GeoTIFF is a widely used format for storing:<\/p>\n\n\n\n
satellite images<\/li>\n\n\n\n
orthophotos<\/li>\n\n\n\n
digital elevation models<\/li>\n\n\n\n
raster maps<\/li>\n<\/ul>\n\n\n\n
However, when a file is stored on a remote server, a problem arises.<\/p>\n\n\n\n
Suppose we have a 3 GB raster.<\/p>\n\n\n\n
If software wants to display only a small area, it often has to:<\/p>\n\n\n\n
download a large portion of the file<\/li>\n\n\n\n
read the necessary blocks<\/li>\n\n\n\n
reconstruct the image.<\/li>\n<\/ul>\n\n\n\n
This works well locally, but becomes inefficient when the data is accessible via HTTP or in the cloud.<\/p>\n\n\n\n
This becomes problematic when the data is stored:<\/p>\n\n\n\n
on a remote server<\/li>\n\n\n\n
in cloud storage<\/li>\n\n\n\n
or accessible via HTTP<\/li>\n<\/ul>\n\n\n\n
Result:<\/p>\n\n\n\n
slowness<\/li>\n\n\n\n
high bandwidth consumption<\/li>\n\n\n\n
poor user experience.<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n
<\/span>2. The Concept of Cloud Optimized GeoTIFF<\/span><\/h2>\n\n\n\n
Cloud Optimized GeoTIFF (COG) is a specific format for GeoTIFF files that allows users to read only the necessary parts of the file without downloading the entire file.<\/p>\n\n\n\n
            Cloud Storage\n         \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n         \u2502    COG file     \u2502\n         \u2502                 \u2502\n         \u2502  \u250c\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2510\n         \u2502  \u2502T1  \u2502T2  \u2502T3  \u2502\n         \u2502  \u251c\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2524\n         \u2502  \u2502T4  \u2502T5  \u2502T6  \u2502\n         \u2502  \u251c\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2524\n         \u2502  \u2502T7  \u2502T8  \u2502T9  \u2502\n         \u2502  \u2514\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2518\n         \u2502                 \u2502\n         \u2502  Overviews      \u2502\n         \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n                 \u25b2\n                 \u2502\n     HTTP Range Request\n                 \u2502\n                 \u25bc\n\n            QGIS \/ Client\n     t\u00e9l\u00e9charge uniquement\n     les tuiles n\u00e9cessaires<\/code><\/pre>\n\n\n\nThe principle is based on two elements:<\/p>\n\n\n\n
\n\n\n\n<\/span>Internal tiling<\/span><\/h3>\n\n\n\nThe raster is stored as internal tiles.<\/p>\n\n\n\n
This allows only the relevant blocks to be read.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
\nif a small area is displayed, the software downloads only the corresponding tiles.<\/p>\n<\/blockquote>\n\n\n\n
\n\n\n\n<\/span>Internal pyramids (overviews)<\/span><\/h3>\n\n\n\nThe file contains multiple resolutions of the image.<\/p>\n\n\n\n
This allows for:<\/p>\n\n\n\n
\nfast low-resolution previews<\/li>\n\n\n\n
without having to recalculate the resolution.<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>A structure optimized for HTTP Range Requests<\/span><\/h3>\n\n\n\nThe key feature of COG is the order in which data is stored in the file.<\/p>\n\n\n\n
It is organized so that software can use partial HTTP requests (HTTP Range Requests).<\/p>\n\n\n\n
This allows:<\/p>\n\n\n\n
\nrequesting only specific parts of the file<\/li>\n\n\n\n
without downloading the rest.<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>3. Why it has become a standard in geospatial<\/span><\/h2>\n\n\n\nCOG is now widely used in modern geospatial infrastructures.<\/p>\n\n\n\n
Its advantages:<\/p>\n\n\n\n
✔ fast access to remote data<\/p>\n\n\n\n
✔ cloud-compatible (S3, Azure, GCS)<\/p>\n\n\n\n
✔ progressive reading<\/p>\n\n\n\n
✔ simple format (always a GeoTIFF)<\/p>\n\n\n\n
Many data catalogs now use COGs:<\/p>\n\n\n\n
\nsatellite data<\/li>\n\n\n\n
digital elevation models<\/li>\n\n\n\n
orthophotos<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>4. Comparison with Other Raster Approaches<\/span><\/h2>\n\n\n\nMethod<\/th> Principle<\/th> Advantages<\/th> Limitations<\/th><\/tr><\/thead>
Classic GeoTIFF<\/td> Monolithic raster<\/td> simple<\/td> Inefficient at a distance<\/td><\/tr>
Cloud Optimized GeoTIFF<\/td> Structured GeoTIFF for partial reading<\/td> Fast access via HTTP<\/td> Requires proper creation<\/td><\/tr>
Web tiles (XYZ)<\/td> Split into thousands of images<\/td> Very fast<\/td> More complex management<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nCOG falls somewhere in between:<\/p>\n\n\n\n
