Imagery data management system

An imagery data management system is designed for cataloging and serving large collections of imagery, Lidar, elevation, multidimensional, oriented imagery and/or video at any scale in both 2D and 3D contexts. This system supports cataloging, querying, loading data models and rendering imagery for enterprise use cases, with access through web services and on-the-fly processing supporting visualization, exploitation, and analysis. This system efficiently unlocks information for use across the many needs of an enterprise organization, meeting users where they are while supporting a vision to scale further into new cloud capabilities.

An imagery data management system pattern adds business value to an organization, by:

• Managing the organization’s imagery collections as enterprise assets.
• Providing users with direct access to extensive catalogs of imagery data for query, data access and visualization.
• Regularly and seamlessly updating source data collections as well as derived data and information products using automation.
• Supporting reality capture workflows, providing the foundation for comprehensive digital twins.
• Applying pre-built analytical models including machine learning and geospatial AI to data sources and extending them with Python tools.
• Using imagery data to extract features, detect objects, segment images and work with computer vision and populate databases of assets across different levels of detail.
• Monitoring time-enabled archives of data to identify changes in features, landscapes and scientific measurements.
• Enabling customized, interactive, dynamic analysis against diverse datasets, from various providers and at local or global scales.
• Cataloging, managing and streaming video assets in a geographic context, for historical review, security operations, mensuration and exploitation.
• Incorporating, accessing, and working with data from a variety of providers, sensors, platforms, formats and modalities, including open data sources such as AWS Open Data and the Microsoft Planetary Computer.

If you’re new to ArcGIS system patterns, review the introduction first.

User personas and workflows

The user personas who most commonly interact with an imagery data management, along with the types of workflows and tasks they typically perform using this system, include:

• Imagery asset manager. Imagery asset managers work with large and growing catalogs of imagery, either created within the organization or acquired from partners or content providers.  Managers usually have deep experience with imagery products, consumption patterns and data considerations.  
• Imagery scientist. Scientists work directly with imagery data – pixels, point clouds and 3D meshes, among other types, usually with an interest in more direct access to less-finished versions of products. They may be developing new rendering or analysis algorithms, assisting with designing data models, and support other roles who work with these datasets.
• Data analyst, scientist, and engineer. Data scientists work with imagery data in an analytical capacity, often accessing it from a specialized science package or using a notebook-style analysis tool. They generally access imagery through catalogs or search interfaces, and work with web services as well as directly with data in object storage.
• GIS analyst and GIS professional. GIS professionals tend to interact with imagery data management systems by consuming image services, 3D meshes or scenes, and combining that data with other, vector-based geospatial data from external providers or other ArcGIS systems. They may create and publish map products or applications, or use data to support other geospatial analyses.
• General user. General users of imagery data systems, including public consumers, typically work with well-defined, finished basemap-style services, but can also interact with image services that provide an analytical experience through raster function templates or dedicated viewing applications.
• Drone operator. Drone operators provide inputs into the imagery data management system, either as raw or pre-processed drone flight data is captured. The drone operators may also interact with data in the system to understand gaps in coverage, to task new flights, or to plan missions based on other data coverages.

Esri maintains an extensive library of imagery workflows including detailed guides and tutorials related to imagery data management, reality mapping, analysis and AI, visualization, exploitation, as well as content. This website provides technical resources, sample workflows and additional learning paths as well.

Applications

There are many applications and experiences provided by ArcGIS, and many of these are deployed and available as part of an imagery data management system. The applications most commonly used by the personas above to interface with this system and perform workflows are described below.

