Raster Datasets
Raster Datasets: Raster datasets are a fundamental data structure used in various fields, particularly in geographic information systems (GIS), remote sensing, and image processing. They represent spatially continuous data and are commonly used to depict information across a two-dimensional grid or matrix of cells, where each cell, also known as a pixel, holds a value representing a specific attribute or measurement. Unlike vector data that use points, lines, and polygons to represent spatial features, raster datasets divide a geographic area into a regular grid of equally sized cells. This gridded representation allows for the efficient storage and processing of continuous data, such as satellite imagery, aerial photographs, elevation models, weather data, land cover classifications, and much more. Key features of raster datasets include: Resolution: The resolution of a raster dataset refers to the size of each pixel or cell in the grid. High-resolution rasters have smaller cells, providing more detailed information, while low-resolution rasters offer a broader view of the area but with reduced detail. Georeferencing: Raster datasets are geo-referenced, meaning they are tied to specific real-world coordinates using a coordinate system. This allows precise positioning of the data on the Earth's surface, facilitating spatial analysis and integration with other geographic data. Data Types: Raster datasets can store many data types, such as integers, floating-point numbers, and categorical values. These data types represent diverse information, from temperature values and elevation measurements to land use classifications. Data Sources: Raster datasets are generated from various sources, including satellite and aerial sensors, ground-based measurements, and simulations from mathematical models. These datasets are commonly available in GeoTIFF, JPEG, PNG, and many proprietary formats specific to different software applications. Raster datasets are crucial in environmental monitoring, urban planning, agriculture, natural resource management, climate studies, and numerous other fields. They allow researchers, scientists, and analysts to visualize, analyze, and interpret complex spatial data, leading to informed decision-making and a deeper understanding of our world. As technology advances, the use of raster datasets continues to evolve, with applications in machine learning, computer vision, and artificial intelligence, further expanding the potential of raster data for solving real-world challenges.
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Additional Info
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| Last Updated | March 25, 2026, 01:56 (UTC) |
| Created | March 25, 2026, 01:56 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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Raster Datasets |
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Description
A description of the dataset.
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Raster Datasets: Raster datasets are a fundamental data structure used in various fields, particularly in geographic information systems (GIS), remote sensing, and image processing. They represent spatially continuous data and are commonly used to depict information across a two-dimensional grid or matrix of cells, where each cell, also known as a pixel, holds a value representing a specific attribute or measurement. Unlike vector data that use points, lines, and polygons to represent spatial features, raster datasets divide a geographic area into a regular grid of equally sized cells. This gridded representation allows for the efficient storage and processing of continuous data, such as satellite imagery, aerial photographs, elevation models, weather data, land cover classifications, and much more. Key features of raster datasets include: Resolution: The resolution of a raster dataset refers to the size of each pixel or cell in the grid. High-resolution rasters have smaller cells, providing more detailed information, while low-resolution rasters offer a broader view of the area but with reduced detail. Georeferencing: Raster datasets are geo-referenced, meaning they are tied to specific real-world coordinates using a coordinate system. This allows precise positioning of the data on the Earth's surface, facilitating spatial analysis and integration with other geographic data. Data Types: Raster datasets can store many data types, such as integers, floating-point numbers, and categorical values. These data types represent diverse information, from temperature values and elevation measurements to land use classifications. Data Sources: Raster datasets are generated from various sources, including satellite and aerial sensors, ground-based measurements, and simulations from mathematical models. These datasets are commonly available in GeoTIFF, JPEG, PNG, and many proprietary formats specific to different software applications. Raster datasets are crucial in environmental monitoring, urban planning, agriculture, natural resource management, climate studies, and numerous other fields. They allow researchers, scientists, and analysts to visualize, analyze, and interpret complex spatial data, leading to informed decision-making and a deeper understanding of our world. As technology advances, the use of raster datasets continues to evolve, with applications in machine learning, computer vision, and artificial intelligence, further expanding the potential of raster data for solving real-world challenges. |
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Tags / Keywords
Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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9.48 |
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2023-08-01 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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public |
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License
License used to access the dataset.
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License not specified |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://open-KitchenerGIS.opendata.arcgis.com/datasets/KitchenerGIS::raster-datasets |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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KitchenerGIS |
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Contact Point
Who to contact regarding access?
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City of Kitchener |
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Contact Point Email
The email to contact regarding access?
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[email protected] |
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Publisher
Publisher of the dataset.
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City of Kitchener |
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Publisher Email
Email of the publisher.
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Author
Author of the dataset.
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KitchenerGIS |
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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2025-09-04 |
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Identifier
Unique identifier for the dataset.
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d4203b1c24e04f9794eb0d960b463b09 |
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Language
Language(s) of the dataset
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English |
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Link to dataset description
A URL to an external document describing the dataset.
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https://open-KitchenerGIS.opendata.arcgis.com/datasets/KitchenerGIS::raster-datasets |
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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City of Kitchener |
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
If tabular dataset, total number of cells with data.
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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