GIS and Mapping

Geographic information systems (GIS) and mapping tools enable organisations to capture, store, analyse, and visualise location-based data. These tools support programme delivery, monitoring and evaluation, asset management, and stakeholder communication through spatial analysis and cartographic output.

This page covers tools for spatial data management (storage and querying), spatial analysis (buffer, overlay, proximity operations), web map publishing (tile servers, feature services), and collaborative map creation. Tools focused exclusively on mobile data collection appear in Data Collection Tools; beneficiary registration systems with mapping components appear in Beneficiary Identity Systems.

Requirements taxonomy

This taxonomy defines evaluation criteria for GIS and mapping tools. Requirements are organised by functional area and weighted by typical priority for mission-driven organisations. Adjust weights based on operational context.

Functional requirements

Core capabilities that define what the tool must do.

Spatial data management

Spatial analysis

Cartography and visualisation

Data interoperability

Collaboration and sharing

Technical requirements

Infrastructure, deployment, and integration considerations.

Deployment and hosting

Scalability and performance

Integration architecture

Security requirements

Security controls and data protection capabilities.

Authentication and access control

Data protection

Security certifications

Operational requirements

Day-to-day administration and management considerations.

Administration

Support and maintenance

Data management requirements

Data handling, portability, and lifecycle management.

Import and export

Data lifecycle

Commercial requirements

Licensing, pricing, and vendor considerations.

Accessibility requirements

Assessment methodology

Tools are assessed against each requirement using the following scale:

Additional notation:

• $ -Feature requires paid tier or add-on
• β -Feature in beta or preview
• E -Feature available in enterprise tier only
• P -Feature requires plugin or extension

Functional capability comparison

Spatial data management

Assessment notes -Spatial data management:

• QGIS F1.6: Version control via plugins (Sketsketcher, GeoGig integration); not native to core
• PostGIS F1.6: Temporal support available but requires application-level implementation
• uMap F1.1: Supports points, lines, polygons but no multi-part geometries or advanced geometry types
• uMap F1.3: Uses Web Mercator (EPSG:3857) only; no custom CRS support
• Earth Engine F1.5: Basic geometry validation; no topology rule enforcement
• GeoNode F1.8: 3D Tiles support added in version 4.4; limited 3D editing

Spatial analysis

Assessment notes -Spatial analysis:

• GeoServer F2.1-F2.8: Analysis available through WPS extension; requires additional configuration
• GeoNode: Relies on GeoServer WPS for analysis; limited native analysis tools
• uMap F2.4: Route drawing with OpenRouteService integration; not analytical routing
• uMap F2.7: Address search via Nominatim; not batch geocoding
• PostGIS F2.4: pgRouting extension required for network analysis
• Mapbox F2.4: Directions API requires additional subscription; client-side only
• ArcGIS F2.4: Network Analyst requires Standard or Advanced licence tier

Cartography and visualisation

Assessment notes -Cartography and visualisation:

• PostGIS: Database system; visualisation requires client applications
• QGIS F3.4: Web publishing via QGIS Server or qgis2web plugin
• uMap F3.3: Print feature in beta as of version 3.4
• Earth Engine F3.2: Basic labelling; limited placement control
• Earth Engine F3.3: Code Editor export; no print composer
• Mapbox F3.3: Export via Static Images API; no interactive print layout

Data interoperability

Assessment notes -Data interoperability:

• uMap F4.1: GeoJSON, GPX, KML, CSV; no Shapefile or GeoPackage
• Earth Engine F4.1: GeoJSON, Shapefile via upload; limited direct format support
• Earth Engine F4.3: Limited WMS/WMTS support; uses proprietary data access
• Mapbox F4.2: Raster tile display; limited raster analysis format support
• PostGIS F4.3: Database storage only; OGC services require GeoServer or similar

Collaboration and sharing

Assessment notes -Collaboration and sharing:

• QGIS F5.1: Via PostGIS or GeoPackage with file locking; no real-time collaboration
• GeoServer F5.2: WFS-T for transactional editing; requires client application
• Earth Engine F5.1: Code sharing; limited simultaneous asset editing
• uMap F5.1: Real-time collaboration in beta development

