Software and Data Mosaic datasets represent a data model in the geodatabase. In this model, participating rasters may be accessed either as a dynamic mosaic composed of images processed on the fly or as a catalog with tables containing geometry and metadata. This provides multiple options for organizing/managing raster data. Functions can be applied to each input raster added to the mosaicked dataset to define how it is processed when creating the mosaicked image on demand. A mosaic dataset may also have associated mosaic methods that define the default ordering of the imagery. In addition, creating referenced mosaic datasets allows the generation of different sets of products, each with specific mosaicking methods and mosaic dataset functions, from the same source. A referenced mosaic dataset behaves like a regular mosaic dataset, but the records that define processing of individual rasters or metadata cannot be edited because they are referenced from the source mosaic dataset. For example, you could create a mosaic dataset to manage all your digital elevation model (DEM) data, then create a referenced mosaic dataset to produce a hillshade and another reference mosaic to define slope. Updating the elevation data automatically updates the hillshade and slope. Other patterns for managing very large image collections can involve creating a mosaic dataset that uses other mosaic datasets as input. Mosaic dataset properties Catalog/Library/Collection of  Imagery  Properties of the imagery  Associated image metadata  Raster processing functions Geodatabase storage Scalable References original pixels as files or database Authored in ArcGIS Desktop Geoprocessing tools and ArcObjects for automation Accessible as  Dynamically mosaicked image, processed on the fly  Catalog table with geometry and metadata On-the-Fly Processing Because mosaic datasets are dynamically mosaicked and processed on the fly, the processes for a mosaic dataset are transactional and executed on demand in contrast to traditional methods for handling rasters that deal with image processing and image mosaicking as separate and linear steps. When an image is required by a client application, the image processing functions and dynamic mosaicking are executed on the fly and the result served to the client. Essentially, the imagery is processed as it is accessed. Mosaic datasets are aware of spatial and temporal information that is maintained as attributes of the source raster datasets. Thus, a mosaic dataset can easily handle data with varying resolutions (e.g., spectral, spatial, temporal, and radiometric). Mosaic dataset Mosaicked images Source images Processed image There is no loss of pixel data or metadata when using mosaic datasets because the source pixels are never altered or converted. There is no loss of pixel data or metadata when using mosaic datasets because the source pixels are never altered or converted. Users have access to the mosaicked image as well as the source data. Consequently, no data is lost when using overlapping datasets; all information in the imagery is preserved. Users can reorder imagery to ensure the most appropriate image is on top. This dynamic handling of overlapping imagery differs greatly from the traditional approach to processing and mosaicking imagery into new products that must be stored and maintained and results in significant storage requirements and information loss. Creating a Mosaic Dataset A mosaic dataset consists of a footprint feature class that acts as a catalog that details the extent of each raster and references the source pixels along with properties, metadata, and processing functions. It includes a boundary feature class that defines the extent of the mosaic dataset and property pages that reference default mosaicking rules and other properties defining how imagery and metadata are accessed. A mosaic dataset also contains a table for logging data loading and other properties. Optionally, it can contain a seamline feature class for seamline mosaicking. Creating a mosaic dataset, even for terabytes of preprocessed imagery, is a straightforward process that can be as easy as pointing the system to the source directory. Note that mosaic datasets can also handle more complex data obtained directly from different satellite and aerial sensors. Advanced, sophisticated mosaic datasets can be generated that fuse imagery from multiple sources and sensors based on the decisions and considerations made while creating and modifying the mosaic dataset. These considerations include choosing mosaic dataset properties, mosaic methods, and mosaic dataset functions as well as the use of referenced mosaics and mosaic datasets that use other mosaic datasets as their source. Continued on page 10 www.esri.com ArcUser Summer 2010 9