• The portal website is the general web interface into ArcGIS systems and supports a wide variety of use cases for viewers, editors, creators, professionals, as well as administrators. In imagery data management systems, the portal website commonly serves as a discovery and collaboration portal, as well as a viewing and analytics experience for some users. It is also commonly used by data owners and stewards for data management.
• The Map Viewer and Scene Viewer applications that are part of the portal website are used for key workflows in the imagery data management system, including authoring raster function templates, creating new image service outputs, performing analysis processes and reviewing the results of image processing.
• ArcGIS Excalibur is an imagery and video exploitation app for creating geospatial intelligence products based on imagery. ArcGIS Excalibur is available with ArcGIS Enterprise as a web application that works with image services and other capabilities of this deployment.
• Python Notebooks are another common application interface for working with the imagery data management system. Notebooks can be accessed through many patterns, but ArcGIS provides them in both ArcGIS Pro and ArcGIS Online or Enterprise. Notebooks provide an interactive code experience using Python or another language, and ArcGIS SDKs can help provide ready access to imagery data for viewing, analyzing and processing, or working with deep learning and machine learning models, for which notebooks are the primary means of access and execution.
• ArcGIS Field Maps is an all-in-one mobile application available for Android, iOS, watchOS, and Windows devices. In data editing and management systems, ArcGIS Field Maps is commonly used for map-centric data collection, typically performed in the field with or without network connectivity. Imagery data is used in Field Maps to provide key background information, but also can work offline when published as a tiled imagery layer. For information on offline (disconnected) data collection, see the integration and support considerations for the Mobile Operations and Offline Data Management pattern.
• ArcGIS Experience Builder is a configurable, no-code application builder used to create web applications. In imagery data management systems, ArcGIS Experience Builder is commonly used to create focused viewing and imagery data interaction web applications.
• ArcGIS Pro is a desktop application used by GIS professionals for a wide variety of use cases, including viewing different data formats, geoprocessing and data model preparation. ArcGIS Pro provides the most flexible and feature-rich experience for working with imagery data and is typically used by GIS professionals and other expert roles. The 3D Analyst, Image Analyst and ArcGIS Reality extensions for ArcGIS Pro bring significant capabilities, tools and workflows to this application. ArcGIS Pro is also extensively commonly used by these personas for data management, data model creation, and raster function authoring.
• Deep Learning Studio is a web app that provides a project-based environment for deep learning image analysis workflows. Users can collaborate to train deep learning models and run inferencing at scale against their managed imagery.
• ArcGIS Reality is a product family including ArcGIS Reality for ArcGIS Pro, ArcGIS Reality Studio, Drone2Map and ArcGIS Flight, which support workflows for processing digital imagery products from data captured by drones, fixed-wing aircraft, and satellites. These tools are available in ArcGIS Pro along with dedicated desktop apps like ArcGIS Flight and online experience through ArcGIS Online and ArcGIS Enterprise.
• Custom applications built with mapping APIs and SDKs are often used to interact with imagery, services and datasets to enable specific workflows or support analytical or summary operations.
• Additionally, most other ArcGIS applications are able to interact with imagery layers when they are configured in a web map or scene, including popups, dynamic rendering and other common imagery layer workflows.

For more information on the full spectrum of applications provided by ArcGIS, see the application architecture in the ArcGIS overview.

Capabilities

The primary capabilities provided by an imagery data management system are introduced below, including both general capabilities as well as industry or workflow-specific capabilities and solutions. Capabilities used in imagery data management workflows, but typically provided by other systems, such as basemaps, geocoding, and other location services provided by a location services system are not listed below.

