Major Geospatial Data Standards

Why This Matters

Geospatial data standards are the reason a surveyor in California can share elevation data with a flood modeler in Florida without everything breaking. You're being tested on understanding why certain standards exist, what problems they solve, and when to use each one. The core concepts revolve around interoperability, data exchange, and the fundamental distinction between vector, raster, and point cloud data structures.

Standards fall into two camps: governance frameworks that establish rules and best practices, and technical formats that define how data is actually structured and shared. When exam questions ask you to recommend a data format or explain why a workflow failed, they're testing whether you understand the underlying architecture, not whether you memorized acronyms. Don't just know what each standard is; know what problem it solves and when you'd reach for it over alternatives.

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Governance and Institutional Frameworks

These organizations and directives don't define file formats themselves. They establish the rules of the road that make cross-platform, cross-border geospatial work possible. Understanding who sets standards helps you trace why formats exist and who enforces compliance.

Open Geospatial Consortium (OGC) Standards

• International standards body that develops specifications adopted by governments, agencies, and software vendors worldwide
• Interoperability focus ensures that geospatial services (WMS, WFS, WCS) work across platforms without proprietary lock-in
• Foundation for web services: if you're serving maps or features over the internet, you're almost certainly using OGC specifications

ISO 19100 Series of Geographic Information Standards

The ISO 19100 series is a comprehensive international framework covering everything from metadata schemas to data quality measures to spatial referencing.

• ISO 19115 (metadata) and ISO 19139 (XML implementation of metadata) are the most frequently referenced in practice
• Ensures global consistency: when agencies in different countries need to share data, ISO standards provide the common language for describing what the data is, how accurate it is, and what coordinate system it uses

Federal Geographic Data Committee (FGDC) Standards

• U.S. federal mandate establishing requirements for how federal agencies document and share geospatial data
• Metadata emphasis through the Content Standard for Digital Geospatial Metadata (CSDGM), now transitioning to ISO-aligned standards
• Enables data discovery: proper FGDC metadata means datasets show up in clearinghouses like Data.gov, so users can find and evaluate data before downloading it

INSPIRE Standards

• European Union directive that legally requires member states to make spatial data interoperable and accessible
• 34 data themes covering environmental monitoring, transport networks, land use, and administrative boundaries
• Cross-border harmonization solves the problem of, say, France and Germany using incompatible coordinate systems or attribute schemas for the same type of data

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Vector Data Formats

Vector formats store geographic features as discrete points, lines, and polygons with associated attributes. The key distinction is between lightweight web-friendly formats and robust formats designed for complex GIS analysis.

GeoJSON

• JSON-based encoding that's human-readable and easy to parse with standard web programming libraries
• Lightweight and web-native: the default choice for web mapping applications, APIs, and real-time data feeds
• Simple feature support for points, lines, polygons, and multi-geometries; not ideal for complex topological relationships
• Uses WGS 84 (EPSG:4326) as its coordinate reference system by specification, which is worth remembering for projection-related questions

Shapefile

• Esri's legacy format and still the most widely exchanged vector format despite being decades old
• Multi-file structure requires at minimum .shp (geometry), .dbf (attributes), and .shx (spatial index) files to function, plus optional .prj (projection) and .cpg (character encoding) files
• Know these limitations for troubleshooting questions: 2 GB file size cap, 10-character field name limit, no support for NULL values (stores zeros or blanks instead), and no support for date-time fields with time zones

GML (Geography Markup Language)

• XML-based OGC standard supporting complex geometries, feature relationships, and rich attribute schemas
• Verbose but powerful: file sizes are larger than GeoJSON, but GML handles topology, 3D coordinates, and temporal data
• Foundation for INSPIRE and many national spatial data infrastructures; often used for authoritative dataset exchange where schema validation matters

KML (Keyhole Markup Language)

• XML format for visualization designed for Google Earth and similar 3D globe applications
• Supports styling and 3D: placemarks, paths, polygons, ground overlays, and camera views with embedded appearance definitions
• Presentation-focused: great for sharing visual products, but not designed for rigorous spatial analysis or attribute-heavy workflows
• KMZ is simply a zipped KML file, often bundling images and other resources together

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Raster Data Formats

Raster formats represent geographic information as grids of cells (pixels), each storing a value. The critical concept is that raster data requires georeferencing to tie pixel coordinates to real-world locations.

GeoTIFF

• Industry-standard georeferenced raster that embeds coordinate system, extent, and resolution directly in the TIFF file header through special metadata tags (called "GeoKeys")
• Supports satellite imagery, DEMs, and orthophotos: any continuous surface data typically lives in GeoTIFF format
• Compression options include LZW (lossless), DEFLATE (lossless), and JPEG (lossy); Cloud Optimized GeoTIFF (COG) reorganizes the internal tile structure so that clients can stream just the portion they need from cloud storage without downloading the entire file

ESRI Geodatabase

• Native Esri storage format supporting vector, raster, and tabular data in a unified structure
• Advanced capabilities include versioning, topology rules, relationship classes, domains (attribute constraints), and raster catalogs
• File geodatabase vs. enterprise geodatabase: file-based (.gdb folder) works for single users or small teams, while enterprise geodatabases (backed by SQL Server, PostgreSQL, or Oracle) support multi-user concurrent editing with version management

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Point Cloud and 3D Formats

LiDAR and photogrammetry generate massive point clouds that require specialized formats. The key challenge is storing millions to billions of points with attributes like intensity, classification, and return number while keeping files manageable.

LAS (LiDAR Data Exchange Format)

• ASPRS standard for point clouds: the universal format for airborne and terrestrial LiDAR data exchange
• Point attributes include XYZ coordinates, intensity, return number, number of returns, classification codes (ground, vegetation, building, etc.), GPS time, and RGB color when available
• LAZ compression reduces file sizes by 70-90% while maintaining all point attributes losslessly; increasingly required for data delivery by agencies like USGS
• A simple XYZ text file would lose classification, return information, intensity, and GPS time, which is why LAS/LAZ is the standard rather than plain text

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Web Services for Data Delivery

OGC web services define how geospatial data is requested and served over the internet. The key distinction is what each service delivers: images, features, or coverages.

Web Map Service (WMS)

• Serves rendered map images: the client requests a bounding box and receives a PNG, JPEG, or GIF of the map
• Multiple layer overlay lets you combine layers from different servers into a single view; the server handles all the rendering
• View-only access: you see the map but can't query individual features or download raw data

Web Feature Service (WFS)

• Serves vector features: the client receives actual geometry and attributes, typically as GML or GeoJSON
• Supports transactions: WFS-T (transactional) allows creating, updating, and deleting features through the service
• Query capabilities: filter features by attribute or spatial relationship before download, reducing unnecessary data transfer

Web Coverage Service (WCS)

• Serves raster data: the client requests a coverage (grid) and receives actual cell values, not just a rendered image
• Subsetting and processing: you can request specific bands, resolutions, or spatial extents; server-side processing reduces data transfer
• Essential for remote sensing: when you need to analyze satellite imagery pixel values, not just view a picture of them

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Legacy and Specialized Standards

Some standards address specific interoperability challenges or represent older approaches still encountered in practice.

Spatial Data Transfer Standard (SDTS)

• Early federal exchange standard developed to transfer spatial data between incompatible systems before modern formats existed
• Preserves data quality: designed to ensure no information loss during transfer, including topology and metadata
• Largely superseded: you'll encounter SDTS in legacy datasets, but modern workflows use GML, GeoJSON, or geodatabases

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Quick Reference Table

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Self-Check Questions

1. A European environmental agency needs to share land-use data with agencies in three neighboring countries. Which governance framework mandates how this data must be structured, and what problem does it solve?

2. Compare GeoJSON and GML: What trade-offs would you consider when choosing between them for a web mapping application versus a national cadastral database submission?

3. You're troubleshooting a workflow where a user can view a map layer but cannot select individual features or access attribute data. Which OGC web service is likely being used, and which service would solve the problem?

4. A project requires sharing LiDAR point cloud data with a contractor. What format would you use, and what key attributes does this format preserve that a simple XYZ text file would not?

5. Explain why a GeoTIFF is preferable to a standard TIFF for satellite imagery analysis. What information does the "Geo" component add, and how does this enable spatial analysis?