\nsimplicity of a single file<\/li>\n\n\n\n
performance comparable to web tiles<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>5. Example of Use in QGIS<\/span><\/h2>\n\n\n\nQGIS directly supports COGs.<\/p>\n\n\n\n
You can load a remote raster via a URL.<\/p>\n\n\n\n
For example:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\nhttps:\/\/download.osgeo.org\/geotiff\/samples\/usgs\/o41078a5.tif<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
QGIS will:<\/p>\n\n\n\n
\nread the file header<\/li>\n\n\n\n
identify the necessary tiles<\/li>\n\n\n\n
download only those portions.<\/li>\n<\/ol>\n\n\n\nThe raster can remain entirely on the remote server.<\/p>\n\n\n\n
In QGIS:<\/p>\n\n\n\n
\nLayer Menu<\/li>\n\n\n\n
Add Layer<\/li>\n\n\n\n
Add Raster Layer<\/li>\n<\/ol>\n\n\n\n<\/a><\/figure>\n\n\n\n
\n\n\n\n<\/span>6. How to Create a Cloud-Optimized GeoTIFF<\/span><\/h2>\n\n\n\nCreating a COG is very simple with GDAL.<\/p>\n\n\n\n
Basic command:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\ngdal_translate input.tif output_cog.tif -of COG<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
Example with compression:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\ngdal_translate input.tif output_cog.tif \\
-of COG \\
-co COMPRESS=DEFLATE \\
-co BLOCKSIZE=512<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
Common options:<\/p>\n\n\n\n
Option<\/th> Description<\/th><\/tr><\/thead>
COMPRESS<\/td> compression (LZW, DEFLATE, ZSTD)<\/td><\/tr>
BLOCKSIZE<\/td> Tile size<\/td><\/tr>
RESAMPLING<\/td> method for pyramids<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nMore complete example:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\ngdal_translate input.tif output_cog.tif \\
-of COG \\
-co COMPRESS=DEFLATE \\
-co BLOCKSIZE=512<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
\n\n\n\n<\/span>7. How to verify that a file is a valid COG<\/span><\/h2>\n\n\n\nA GeoTIFF is not necessarily a valid COG.<\/p>\n\n\n\n
You can verify the structure using:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\ngdalinfo fichier.tif<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
or using the tool:<\/p>\n\n\n
<\/div><\/div><\/div><\/div><\/p>\n\n\n\ncog_validate fichier.tif<\/pre>\n\n\n<\/div><\/div>\n\n\n\n
This tool checks:<\/p>\n\n\n\n
\nthe internal order of the blocks<\/li>\n\n\n\n
the presence of overviews<\/li>\n\n\n\n
the file\u2019s optimized organization.<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>8. When should you use COG?<\/span><\/h2>\n\n\n\nCOG is particularly useful for:<\/p>\n\n\n\n
\nonline data catalogs<\/li>\n\n\n\n
satellite imagery<\/li>\n\n\n\n
DTMs<\/li>\n\n\n\n
orthophotos<\/li>\n\n\n\n
open data infrastructures<\/li>\n<\/ul>\n\n\n\nHowever, it is not necessarily useful for:<\/p>\n\n\n\n
\nsmall rasters<\/li>\n\n\n\n
files used only locally.<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n<\/span>9. A key element of modern geospatial architectures<\/span><\/h2>\n\n\n\nCOG is now a central component of geospatial cloud architectures.<\/p>\n\n\n\n
It is often used with:<\/p>\n\n\n\n
\nSTAC (data catalogs)<\/li>\n\n\n\n
Tile servers<\/li>\n\n\n\n
Geospatial APIs<\/li>\n<\/ul>\n\n\n\nIt allows direct access to rasters without a raster database.<\/p>\n\n\n\n
\n\n\n\n<\/span>10. The Connection to STAC<\/span><\/h2>\n\n\n\nCOG is often used with STAC (SpatioTemporal Asset Catalog).<\/p>\n\n\n\n
STAC enables the cataloging of geospatial data in the cloud.<\/p>\n\n\n\n
In this case:<\/p>\n\n\n\n
\nSTAC describes the metadata<\/li>\n\n\n\n
the rasters are stored as COG<\/li>\n<\/ul>\n\n\n\nThis combination has become a widely used architecture for satellite data.<\/p>\n\n\n\n
\n\n\n\n<\/span>Conclusion<\/span><\/h2>\n\n\n\nCloud Optimized GeoTIFF is not a new format but an optimized version of GeoTIFF.<\/p>\n\n\n\n
This optimization enables:<\/p>\n\n\n\n
\nfast reading<\/li>\n\n\n\n
efficient remote access<\/li>\n\n\n\n
easy integration into cloud architectures.<\/li>\n<\/ul>\n\n\n\nIt is now one of the most widely used raster formats for the distribution of geospatial data.<\/p>\n\n\n\n
\nA Cloud-Optimized GeoTIFF allows you to transform a simple raster file into a remotely accessible data service. GIS software downloads only the necessary portions of the file, making it possible to view very large rasters without the need for complex server infrastructure.<\/p>\n<\/blockquote>\n\n\n\n
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