Mosaic datasets—ArcGIS Pro | Documentation (2024)

Mosaic datasets are used tomanage, display, analyze, serve, and share imagery and raster data. When you create anew mosaic dataset, it is created as an empty container in thegeodatabase with some default properties to which you can addraster data. You must have write access to thatgeodatabase.

A mosaic dataset consists of many parts:

A catalog that providesthe source of the pixels and footprints of the imagery and rasters
A feature class thatdefines the boundary
A set of mosaicking rulesthat are used to dynamically mosaic the rasters
A set of properties usedto control the mosaicking and image extraction
A table for loggingduring data loading and other operations
Optionally, a multidimensional table defining variables and dimensions
Optionally, a seamlinefeature class for seamline mosaicking
Optionally, a stereo table defining stereo models
Optionally, a colorcorrection table that defines the color mapping for each raster inthe table

You can create a mosaic dataset in a geodatabaseand can add raster datasets to it directly, or you can create it using a selection from an existing mosaicdataset. A mosaic dataset manages its raster data in a table, where datasets are indexed and you can perform queries onthe collections. There are geoprocessing tools in the Mosaic Dataset toolbox to create and edit mosaic datasets.

The imagery in a mosaic dataset does nothave to be adjoining or overlapping but can exist as unconnected,discontinuous datasets. For example, you can have images thatcompletely cover an area, or you can have many strips of images thatmay not join together to form a continuous image, such as alongpipelines.

Mosaic datasets—ArcGIS Pro | Documentation (1)

Mosaic datasets—ArcGIS Pro | Documentation (2)

To learn how to create mosaic datasets, see Creating Mosaic Datasets.

Types of mosaic datasets

There are two types of mosaic datasets: one allows you to add in your data, and the other allows you to reference another mosaic dataset.

When you create a mosaic dataset, you can add all types of image and raster data and modify theproperties and functions applied per raster and on the mosaicdataset. This is created using the Create Mosaic Dataset tool. There are no limitations on this mosaicdataset.

When your mosaic dataset only references another mosaic dataset, it is created usingthe Create Referenced Mosaic Dataset tool. The referenced mosaic dataset behaves similarly toa regular mosaic dataset but is read-only. For example,you cannot add additional images to the mosaic dataset, you cannotbuild overviews, and you cannot calculate the pixel size ranges. Itis used to serve conventional mosaic datasetswith different mosaic dataset-level functions. For example, youcan create a mosaic dataset to manage all your DEM data andcreate a referenced mosaic dataset to produce a hillshade or aslope product from the source mosaic dataset. Or, you can use a reference mosaic dataset to subset a mosaic dataset by defining a clip boundary or an attribute query such as aparticular date or type of image. Sharing access to a referencedmosaic dataset also ensures that those accessing it cannot makemodifications to the source mosaic dataset, which can impactother users.

Mosaic datasets and raster types

Mosaic datasets use raster types to readand absorb the required information from raster datasets. Itidentifies metadata, such as georeferencing, acquisition date, andsensor type, along with a raster format. A raster type can readraster data using a raster format, suchas TIFF or JPEG. The Raster Dataset raster type reads all rasterformats; however, you may want to use a specific raster type thathas been created specifically to read and display the pixel dataand apply the spatial reference associated with specific rasterdatasets. Also, raster function processing chains can be automatically applied to specific raster types to perform on the fly processing on the imagery, such as NDVI processing of imagery containing red and near infrared bands.

Many specific raster types have sophisticatedcapabilities and can recognize that a particular image, such as oneprovided by a satellite imaging company, includes a raster datasetwith several bands, varying spatial resolutions, and other metadatathat affect the spatial reference. Therefore, if the product hasfour bands of data at a 1-meter resolution and one band with a30-centimeter resolution, the raster type creates a product thatsharpens the lower-resolution data with the higher-resolutiondataset (also known as pan-sharpening). Additionally, if thecorrect rational polynomial coefficient (RPC) information isprovided, you can use this raster type to improve the fuseddata product by performing an orthorectification. Using thecorrect raster type, you can automatically define functions thatare applied on the fly when the raster datasets areaccessed.

