Skip to main content

Metadata

Metadata?

GIS metadata refers to structured information that describes the characteristics of a geographic dataset. It acts as a "data label" that provides essential details about the dataset, including its source, accuracy, projection, format, and usage constraints. Metadata ensures that GIS data is properly understood, used, and shared across different systems and organizations.

Key Terminologies in GIS Metadata

  • Spatial Metadata: Metadata specifically related to the geographic properties of a dataset, such as coordinate reference system and projection.
  • Data Provenance: The history of a dataset, including its origin, transformations, and modifications.
  • Data Lineage: A record of the steps and processes applied to a dataset, ensuring transparency in data processing.
  • Interoperability: The ability of GIS data to be shared and used across different software and organizations due to standardized metadata.
  • Data Discovery: The process of searching and retrieving datasets based on metadata descriptions.

Importance of GIS Metadata Standards

Metadata standards provide a structured format for documenting spatial data, ensuring consistency, interoperability, and usability across GIS platforms.

1. Consistency

Metadata standards ensure that geospatial data is documented in a uniform manner, making it easier to interpret and compare datasets.

Example: A national database of flood risk maps follows ISO 19115 standards, ensuring that all maps use the same metadata structure, making them easy to integrate.

2. Interoperability

Standardized metadata allows GIS datasets to be shared between different organizations, ensuring seamless integration and analysis.

Example: A city planning department uses FGDC metadata standards, allowing GIS data to be easily shared with federal agencies for disaster response planning.

3. Data Discovery

Comprehensive metadata enables efficient searching and retrieval of relevant GIS datasets in a database or a spatial data infrastructure (SDI).

Example: A researcher looking for deforestation data can filter datasets by date, resolution, or accuracy using metadata records.

Key Components of GIS Metadata

Metadata records include several core components that define a dataset's characteristics:

1. Data Identification

  • Dataset Name: The official name of the dataset.
  • Creator: The organization or individual who created the dataset.
  • Date Created: The date the dataset was generated or last updated.
  • Contact Information: Details for inquiries about the dataset.

Example:
A dataset named "Land Cover Classification - India 2024" includes metadata stating that it was created by the Indian Space Research Organization (ISRO) on January 5, 2024, with contact information for ISRO's GIS department.

2. Spatial Reference

  • Coordinate System: Defines how the spatial data is positioned on Earth (e.g., WGS 84, NAD 83).
  • Projection: The method used to represent the curved Earth on a flat surface (e.g., UTM, Mercator).
  • Datum: The reference point for the coordinate system (e.g., WGS 84, GCS).

Example:
A GIS dataset of India's coastline includes metadata stating that the data uses the WGS 84 coordinate system with a UTM Zone 44N projection.

3. Data Quality

  • Positional Accuracy: The level of accuracy of spatial coordinates.
  • Attribute Accuracy: The correctness of non-spatial data linked to geographic features.
  • Lineage: The source and processing steps taken to create the dataset.
  • Limitations: Any known issues or constraints in the dataset.

Example:
A forest cover map derived from Landsat-8 imagery includes metadata stating that the positional accuracy is ±15 meters, and classification errors may exist in areas with cloud cover.

4. Attribute Information

  • Field Descriptions: A breakdown of attributes within the dataset.
  • Data Types: Specifies whether attributes are numeric, categorical, or text-based.

Example:
A land use dataset includes an attribute table with metadata stating:

  • "Land_Type" (categorical) – values: "Forest," "Urban," "Agriculture."
  • "Area_sqkm" (numeric) – values: 100.5, 45.2, etc.

5. Access and Usage Constraints

  • License Type: Specifies whether the dataset is open-access or restricted.
  • Copyright Information: Defines who owns the data and how it can be used.
  • Confidentiality: Indicates if any part of the dataset is sensitive.

Example:
A wildlife habitat dataset has metadata stating:

  • License: CC BY 4.0 (free for public use with attribution).
  • Restrictions: Cannot be used for commercial purposes.

Common GIS Metadata Standards

Several metadata standards exist globally to ensure structured documentation of geospatial datasets:

Metadata StandardDescriptionRegion
ISO 19115International standard for geospatial metadata.Global
FGDC (Federal Geographic Data Committee)U.S. metadata standard used for federal geospatial datasets.United States
INSPIRE Metadata DirectiveStandardized metadata format for EU spatial data.European Union
Dublin CoreGeneral metadata standard used for various types of datasets, including GIS.Global

Example:
An air quality monitoring dataset from a U.S. environmental agency follows FGDC metadata standards, making it compatible with other U.S. government GIS datasets.

Significance of GIS Metadata

1. Informed Decision-Making

  • Users can assess whether a dataset is suitable for their analysis based on accuracy, resolution, and data limitations.
  • Example: A city planner evaluating road network data checks the metadata accuracy level before using it for infrastructure development.

2. Data Quality Control

  • Helps identify potential errors in data collection and processing.
  • Example: A climate scientist analyzes metadata to verify the sensor calibration details of satellite temperature data.

3. Data Sharing and Collaboration

  • Ensures seamless data exchange between researchers, agencies, and GIS professionals.
  • Example: A disaster response team shares GIS flood models using ISO 19115 metadata, allowing emergency responders to access and integrate the data quickly.

Comments

Post a Comment

Popular posts from this blog

Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation , coverage , and spectral sensitivity . Below is a breakdown of the major types: 1️⃣ Based on Camera Axis Orientation Type Description Key Feature Vertical Photo Taken with the camera axis pointing directly downward (within 3° of vertical). Used for maps and measurements Oblique Photo Taken with the camera axis tilted away from vertical. Covers more area but with distortions Low Oblique: Horizon not visible High Oblique: Horizon visible 2️⃣ Based on Number of Photos Taken Type Description Single Photo One image taken of an area Stereoscopic Pair Two overlapping photos for 3D viewing and depth analysis Strip or Mosaic Series of overlapping photos covering a long area, useful in mapping large regions 3️⃣ Based on Spectral Sensitivity Type Description Application Panchromatic Captures images in black and white General mapping Infrared (IR) Sensitive to infrared radiation Veget...
Post a Comment
Read more

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping.  Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...
Post a Comment
Read more

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...
Post a Comment
Read more

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...
Post a Comment
Read more

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...
Post a Comment
Read more