Top 30 GIS File Formats Every Mapping Professional Should Know

Whether you’re a GIS (Geographic Information System) professional, developer, or student, navigating the world of GIS file formats can feel overwhelming. With countless file types for vector, raster, spatial databases, and metadata, understanding which file format to use—and when—can drastically boost your productivity.

In this comprehensive guide, we’ll break down 30 of the most commonly used GIS file formats, helping you decode their purpose, compatibility, and usage in modern mapping and spatial analysis workflows.

30 GIS File Formats

🔷 Vector File Formats

Vector files store geographic features as points, lines, and polygons. These formats are perfect for boundaries, roads, rivers, and more.

When working with spatial data, choosing the right vector file format is crucial. Vector formats represent geographic features using points, lines, and polygons, making them ideal for storing roads, boundaries, buildings, and other precise features. Let’s explore some of the most commonly used vector GIS file types, including their structure, use cases, and compatibility.

1. Shapefile (.shp, .shx, .dbf)

The Shapefile is a widely used vector file format developed by Esri, and despite being introduced in the early 1990s, it’s still very popular.

🔍 Key Components:

• .shp – Stores geometry (points, lines, polygons)

• .shx – Shape index format for fast access

• .dbf – DBase file containing attribute data

✅ Advantages:

• Supported by nearly every GIS software (ArcGIS, QGIS, MapInfo, etc.)

• Simple and reliable for sharing vector data

⚠️ Limitations:

• No support for topology

• Attribute table has a 10-character field name limit

• Maximum file size ~2 GB

2. GeoJSON (.geojson)

GeoJSON is an open-standard format based on JavaScript Object Notation (JSON). It represents geographic features and their attributes in a lightweight, human-readable structure.

🌍 Best For:

• Web mapping applications (Leaflet, Mapbox, OpenLayers)

• API data exchange and web-based visualizations

✅ Advantages:

• Easy to read and write

• Excellent for browser-based GIS applications

• Supports properties (attributes) along with geometry

⚠️ Limitations:

• Larger files may impact performance

• Doesn’t support complex topology

3. KML/KMZ (.kml, .kmz)

KML (Keyhole Markup Language) and KMZ (its compressed version) were developed by Google for Google Earth. These are XML-based formats used to display geographic data in 3D.

🌍 Use Cases:

• Creating interactive maps

• Visualizing spatial features in 3D viewers

• Sharing geographic data in presentations

✅ Advantages:

• Easy to view in Google Earth/Maps

• Supports styling, pop-ups, and 3D visualizations

⚠️ Limitations:

• Not ideal for advanced GIS analysis

• Slower performance with large datasets

4. GeoPackage (.gpkg)

GeoPackage (GPKG) is a modern, open-source format based on SQLite that can store vector, raster, and tile data in a single file.

🔧 Ideal For:

• Mobile GIS applications

• Offline mapping and data portability

✅ Advantages:

• All-in-one container (vector + raster)

• Fully OGC-compliant and platform-independent

• Faster performance and better scalability than shapefiles

⚠️ Limitations:

• Slightly larger learning curve for beginners

• Not supported by some legacy systems

5. CSV (.csv)

Comma-Separated Values (CSV) files are not GIS-specific but are frequently used for storing tabular spatial data, especially when paired with coordinate columns (e.g., latitude/longitude).

🧭 Typical Use:

• Simple geocoding (e.g., plotting store locations)

• Joining tabular data with spatial datasets

✅ Advantages:

• Easy to create/edit in Excel, Notepad, Google Sheets

• Widely supported for quick imports

⚠️ Limitations:

• Cannot store complex geometry

• Needs coordinate columns for spatial use

6. GPX (.gpx)

GPS Exchange Format (GPX) is an XML format designed specifically for sharing GPS data such as waypoints, tracks, and routes.

🚴 Use Cases:

• Outdoor recreation (hiking, cycling)

• GPS logging and route tracking

• Import/export from GPS devices

✅ Advantages:

• Supported by many fitness/GPS apps

• Easy to view and share track data

⚠️ Limitations:

• Limited attribute support

• Not suitable for complex GIS analysis

7. DWG (.dwg)

DWG is the native file format for AutoCAD, primarily used in architectural and engineering design. It stores 2D and 3D vector data.