Apply raster functions with mosaic datasets

Raster functions are a key component to every mosaic dataset. They allow the mosaic dataset to deliver a dynamically mosaicked image, and you can use them to enhance the mosaicked image product by applying on-the-fly processing operations such as orthorectification, image enhancements, and image algebra. You can add functions to the mosaic dataset or to individual images in the mosaic dataset, or they may be added when the data is added to the mosaic dataset. For example, when you add specific raster data products (such as from a satellite sensor) to a mosaic dataset, some functions are automatically added to the raster data. As mentioned above, you can add raster datasets that are used to generate an orthorectified, pan-sharpened image. To generate this image, both a pan-sharpen function and orthorectification function are applied to the raster data when it is accessed. This is advantageous, because it saves disk space, since you aren't required to store both source and preprocessed datasets. Additionally, if you wanted to process the same data differently, you can add the same data to a different mosaic dataset and apply different functions. You may still want to use the orthorectification function, but you may want to generate a vegetative index. For this, you can use the Band Arithmetic function or the NDVI function.

Note:

When you add Landsat 8 Surface Reflectance and ARD data to a mosaic dataset, a function is available in the raster dataset properties to add a mask. This mask allows you to mask out unwanted image data values that can affect subsequent raster functions such as negative values.

Manage multiple resolutions with mosaic datasets

Mosaic datasets are designed to handle datawith varying resolutions—spectral, spatial, temporal, andradiometric. The raster types and functions in a mosaic datasetplay a strong role in how all this data is handled and displayed.Additionally, the mosaic dataset is particularly aware of thespatial and temporal information as attributes of the raster data.Based on the pixel sizes, the mosaic dataset displays theimagery at the most appropriate scales. With some additionaldisplay control properties, called mosaic methods, a user cancontrol the temporal information, allowing them to view the imagesfor the dates they require.

You can set a mosaic method that controls what image data is presented each time a mosaic dataset is displayed, roamed or zoomed. For example, the mosaic is generatedby displaying the dataset that is the closest to the centerof the image. Another mosaic method allows you to define a query basedon attributes such as acquisition date or cloud cover. These mosaicmethods and querying capabilities allow users to access everyraster dataset within the mosaic dataset, even when there isoverlap.

There is no pixel data loss or metadata losswhen using mosaic datasets, as the source pixels are never alteredor converted, and the files are never moved; therefore, metadata files remain in their location. Because the mosaic datasetdoes not alter the source data or its location, the pixel valuesare not altered. Additionally, the mosaicking performed by themosaic dataset occurs dynamically when the mosaic dataset is accessed.Users have access to the mosaicked image as well as the sourcedata; therefore, there is no data loss occurring for overlappingdatasets.

Overviews, which are similar to raster pyramids, canbe generated for a mosaic dataset. Overviews are reduced resolutiondatasets that are generated to improve the speed at which themosaic is displayed. You can allow the default overviews to begenerated over the entire mosaic dataset. Alternatively, you cancontrol how they're created by defining the downsampling ratio, theextent, a specific spatial resolution, and so on.

Advantages of mosaic datasets

Mosaic datasets are excellent data models forstoring and managing data. Mosaic datasets are ideal fordistributing data because they can be directly accessed by usersand served. A server administrator can modify manyproperties of a mosaic dataset—such as the maximum image size, thelevel of metadata, the compression method, or the maximum number ofdownloads—to get the maximum performance out of their server andmeet user needs. When clients connect to a server to see themosaicked image, their application can control the same mosaicmethods and other properties that a directly connected user can, along with the ability to select raster datasets and downloadthem to their local disk. Not only does a mosaic dataset manage andvisualize data, it is also a tool for disseminatingimagery.