Design methodology for a raster database

Designing and implementing a GIS using raster data is no different than it would be for any other GIS. The only difference is that you use raster data instead of, or in addition to, vector feature data.

When working with raster data, your workflow could be the following:

Identify purpose or objective

You can use raster data in either one or both of the following ways: for display purposes, and for analysis analysis purposes. Raster data for display is quite common, especially using orthophotos as a background for a map. Raster data for analysis can be implemented in many ways, such as: a watershed analysis or terrain analysis, updating some topographic features in other datasets, or updating land-cover classes to assess the location of a new housing development.

Identify the data

If you’re looking to extract information from imagery, consider the resolution you require and whether you need one or more spectral bands. You might consider whether the data comes from an aircraft or satellite. If you’re going to work with elevation data, you might consider the most appropriate methods for collection, such as lidar , contour lines, or radar interferometry. If you intend to create a collection of scanned maps, you need to identify what those maps are, such as scanned documents, CAD drawings, or topographic maps.

Refine the requirements

Determine more detailed requirements based on the following:

Acquire and review data

This can involve placing orders for the data with a company capable of providing it, scanning the maps you need, or acquiring the source data and building the corresponding raster datasets. It is important that you have a system for checking the quality of the data, whether created in-house or acquired from outside sources. You might have to check for missing data (such as dropped lines or pixels), for poorly represented data, or whether the data is georeferenced for your area of interest.

Prepare the data

Building the database could require the prior extraction or conversion from one data format to another, such as from lidar elevation points to a DEM. It could also involve some preprocessing, such as georeferencing or rubber sheeting.

Design and build the database

This could involve one of several choices:

Additional considerations include which compression method to use, whether to use a personal geodatabase or a multiuser geodatabase management system, and what your data dissemination will include. For example, if you will be serving your imagery, you might consider a mosaic dataset since it is optimized for this type of dissemination.

You will need to create some level of metadata, depending on your intended distribution and access to the data. For example, what kinds of queries should users expect to perform to find your raster data over the web? If using raster catalogs or mosaic datasets, you might consider additional catalog fields to allow more extensive querying capabilities.

Deploy and maintain the geodatabase

One of the main reasons for going through this entire loading process is to enable many people to use the data for various purposes and projects. This requires administration and management.

In most situations, you will plan on reusing your dataset or database. You will need to plan for updates, modifications, and the ability to build on your initial implementation.

Database fragmentation and frequent data manipulation may increase the size of your mosaic dataset dramatically. If your database size is inflated due to constant transactions, you should run the Compact tool.

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