Technical capability comparison

Deployment and hosting

Deployment details:

Integration architecture

Standards compliance:

Security capability comparison

Authentication and access control

Authentication methods:

Data protection and certifications

Notes:

• Self-hosted tools (QGIS, PostGIS, GeoServer, GeoNode, uMap): encryption and certifications depend on deployment infrastructure
• PostGIS S2.1: PostgreSQL Transparent Data Encryption or filesystem encryption
• Earth Engine S2.4: Limited region selection; data processed in Google data centres globally
• Mapbox S2.4: Data stored in US; limited regional options

Commercial comparison

Pricing models

Infrastructure costs for self-hosted tools (estimated monthly):

Vendor details

Jurisdictional considerations:

• Earth Engine, Mapbox, ArcGIS Online (US HQ): Subject to CLOUD Act; US government can compel access regardless of data location
• Earth Engine: Data processing occurs in Google data centres; limited control over processing location
• Mapbox: Data stored on AWS infrastructure; US-centric
• ArcGIS Online: Regional data centres available; some control over data residency with Enterprise
• Self-hosted options: Complete data sovereignty when deployed on own infrastructure

Detailed tool assessments

QGIS

Type

Open source desktop GIS

Licence

GPL-2.0 -requires source disclosure if distributing modified versions

Current version

3.42 “Münster” (February 2025); 4.0 scheduled February 2026

Deployment options

Desktop (Windows, macOS, Linux); QGIS Server for web services

Source repository

https://github.com/qgis/QGIS

Documentation

https://docs.qgis.org/

Overview

QGIS is a full-featured desktop geographic information system that provides data viewing, editing, and analysis capabilities comparable to commercial alternatives. The software supports over 100 vector and raster formats through GDAL/OGR integration and connects to all major spatial databases including PostGIS, Oracle Spatial, and SQL Server.

The project originated in 2002 and became an OSGeo project in 2007. Development follows a predictable release cycle with feature releases every four months and long-term support releases maintained for one year. The transition to Qt6 with version 4.0 represents the largest architectural change since the Qt5 migration.

QGIS addresses the full GIS workflow from data management through analysis to cartographic output. The print composer produces publication-quality maps, while the processing framework provides access to over 1,000 geoprocessing algorithms including native tools, GDAL, GRASS, and SAGA.

Capability assessment for mapping and GIS

QGIS excels at desktop spatial analysis and cartography. The software handles complex symbology including rule-based rendering, data-driven styling, and 3D visualisation. For web mapping, QGIS Server provides OGC-compliant WMS, WFS, and WMTS services, while the qgis2web plugin exports interactive maps using Leaflet or OpenLayers.

The plugin architecture extends capabilities significantly. Over 1,000 plugins address specialised needs including geocoding, network analysis, remote sensing, and database management. Python scripting through PyQGIS enables automation and custom tool development.

For mission-driven organisations, QGIS eliminates software licensing costs while providing capabilities matching commercial software. The learning curve is moderate; users familiar with other GIS software adapt within days.

Key strengths:

• Complete desktop GIS at no cost with no feature restrictions
• Extensive format support through GDAL/OGR (100+ formats)
• Rich plugin ecosystem addressing specialised requirements
• Publication-quality cartographic output with flexible print composer
• Strong PostGIS integration for enterprise spatial databases
• Active development with predictable release cycle

Key limitations:

• Desktop-focused; web collaboration requires additional components
• Multi-user editing depends on database backends (no native collaboration)
• Performance degrades with very large datasets (10+ million features)
• Some advanced analysis requires plugin installation and configuration
• No cloud-hosted option; requires local installation

Deployment and operations

Desktop requirements:

Operating system: Windows 10/11, macOS 10.15+, Linux (Ubuntu 20.04+)
Memory: 4GB minimum, 16GB recommended for large datasets
Storage: 2GB for installation, variable for data

Server requirements (QGIS Server):

Operating system: Linux (Ubuntu 20.04+, Debian 11+)
Memory: 4GB minimum, 16GB recommended
Dependencies: Apache or Nginx with FastCGI