🛠️ Commonly Used By:

• Engineers, architects, and surveyors

• Infrastructure planning and CAD-GIS workflows

✅ Advantages:

• High precision

• Extensive CAD support

⚠️ Limitations:

• Limited GIS-specific functionality

• Requires conversion to use in most GIS tools

8. DXF (.dxf)

Drawing Exchange Format (DXF) is a more open version of DWG, used to share AutoCAD data with other software, including GIS platforms.

🌐 Use Cases:

• CAD-to-GIS data exchange

• Importing building layouts or blueprints into GIS

✅ Advantages:

• Open format with wide support

• Easier to integrate into GIS than DWG

⚠️ Limitations:

• Still CAD-focused, not ideal for spatial analysis

9. GML (.gml)

Geography Markup Language (GML) is an XML-based format developed by the OGC (Open Geospatial Consortium) for storing geographic data.

🔄 Best For:

• Interoperability and data sharing between different GIS platforms

• Web Feature Services (WFS)

✅ Advantages:

• Highly customizable

• Supports rich attribute data and geometry

⚠️ Limitations:

• Verbose and large file sizes

• Complex structure can be overwhelming for beginners

10. WKT/WKB (.wkt, .wkb)

Well-Known Text (WKT) and Well-Known Binary (WKB) are formats used to represent geometry in a standard, database-friendly way.

📊 Ideal For:

• Spatial databases like PostGIS

• Storing geometry in tabular formats

✅ Advantages:

• Simple to parse and store

• Great for database integration

⚠️ Limitations:

• No styling or attribute metadata

• Not standalone GIS data formats

11. XLS/XLSX (Excel)

Excel files are not traditional GIS formats, but they’re often used to store coordinates and attribute data before being imported into GIS software.

🧮 Use Cases:

• Storing survey data

• Mapping address or point data

✅ Advantages:

• Familiar interface for non-GIS users

• Supports advanced formatting and formulas

⚠️ Limitations:

• Needs conversion to proper GIS formats for spatial analysis

• Cannot natively store spatial geometry

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🟩 Raster File Formats

Raster formats store data in pixel-based grids. Best used for satellite images, elevation models, and environmental data.

Raster GIS data represents the world in a grid of pixels, where each cell holds a value—commonly used for elevation, imagery, land cover, and environmental modeling. Below are six powerful raster file formats used in geospatial analysis, remote sensing, and scientific research.

1. Esri Grid (.adf)

Type: Proprietary Raster Format
Developer: Esri

The Esri Grid is a legacy raster data format created by Esri, used primarily in ArcGIS for storing and analyzing spatial surfaces.

📦 Structure:

• Comes in two types: Binary Grid and ASCII Grid

• Binary Grid uses a folder of multiple files (.adf) to store spatial and attribute data

✅ Best For:

• Terrain modeling (e.g., DEMs)

• Hydrological analysis (e.g., watershed, slope)

• Suitability modeling

✅ Advantages:

• Optimized for use in ArcGIS

• Supports integer and floating-point data

• Ideal for analytical workflows like map algebra

⚠️ Limitations:

• Proprietary to Esri

• Not easily portable across non-Esri platforms

• File name restrictions (limited character length)

2. NetCDF (.nc)

Type: Scientific Raster Format
Developer: UCAR (Unidata)

NetCDF (Network Common Data Form) is a powerful data format used to store multidimensional scientific data, such as time series, climate models, ocean currents, and atmospheric parameters.

🔍 Features:

• Stores data in arrays with dimensions like time, depth, and spatial coordinates

• Includes embedded metadata

✅ Best For:

• Environmental modeling (climate, weather)

• Oceanographic and atmospheric data

• Long time-series datasets

✅ Advantages:

• Open, self-describing format

• Scalable and efficient for large datasets

• Widely supported in scientific tools (MATLAB, Python, QGIS)

⚠️ Limitations:

• Not beginner-friendly

• Needs specialized libraries (e.g., netCDF4 in Python) for manipulation

3. GeoTIFF (.tif, .tiff)

Type: Georeferenced Raster Format
Developer: Open Format (GeoTIFF consortium)

GeoTIFF is a standard raster image format that includes embedded georeferencing information, making it one of the most widely used raster formats in GIS.