General capabilities

• Imagery visualization and analysis allows users to interact with imagery data as a basemap in an application, through dynamic image overlays, by navigating through collections of historic imagery, or collect observations based on a recent drone flight. Enhance imagery through dynamic adjustments, stretching, and changing band combinations. Imagery rendering is optimized to show the requested area of interest and re-apply rendering rules on each pan and zoom. Use geoprocessing tools, algorithms, and functions to analyze imagery data, to assess land use, monitor activity and change, measure damage, and assess environmental factors.
• Data modeling and structuring creates standardized approaches to add large sets of data into common data models such as mosaic and LAS datasets, raster products and sensor models, oriented imagery catalogs, or other industry-specific or use case-specific models such as trajectory data. Create catalog datasets and interact with catalog layers of assets in local or networked storage, or add items and services from an ArcGIS Online or ArcGIS Enterprise organization. These models help to organize, provide metadata about, and enable the usage of these detailed datasets.
• Imagery data publishing allows users of all types to create and host collections of imagery and other remotely sensed data sources. Publish imagery collections and products as dynamic or tiled services at local or global scale, which can be visualized and interacted with using web, mobile, and desktop applications.
• Tiled imagery layer hosting enables access to full bit depth pixel data with fast performance using standard tiled visualization methods including dynamic client-side rendering. Create multidimensional layers with time slices and identify change over time while accessing multiple bands of pixels with reduced-resolution rendering at small scales.
• On-the-fly raster analysis relies on using raster functions and combining a set of those functions into raster function templates to quickly combine bands, compare imagery, and analyze values through collections of images to create a dynamic output image. Raster functions are applied at request-time, are only applied to the requested pixel area, and represent an efficient way to dynamically render imagery without reprocessing an entire dataset.
• Elevation analysis provides capabilities to generate contours, run hydrological models, view and delineate watersheds, and view terrain, slope and aspect renderings of detailed datasets. Complete volumetric analyses by cutting and filling or comparing 3D surfaces and datasets. Combine elevation from different sources, at different resolutions, and prepare a seamless elevation service that can be used for direct display or as the basis for a 3D rendering of a city or regional area. Esri also provides ready-to-use elevation services for visualization and analysis requests.
• Distributed raster analytics jobs can be authored to run raster function calculations across massive imagery holdings in a distributed computing model. These operations may also include inferencing using trained deep learning models, or creating new output data products based on a predefined renderers or calculation. Data from raster analytic jobs is persisted through the image hosting capability of ArcGIS Enterprise.
• Image extraction capabilities allow dynamic and programmatic export and download of source and mosaicked imagery data for use in other applications or as image chips in deep learning workflows. Extraction can provide access directly to the source pixels or create a new, resampled image at a specific resolution for a requested extent. ArcGIS also supports extraction of areas of the World Imagery basemap for use offline in disconnected data access and editing workflows.
• Deep learning and AI are embedded throughout ArcGIS and imagery data management systems. Users can train and run inferencing on deep learning models using imagery assets and local compute resources or scaled across large systems including cloud resources and services. ArcGIS Living Atlas also contains a gallery of pre-trained models that are available for direct use or can be adapted to an organization’s specific workflows, data or geography.
• Multidimensional data can be explored using standard scientific formats such as NetCDF, GRIB, HDF, and Zarr. These data display variables such as change over time or measurements at different atmospheric altitudes or depths. ArcGIS includes dedicated user interfaces in ArcGIS Pro and the ArcGIS Map Viewer to quickly display time slices, build and display complex multivariate symbols, or identify available variables, build a new calculation of your own, or predict variables outside the time extent of the dataset.
• Work in image space and perform image mensuration tasks and visualize imagery as it was captured from the sensor, along with traditional ortho views and stereo viewing. Image space analysis can also be used to collect features, view details without resampling, and prevent distortion.
• Use stereo viewing capabilities to visualize imagery in 2.5D, conduct image mensuration tasks, manually digitize and extract features with high precision and 3D object potential. Used frequently in photogrammetry workflows, stereo editing is primarily available in ArcGIS Pro.

Industry-specific capabilities and solutions

• Work with oriented imagery of various types, including oblique, bubble or spherical imagery, 360-degree panoramas, street-side, and inspection imagery. These datasets are not traditional nadir images but can have significant value to organizations through workflows like security investigation, asset inspection or data collection. Oriented imagery capabilities in ArcGIS include a structured data model, a dedicated viewer application and support for serving and working with oriented imagery in a variety of applications.
• Support drone operations from fleet management to specific mission planning and on-the-ground data processing, using an array of web, desktop and mobile apps and tools. ArcGIS Dashboards can be used to monitor collection progress, identify operational issues, and manage reporting to data processing and quality teams.
• Reality mapping incorporates extensive ortho mapping capabilities for high-fidelity product generation. Use drone and other aerial imagery to create full-resolution digital surface models, True Orthos, oriented imagery catalogs, 2D surface meshes, dense 3D point clouds, and photo-realistic 3D meshes. Reality mapping capabilities are available in web, desktop and server-based processing patterns.
• ArcGIS supports indexing, searching, publishing, and streaming of video as a service with geospatial and temporal context. Share video content from sources such as drones, security cameras, sensors, handheld devices or other motion imagery sources, and seamlessly integrate video as another spatial data source in existing GIS workflows, information products, and stakeholder briefings.
• Manage, visualize, and analyze Lidar datasets including a variety of data formats, to understand surface conditions, identify different levels of intensity, layers of return points, extract features, classify point clouds, work with photo-realistic colorization, and create derivative products. Manage large sets of Lidar files as one continuous layer using a LAS dataset.
• Work with synthetic aperture radar by accessing collections of imagery from SAR sensors and platforms. ArcGIS includes SAR-specific raster types, raster functions and visualization approaches that support this unique and powerful data type.
• Work with Spatio-temporal Asset Catalogs (STAC) to connect to existing catalogs of imagery and search, filter, and parse records to identify the proper data for a project. Use the STAC connection and search experience in ArcGIS Pro, the arcpy Python module and the ArcGIS API for Python to query public and private STAC catalogs and directly access assets through cloud data connections.