Deployment complexity: Low for desktop; Medium for server Operational overhead: Low -automatic updates available on Windows/macOS Upgrade path: Direct installation of new versions; projects forward-compatible

Integration capabilities

API coverage: PyQGIS provides complete access to QGIS functionality

Key integrations:

Standards supported: WMS 1.1/1.3, WFS 1.0/1.1/2.0, WMTS 1.0, WCS 1.0/1.1, OGC API Features, SLD 1.0/1.1, GeoPackage 1.2

Security assessment

Authentication: N/A for desktop; QGIS Server supports HTTP Basic, digest, and external authentication Authorisation: Project-level access control; layer visibility settings Data protection: Dependent on operating system and database security Certifications: N/A (desktop software)

Cost analysis

Direct costs:

• Licence: Free
• Support: Community support free; commercial support from multiple vendors (£500-5,000/year)
• Training: Free online resources; commercial training available

Infrastructure costs: Desktop hardware only for basic use

Hidden costs to consider:

• Staff training time for users new to GIS
• Plugin compatibility verification during upgrades
• Server infrastructure if deploying QGIS Server

Organisational fit

Best suited for:

• Organisations requiring full GIS capabilities without licensing costs
• Teams with technical capacity to manage desktop software deployment
• Projects requiring complex spatial analysis and cartographic output
• Environments with existing PostGIS databases

Less suitable for:

• Organisations requiring web-only access with no desktop installation
• Use cases requiring real-time multi-user collaboration
• Contexts where centralised IT management is unavailable

PostGIS

Type

Open source spatial database extension

Licence

GPL-2.0 -requires source disclosure if distributing modified versions

Current version

3.6.1 (November 2025)

Deployment options

PostgreSQL extension (Linux, Windows); Docker; cloud managed services

Source repository

https://github.com/postgis/postgis

Documentation

https://postgis.net/documentation/

Overview

PostGIS extends PostgreSQL with spatial data types, indexes, and functions, transforming a standard relational database into a spatial database capable of storing and querying geographic data. The extension provides over 400 spatial functions covering geometry operations, coordinate transformations, raster processing, and topology.

Originally developed by Refractions Research in 2001, PostGIS became an OSGeo project and remains the most widely deployed open source spatial database. The project maintains compatibility with PostgreSQL versions 13 through 18 and coordinates releases with PostgreSQL’s annual cycle.

PostGIS functions as infrastructure rather than end-user software. Desktop applications (QGIS, ArcGIS), web mapping servers (GeoServer), and custom applications connect to PostGIS for spatial data storage and query processing. This architectural position makes PostGIS foundational to many open source GIS deployments.

Capability assessment for mapping and GIS

PostGIS provides the data management and spatial processing foundation that other tools build upon. Spatial queries execute within the database, reducing data transfer and enabling complex analysis at scale. The ST_* function family supports standard spatial operations: buffers, intersections, unions, and distance calculations.

For large datasets, PostGIS outperforms file-based storage. Spatial indexes (GiST, SP-GiST) enable sub-second queries across millions of features. Parallel query execution, introduced in PostgreSQL 9.6, accelerates computationally intensive operations.

Raster support (PostGIS Raster) stores gridded data alongside vector features, enabling integrated analysis. Point cloud support handles LiDAR data at scale. The pgRouting extension adds network analysis capabilities including shortest path, travelling salesman, and service area calculations.

Key strengths:

• Enterprise-grade spatial database with ACID compliance and replication
• Spatial SQL enables complex queries without application-level processing
• Scales to billions of features with proper indexing
• Integrates with standard PostgreSQL ecosystem (backup, monitoring, administration)
• Raster and point cloud support alongside vector data
• Strong integration with all major GIS applications

Key limitations:

• Database expertise required for installation, tuning, and maintenance
• No user interface; requires client applications for visualisation
• Complex queries require SQL knowledge
• Horizontal scaling requires PostgreSQL-specific approaches (Citus, read replicas)
• Geographic functions assume sphere; some calculations less accurate than geodetic alternatives

Deployment and operations

Self-hosted requirements:

