Top Lossy File Types and When to Use Them
- Anvita Shrivastava
- 7 days ago
- 4 min read
Geographic Information Systems (GIS) rely heavily on large raster datasets, including satellite imagery, aerial photography, LiDAR-derived rasters, and scanned maps. These datasets can easily reach gigabytes or even terabytes in size, creating challenges for storage, processing, and sharing.
To manage these large files efficiently, GIS professionals often rely on lossy compression formats. These formats significantly reduce file size while preserving enough visual and spatial detail for mapping, analysis, and visualization.
In this guide, we’ll explore the top lossy file types used for GIS data, their advantages, and when each format should be used—including MrSID, one of the most widely adopted compression technologies for large geospatial imagery.
What Is Lossy Compression?
Through lossy compression, raster datasets can be shrunk by eliminating information that is not very visible to human eyes. In contrast to lossless compression that restores everything after being compressed, lossy compression cannot restore the full dataset after being compressed.
When properly utilized, however, lossy compression can produce raster datasets that are as small as one-tenth to fifty times smaller than their original size but still have excellent visual quality on the majority of GIS workflows.
Advantages of using Lossy Compressed Images in GIS
Dramatically reduced file size
More rapidly rendered and streamed maps.
Lower cost of storage and infrastructure
More effectively distributing large image datasets.
Disadvantages
A loss of image detail
Not useful for scientific analysis that needs to maintain precise pixel values
Redundant compression will cause the image quality to decrease.
For visualization, mapping, and large-scale distribution of image data, lossy image formats are typically the best choice.
Top Lossy File Types
MrSID (Multi-resolution Seamless Image Database)
MrSID (Multi-resolution Seamless Image Database) is a specially crafted raster compression format that was designed to contain large geospatial imagery datasets. MrSID was created to solve the problems created by the challenge of storing and distributing extremely large collections of aerial and satellite images.
Best Use Cases
Statewide aerial imaging programs
Satellite image archives
Large orthophoto mosaics
Remote Sensing datasets
GIS for government and defense
Major Benefits:
High compression ratio. Raster images can be compressed using MrSID at a ratio of 20:1 or greater while achieving very high visual quality.
Multi-resolution access. The MrSID format allows GIS software to gain access to different levels of resolution without loading the complete dataset.
Fast streaming. Large imagery datasets can be streamed quickly over a network, making MrSID an ideal choice for enterprise GIS systems.
Optimized for large raster files. MrSID was created specifically for the purpose of handling multi-gigabyte or terabyte-scale imagery datasets.
Why GIS Professionals Use MrSID
Because traditional file formats are unable to handle large raster datasets, organizations that use MrSID can:
Efficiently store massive imagery archives
Improve GIS performance when viewing large images
Share aerial imagery with many different teams and systems
Reduce the overall cost of storage infrastructure
JPEG 2000 (JP2)
JPEG 2000 is an advanced image compression format that allows for both .... and is commonly accepted in geospatial applications.
Best Uses in GIS
Orthophotos
Large Raster Datasets Used for Maps
Advantages
Greater Compression Efficiency than Traditional JPEG Image Formats
Supports Progressive Image Streaming
Can Store Multiple Resolution Levels in One File
Limitations
Processing May Be Slower in Certain GIS Systems
Not Supported by Older GIS Software
Despite the limitations identified above, JP2 is still a popular format for large image archives and the distribution of satellite data.
ECW (Enhanced Compression Wavelet)
ECW is another widely used lossy compression format designed specifically for large geospatial imagery and aerial photography.
It uses wavelet-based compression to significantly reduce file sizes while maintaining high image quality.
Best Uses in GIS
Large aerial imagery datasets
Orthophoto mosaics
Satellite imagery distribution
Web-based GIS visualization
Advantages
Very high compression ratios
Fast image rendering and streaming
Optimized for large raster datasets
Efficient viewing without loading the full file
Limitations
Encoding may require specialized software
Licensing restrictions in some workflows
ECW is commonly used in mapping agencies, environmental monitoring projects, and enterprise GIS platforms where large imagery datasets need to be stored and accessed efficiently.
When Should You Use Lossy Formats in GIS?
Lossy compression works best when:
Distributing large aerial or satellite imagery
Building web mapping platforms
Storing large raster archives
Improving GIS performance with large imagery datasets
Reducing storage costs
However, if the workflow requires precise pixel-level analysis, such as scientific modeling or classification, lossless formats like GeoTIFF may be preferred.
GIS professionals have a significant challenge in dealing with large raster-based datasets. Lossy compression formats create a viable solution by decreasing the file sizes with little or no degradation in the usable image quality.
Standard loss compression formats, like JPEG and JPEG2000, can be used to visually view GIS data; however, MrSID is an example of an advanced technology that focuses on addressing the needs for large amounts of geospatial imagery datasets.
GIS professionals who work with massive collections of aerial data, statewide image libraries, and satellite image archives will find using MrSID to be an effective way to enhance the storage efficiencies of the image data, improve access to the data, and improve the ability of the GIS system to function effectively when working with large raster datasets.
By carefully selecting the optimal compression format, GIS professionals can effectively optimize their data processing workflow and improve the efficiency of the infrastructure supporting GIS by lessening the cost to access and share geospatial data between groups and/or platforms.
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