🌍 Embedded Metadata Includes:

• Coordinate reference system (CRS)

• Bounding box and resolution

• Projection information

✅ Best For:

• Satellite and aerial imagery

• Elevation models (DEMs)

• Land use/land cover analysis

✅ Advantages:

• Fully open and widely supported (QGIS, ArcGIS, GDAL)

• Supports lossless compression

• Easy to share and publish

⚠️ Limitations:

• Large file sizes without compression

• Not optimized for web display (requires tiling or pyramids)

4. HDF (.hdf)

Type: Hierarchical Scientific Data Format
Developer: HDF Group

HDF (Hierarchical Data Format) is designed for managing large, complex datasets, often in scientific applications including Earth observation, remote sensing, and modeling.

🌐 Key Features:

• Supports multidimensional arrays

• Hierarchical structure with groups, datasets, and attributes

✅ Best For:

• Satellite mission data (e.g., MODIS, NASA datasets)

• Environmental and climate science

• Big data processing

✅ Advantages:

• Highly scalable

• Stores multiple datasets in a single file

• Works well in high-performance computing environments

⚠️ Limitations:

• Complex format structure

• Requires specific libraries or viewers (e.g., HDFView)

5. ENVI (.hdr, .img)

Type: Remote Sensing Raster Format
Developer: Harris Geospatial (originally RSI)

ENVI format is widely used in remote sensing for processing hyperspectral and multispectral imagery.

📂 File Components:

• .img – Stores the raster data

• .hdr – Header file with metadata (band count, data type, resolution)

✅ Best For:

• Hyperspectral image analysis

• Vegetation studies, mineral exploration

• Image classification and spectral analysis

✅ Advantages:

• Supports hundreds of spectral bands

• High compatibility with remote sensing tools (ENVI, QGIS, ArcGIS)

• Works well with tools like FLAASH, ATCOR

⚠️ Limitations:

• Proprietary format, but readable in many GIS apps

• Larger file sizes due to high data volume

6. LiDAR (.las, .laz)

Type: 3D Point Cloud Raster Format
Developer: ASPRS (American Society for Photogrammetry and Remote Sensing)

LiDAR files store 3D point cloud data from airborne or terrestrial laser scanning systems, typically used for terrain modeling and 3D mapping.

📐 Data Includes:

• X, Y, Z coordinates

• Intensity, return number, classification (e.g., ground, vegetation)

• Time stamps and color values (optional)

✅ Best For:

• Digital Elevation Models (DEMs)

• Canopy height and forest structure analysis

• Urban modeling and infrastructure planning

✅ Advantages:

• High-resolution 3D spatial data

• .laz offers compressed version of .las for easier storage and sharing

• Compatible with tools like LASTools, PDAL, ArcGIS, QGIS

⚠️ Limitations:

• Requires specialized software to process and visualize

• Large files can be resource-intensive

🗃️ Spatial Databases & Web Formats

Store and manage GIS data efficiently in scalable, often web-friendly formats.

As GIS technology evolves, the need for efficient, scalable, and web-friendly formats has become more critical than ever. Spatial databases and modern data exchange formats play a vital role in powering real-time web maps, spatial analysis, and multi-user geospatial systems.

This guide breaks down 8 popular formats—from high-performance databases to lightweight web-ready formats.

1. 🗂 File Geodatabase (.gdb)

Type: Spatial Database
Developer: Esri

The File Geodatabase (GDB) is a powerful format developed by Esri for managing large volumes of vector, raster, and tabular data within the ArcGIS ecosystem.

🔍 Features:

• Stores multiple layers, tables, and relationships in a single folder-based structure

• Supports advanced capabilities like topology, subtypes, domains, and versioning

✅ Best For:

• Enterprise-level GIS workflows

• Editing and storing complex spatial datasets

• Integration with ArcGIS Pro and ArcGIS Server

✅ Advantages:

• High performance and scalability

• Can handle large datasets (>1 TB)

• Supports spatial indexing and SQL queries

⚠️ Limitations:

• Proprietary (non-Esri tools have limited read/write support)

• Folder-based structure can be tricky to share

2. 💾 SpatiaLite (.sqlite)

Type: Lightweight Spatial Database
Based On: SQLite

SpatiaLite extends the popular SQLite database engine by adding spatial capabilities compliant with OGC standards.

🔧 Use Cases:

• Lightweight mobile and desktop GIS apps

• Local offline storage of spatial data

• Rapid prototyping and spatial querying

✅ Advantages:

• Open-source and cross-platform

• Single-file structure for easy sharing

• SQL-based spatial analysis

⚠️ Limitations:

• Less performant for very large datasets

• Not as feature-rich as enterprise systems like PostGIS

3. 🐘 PostGIS (.postgres)

Type: Spatial Extension for PostgreSQL
Developer: Open Source (PostgreSQL + PostGIS community)

PostGIS is one of the most powerful and widely used open-source spatial databases. It adds geospatial capabilities to the PostgreSQL relational database.

🌐 Ideal For:

• Web GIS and cloud-based mapping systems

• Multi-user environments

• Complex spatial querying and analysis

✅ Advantages:

• Open-source and scalable

• Fully compliant with OGC standards

• Supports advanced GIS operations (e.g., spatial joins, buffering, geometry validation)

⚠️ Limitations:

• Requires setup and configuration (not plug-and-play)

• Needs technical expertise to manage

4. 🔍 JSON (.json)

Type: Web Data Exchange Format
Format: JavaScript Object Notation

JSON is a lightweight, text-based format widely used in web applications for data exchange. In GIS, it’s often used for storing metadata or simplified geometry.

Note: JSON is the base structure of GeoJSON, but standard JSON can still be used for basic spatial data and configuration.

✅ Best For:

• Web APIs and data visualization tools

• Metadata sharing

• Lightweight configuration files

✅ Advantages:

• Human-readable and easy to parse

• Works seamlessly with JavaScript libraries

• Highly portable

⚠️ Limitations:

• Not suitable for complex geometries or large datasets

• Lacks spatial indexing

5. 🧭 XYZ (.xyz)

Type: Point Cloud Format
Structure: Plain text (x, y, z columns)

XYZ files are a simple format used to store LiDAR or elevation data in 3D point clouds. Each row contains an x, y, z coordinate representing a point in space.

Use Cases:

• Digital Elevation Models (DEMs)

• Terrain mapping

• LiDAR data analysis

✅ Advantages:

• Simple, universal format

• Easy to convert to other formats (e.g., LAS, GeoTIFF)

• Supported by most point cloud tools

⚠️ Limitations:

• No compression or metadata

• Not optimized for very large datasets

6. 🗺️ MapInfo TAB (.tab)

Type: Native GIS Format
Developer: MapInfo (now part of Precisely)

MapInfo TAB is the native file format used by MapInfo Professional, a popular desktop GIS software.

📁 Structure:

• Often comes with several associated files (.tab, .dat, .map, .id)

✅ Best For:

• Users in government, insurance, and telecommunications

• Organizations already using MapInfo GIS

✅ Advantages:

• Stable and efficient for vector data

• Maintains strong backward compatibility

⚠️ Limitations:

• Not as universally supported as Shapefile or GeoJSON

• Proprietary format (limited open-source tools)

7. 🧪 SAGA GIS (.grd)

Type: Raster/Analysis Format
Developer: SAGA GIS (System for Automated Geoscientific Analyses)

The .grd format is used by SAGA GIS, an open-source GIS tool focused on scientific and geospatial terrain analysis.

✅ Best For:

• Terrain modeling

• Hydrological and climatological analysis

• Academic and research applications

✅ Advantages:

• Optimized for raster processing in SAGA

• Open-source and customizable

⚠️ Limitations:

• Native only to SAGA GIS

• May need conversion for use in other platforms

8. 📜 XWL/XML (.xwl, .xml)

Type: Metadata/Configuration Format
Structure: Extensible Markup Language (XML)

XML-based formats like .xml and .xwl are used to store metadata, style configurations, symbology, or settings for GIS datasets.

🔧 Use Cases:

• Metadata exchange (e.g., ISO 19115 standards)

• GIS project configuration files

• Layer symbology and styling (e.g., QGIS SLD files)

✅ Advantages:

• Highly structured and machine-readable

• Easy to validate and share

• Supports namespaces and data schemas

⚠️ Limitations:

• Can be verbose and hard to read manually

• Requires parsing tools or viewers

🎨 Specialized & 3D Formats

Great for 3D modeling, specialized analysis, and visualization.

GIS isn’t just about 2D maps—modern geospatial analysis often requires 3D visualization, engineering-grade precision, or specialized raster processing. These formats are essential in sectors like urban planning, civil engineering, environmental modeling, and 3D printing.

Let’s explore five important specialized and 3D file formats used in GIS, CAD, and scientific modeling.

1. 🏗 DGN (.dgn)

Type: CAD / Engineering Format
Developer: Bentley Systems
Used In: MicroStation

DGN files are a primary file format for MicroStation, a leading CAD software used in civil engineering, transportation design, and infrastructure projects.

🧰 Key Features:

• Supports both 2D and 3D geometry

• Similar in concept to DWG/DXF used in AutoCAD

• Highly detailed vector representations for technical drawings

✅ Best For:

• Road, bridge, and utility network design

• Transportation infrastructure planning

• CAD-to-GIS conversion workflows

✅ Advantages:

• High precision geometry

• Maintains symbology, layers, and complex engineering elements

• Often used by public works departments and engineering firms

⚠️ Limitations:

• Proprietary format (though supported in GIS apps like ArcGIS and QGIS with plugins)

• Limited attribute data compared to GIS-native formats

2. 🖨 STL (.stl)

Type: 3D Mesh Format
Acronym: STereoLithography
Commonly Used In: 3D printing, modeling, and visualization

STL is a widely supported format for 3D surface geometry, defining the external surface of an object using a mesh of triangles. It’s particularly prominent in 3D printing and digital terrain modeling.

🧱 Structure:

• Contains a list of triangular facets, each defined by three vertices and a normal vector

• No color, texture, or geographic location data

✅ Best For:

• Creating 3D models of terrain, buildings, or geological layers

• Exporting data to 3D printers

• Visual simulations of landscapes or structures

✅ Advantages:

• Simple and lightweight format

• High compatibility with 3D software (Blender, SketchUp, CAD apps)

• Easy to convert from GIS elevation models

⚠️ Limitations:

• Lacks coordinate systems or metadata

• Not georeferenced—requires pairing with external spatial information

3. 🔬 VTK (.vtk)

Type: Scientific Visualization Format
Developer: Kitware (Visualization Toolkit)
Used In: 3D modeling, scientific simulations

VTK (Visualization Toolkit) is a specialized format for scientific and medical visualization, capable of representing 3D volumetric and vector field data.

🔍 Features:

• Supports complex 3D geometries and grid-based data

• Can store scalar/vector fields, surfaces, contours, and more

✅ Best For:

• Environmental simulations (e.g., fluid dynamics, airflow, climate models)

• Geological modeling

• Medical imaging in 3D (e.g., MRI or CT scan visualizations)

✅ Advantages:

• Highly customizable and powerful for volumetric data

• Integrated with tools like ParaView and Mayavi

• Supports both ASCII and binary formats

⚠️ Limitations:

• Requires technical knowledge to use

• Not natively supported in most traditional GIS software

4. 🌿 IDRISI (.rst)

Type: Raster Format
Developer: Clark Labs (TerrSet, formerly IDRISI)

.rst is the raster format used by IDRISI, an advanced GIS and remote sensing toolset focused on environmental modeling, climate change, and land use planning.