Content

With an imagery data management system, organizations can access ready-to-use, curated content from Esri and our partners, including:

• ArcGIS Living Atlas of the World. ArcGIS Living Atlas provides ready-to-use imagery from authoritative sources, including Landsat, Sentinel, NAIP, NOAA, and more. This collection offers options for true color, false color, multi-spectral, and multi-temporal imagery. It also provides a global basemap built from recent satellite imagery and high-resolution aerial imagery. This gives organizations access to a wide range of imagery that they can apply immediately.
• Premium imagery content. Esri partners with leading satellite and aerial image providers (like Airbus, BlackSky, Maxar, Nearmap, Planet, and Vexcel) to deliver their data in ArcGIS. Users can acquire premium imagery from an existing collection, or task image capture sessions and deliver the data directly to ArcGIS. This makes it easy for customers to acquire new imagery and access already-collected data. The ArcGIS Marketplace also offers dedicated imagery data content for purchase from Esri partners.
• External Libraries and Catalogs Esri users can also connect to compatible catalogs and libraries of data such as the Microsoft Planetary Computer which provide access to open science datasets from missions and sensors of various types. These sources can be used to access historical records dating back decades, and allow for direct access to data through cloud-optimized formats and either open or requester-pays access methods.

Users of this system can interact with this content in various ways, using it for visualization, as inputs for analysis, or even combining with their own data to create new products and experiences.

Architecture considerations

Considerations specific to imagery data management systems are described below. Learn more about general integration and support considerations in the ArcGIS architecture.

• Imagery data management systems are highly dependent on decisions related to how (and where) imagery data is stored. Most clients access imagery through web services or directly as cloud-optimized data, which allows for a central storage strategy with dispersed clients. For any imagery data management system, placing the compute and image rendering as “close” (in network proximity terms) to the imagery data as possible is critical – to allow for rapid rendering and improve client experience while minimizing data egress costs. While accessing cloud-based imagery from a local desktop is technically feasible, using a dynamic image service will provide a better and more responsive user experience in most cases.
• Also relevant are the formats of imagery data sources and the volume of imagery data the organization is managing or plans to manage in the future. Imagery data file formats have a significant impact on the performance of rendering as well as the size of data on disk, which impacts storage cost. In some cases, there is an inverse tradeoff where increasing compression can save on storage costs but reduce performance when the imagery is accessed – this balance should be carefully assessed to make decisions based on your system requirements. The volume of data, and the future growth of that data as new collections are incorporated, is another factor that can guide decisions about how, and where to architect and deploy this system or taking an approach that involves multiple systems for different purposes.
• Running large image analytics jobs such as raster analytics operations or deep learning inferencing requires careful planning for compute resources, analytical approach, and distributed computation. The ArcGIS raster analytics technology can assist with distribution of the job or jobs across compute nodes, but the analytical process should be carefully planned, tested, and understood before it is implemented.

For more detailed architecture considerations, see selecting a deployment pattern.

Examples

Industry-specific system examples for this system pattern include:

• Conservation. Conservation organizations are charged with the care, protection and management of land and buildings. The imagery data management system pattern supports the dissemination of imagery to stakeholders across conservation organizations. The National Trust reduced the need to manually generate a document or map by using ArcGIS Image for ArcGIS Online to share imagery with non-GIS users in the organization via web apps in a browser.
• Transportation. State transportation agencies typically have substantial imagery assets that can be of limited value without an imagery data management system. For example, UDOT used the system pattern to support the management of right-of-way property of land the owned by the state.
• Utilities New Jersey American Water is an example of an organization using the system pattern to support an unmanned aircraft system (UAS) program. The UAS program produces basemaps, 3D maps, and topography that supports inspections, disaster response, and security. ArcGIS Image Server serves the basemaps to other GIS maps within the utility.
• Natural Resources Esri startup partner Pollen Systems uses the imagery data management system pattern to support vineyards, orchards, and nurseries to improve their decision making. The use of ArcGIS Enterprise and ArcGIS Image Server allows Pollen Systems to disseminate large collections of overlapping, multi-resolution imagery and raster data from different sensors, sources, and time periods.
• Commercial Satellite and Aerial Imagery Organizations The imagery data management system pattern supports commercial satellite and aerial imagery organizations that provide services to their customers. The system pattern supports the processing of drone and/or satellite images and combining results into either continuous coverage service, regional service, or customer-specific service that can be delivered to end-customer users. For example Skytec uses ArcGIS Image for ArcGIS Online to support UAS work for land conservation.