such as orthorectification. Organizations often obtain publically available elevation data and then supplement it with datasets procured off-the-shelf or custom datasets collected for the organization via contract.
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The workflows for the following imagery types will be released soon: