Major Geospatial Data Standards

Why This Matters

Geospatial data standards aren't just bureaucratic checkboxes—they're 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 here revolve around interoperability, data exchange, and the fundamental distinction between vector, raster, and point cloud data structures.

Think of standards as falling 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—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

• Comprehensive international framework—covers everything from metadata schemas to data quality measures to spatial referencing
• ISO 19115 (metadata) and ISO 19139 (XML implementation) 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

Federal Geographic Data Committee (FGDC) Standards

• U.S. federal mandate—establishes 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

INSPIRE Standards

• European Union directive—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 France and Germany using incompatible coordinate systems or attribute schemas

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Vector Data Formats

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

GeoJSON

• JSON-based encoding—human-readable, 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

Shapefile

• Esri's legacy format—still the most widely exchanged vector format despite being decades old
• Multi-file structure requires .shp (geometry), .dbf (attributes), and .shx (index) files to function, plus optional projection files
• Limitations include 2GB file size cap, 10-character field names, and no support for NULL values—know these weaknesses for troubleshooting questions

GML (Geography Markup Language)

• XML-based OGC standard—supports 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

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

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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—embeds coordinate system, extent, and resolution directly in the TIFF file header
• Supports satellite imagery, DEMs, and orthophotos—any continuous surface data typically lives in GeoTIFF format
• Compression options include LZW, DEFLATE, and JPEG; Cloud Optimized GeoTIFF (COG) enables efficient streaming from cloud storage

ESRI Geodatabase

• Native Esri storage format—supports vector, raster, and tabular data in a unified structure
• Advanced capabilities include versioning, topology rules, relationship classes, and raster catalogs
• File geodatabase vs. enterprise geodatabase—file-based for single users, enterprise (SQL Server, PostgreSQL) for multi-user editing

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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 of points with attributes like intensity, classification, and return number.

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, classification codes, and GPS time
• LAZ compression reduces file sizes by 70-90% while maintaining all point attributes; increasingly required for data delivery

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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—client requests a bounding box and receives a PNG, JPEG, or GIF of the map
• Multiple layer overlay—combine layers from different servers into a single view; the server does 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—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

Web Coverage Service (WCS)

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

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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?