📦 File Components:

• .rst – Main raster data file

• .rdc – Accompanying metadata file

✅ Best For:

• Land change modeling (LCM)

• Habitat suitability analysis

• Sustainability and resource management studies

✅ Advantages:

• Tailored for scientific and environmental applications

• Rich raster processing tools within TerrSet software

• Supports time-series analysis and model building

⚠️ Limitations:

• Not widely supported outside the IDRISI/TerrSet ecosystem

• Requires conversion for broader compatibility

5. 🖼 JPEG (.jpg / .jpeg) + World File (.jgw)

Type: Raster Image Format
Developer: Joint Photographic Experts Group

While JPEG is not inherently a GIS file format, when paired with a world file (.jgw), it becomes a georeferenced basemap that can be used in mapping applications.

🌍 World File (.jgw):

• Contains scale, rotation, and location information

• Allows the JPEG to align correctly within a spatial reference system

✅ Best For:

• Satellite imagery overlays

• Aerial photography in mapping apps

• Visual context in digital maps

✅ Advantages:

• Compressed and fast-loading

• Compatible with nearly all GIS software

• Ideal for background imagery and web tiles

⚠️ Limitations:

• Lossy compression may reduce image quality

• No native support for multiple bands or elevation data

• Needs a sidecar world file for georeferencing

✅ Final Thoughts

GIS file formats may vary widely in structure and application, but understanding their differences is key to an efficient geospatial workflow. Whether you’re working with vector boundaries, massive raster datasets, or 3D point clouds, the right format ensures data accuracy, faster processing, and better visualization.

👉 Tip: When choosing a file format, consider compatibility with your software, data size, and the type of analysis you plan to perform.

❓ Frequently Asked Questions (FAQ)

1. What are the most common GIS file formats?

The most commonly used GIS file formats include Shapefile (.shp), GeoJSON (.geojson), KML/KMZ (.kml, .kmz), and GeoTIFF (.tif). These formats are widely supported across desktop GIS platforms like QGIS and ArcGIS.

2. What is the difference between vector and raster GIS formats?

• Vector formats (e.g., Shapefile, GeoJSON) represent data as points, lines, and polygons—ideal for boundaries, roads, and locations.

• Raster formats (e.g., GeoTIFF, Esri Grid) represent data as pixels or cells—best for continuous data like elevation, land cover, or satellite imagery.

3. What file format is best for 3D GIS data?

For 3D GIS or modeling, formats like STL (.stl), VTK (.vtk), LAS (.las), and DGN (.dgn) are commonly used. These formats support complex geometries and elevation information.

4. What is a GeoTIFF file used for in GIS?

A GeoTIFF is a raster image that includes embedded georeferencing information, making it perfect for satellite imagery, DEM files, and land use analysis. It’s widely supported and ideal for analytical and visualization tasks.

5. Can I open a Shapefile in QGIS or ArcGIS?

Yes! Shapefile (.shp, .shx, .dbf) is natively supported by both QGIS and ArcGIS. Just make sure all associated files (.shp, .shx, .dbf, and optionally .prj) are in the same folder when importing.

6. What is the difference between KML and KMZ files?

• KML is an XML-based format used primarily by Google Earth for storing geographic data.

• KMZ is a compressed version of a KML file, which may also include additional resources like images or icons.

7. What are spatial databases and why are they important?

Spatial databases (like PostGIS, File Geodatabase, and SpatiaLite) allow for efficient storage, indexing, and querying of spatial data. They are essential for large-scale, multi-user GIS systems, especially in web and enterprise applications.

8. Can I use Excel or CSV files in GIS?

Absolutely! CSV and Excel (XLS/XLSX) files can be imported into GIS platforms as tables, especially if they include coordinate columns (latitude/longitude). They’re great for quick mapping and data joins.

9. What is a world file in GIS (e.g., .jgw)?

A world file provides georeferencing information for raster images like JPEGs or PNGs. It tells the GIS software how to position the image correctly on a map, even though the image itself has no coordinate system.

10. Which GIS file format is best for web mapping?

GeoJSON and KML are ideal for web maps because they’re lightweight, human-readable, and compatible with JavaScript libraries like Leaflet and Mapbox.