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COG

Cloud Optimized GeoTIFF (COG) is a geospatial raster format developed to facilitate the storage and use of imagery and raster datasets in cloud environments. Using the popular GeoTIFF format as a base, COG relies on internal tiling, overviews, and proper file structure to enable software to access only those parts of the imagery that are needed. This allows one to avoid excessive data transfers, thereby optimizing the workflow of web-based GIS, remote sensing, and cloud-based GIS tasks. COG is typically used to represent satellite imagery, aerial imagery, digital elevation models (DEMs), land cover data, and other raster datasets. COG files can be accessed via HTTP range requests; therefore, the user is able to operate on the data stored in cloud object storages without having to download the whole dataset. COGs are extensively used in modern geospatial data workflows including GIS, remote sensing, image processing, and spatial analysis.

COG

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A Cloud Optimized GeoTIFF (COG) is a specialized form of the GeoTIFF raster format designed for efficient storage, distribution, and processing of geospatial data in cloud environments. Unlike conventional raster files that may require downloading an entire dataset before analysis, COGs organize imagery into independently accessible tiles and include reduced-resolution overviews. This structure allows GIS and remote sensing applications to retrieve only the required portions of a dataset through web-based requests. COGs are particularly useful for managing large satellite imagery, aerial imagery, elevation models, orthophotos, and scientific raster datasets. They support cloud-native geospatial workflows by reducing data transfer requirements and enabling faster visualization and on-demand processing. COGs can be stored in cloud object storage and accessed directly by compatible geospatial software and web services. By combining the interoperability of GeoTIFF with efficient web access, the COG format has become an important component of modern cloud GIS, remote sensing, spatial data infrastructure, and large-scale raster analytics.

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