Operating system: Linux (Ubuntu 20.04+, RHEL 8+), Windows Server 2019+
PostgreSQL: Version 13-18
GEOS: 3.8+ (3.14+ for all features)
PROJ: 6.1+
Memory: 4GB minimum, 16GB+ recommended
Storage: Variable (allow 2x raw data size for indexes)

Deployment complexity: Medium -requires PostgreSQL administration knowledge Operational overhead: Medium -regular maintenance (VACUUM, ANALYZE, backups) Upgrade path: pg_upgrade for PostgreSQL; ALTER EXTENSION UPDATE for PostGIS

Cloud managed options:

Integration capabilities

API coverage: SQL access via any PostgreSQL client library

Key integrations:

Standards supported: SQL/MM Spatial, OGC Simple Features, GeoPackage (via ogr2ogr)

Security assessment

Authentication: PostgreSQL methods (password, LDAP, certificate, GSSAPI) Authorisation: PostgreSQL role system; schema and table-level permissions; row-level security Data protection: Transparent Data Encryption (TDE) in PostgreSQL 17+; SSL/TLS connections Audit logging: PostgreSQL logging; pgAudit extension for detailed audit trails

Cost analysis

Direct costs:

• Licence: Free
• Support: Community free; commercial from multiple vendors
• Training: PostgreSQL training widely available

Infrastructure costs:

Hidden costs:

• Database administration expertise or managed service
• Backup storage and testing
• High availability configuration

Organisational fit

Best suited for:

• Organisations with PostgreSQL expertise or willingness to develop it
• Projects requiring spatial queries at database level
• Multi-application environments sharing spatial data
• Large datasets (millions of features) requiring efficient querying

Less suitable for:

• Small projects with simple spatial data needs (consider GeoPackage)
• Organisations without database administration capacity
• Serverless or edge computing scenarios

GeoServer

Type

Open source geospatial server

Licence

GPL-2.0 -requires source disclosure if distributing modified versions

Current version

2.28.0 (October 2025)

Deployment options

WAR deployment (Tomcat, Jetty); Docker; Kubernetes

Source repository

https://github.com/geoserver/geoserver

Documentation

https://docs.geoserver.org/

Overview

GeoServer publishes spatial data from various sources as OGC-compliant web services. The server implements WMS, WFS, WCS, and WMTS standards, enabling interoperability with desktop GIS, web mapping libraries, and other geospatial systems. Recent versions add OGC API support for modern REST-based access patterns.

The project began in 2001 at The Open Planning Project and became an OSGeo project. Development continues with community and commercial contributor support, maintaining quarterly releases and long-term support branches. GeoServer achieved OGC CITE certification in 2025 after a multi-year effort.

GeoServer connects to PostGIS databases, file-based data (Shapefile, GeoPackage, GeoTIFF), cloud storage, and other data sources. The server handles coordinate transformation, styling (SLD and CSS), tile caching (via integrated GeoWebCache), and security. This positions GeoServer as the publication layer in open source spatial data infrastructures.

Capability assessment for mapping and GIS

GeoServer excels at serving spatial data to multiple clients simultaneously. A single GeoServer instance can publish hundreds of layers from diverse sources, applying consistent styling and security. The cascading capabilities allow aggregation of external WMS/WMTS services.

Styling uses SLD (Styled Layer Descriptor) or the simpler CSS extension. Style expressions enable data-driven rendering including classification, scaling, and conditional visibility. The REST API allows programmatic style and layer management.

The WPS extension adds server-side geoprocessing, enabling spatial analysis without client-side processing. This proves valuable for web applications requiring buffer, overlay, or aggregation operations.

Key strengths:

• OGC-compliant service publishing (certified 2025)
• Connects diverse data sources through single interface
• Integrated tile caching (GeoWebCache) for performance
• Flexible styling with SLD and CSS
• REST API for administration and configuration
• Active development with regular security updates

Key limitations:

• Java application requires JVM tuning for optimal performance
• Memory consumption increases with concurrent requests
• Web administration interface has learning curve
• WPS extension requires careful configuration for production use
• No built-in user management (relies on external systems)

Deployment and operations

Self-hosted requirements:

Operating system: Linux (Ubuntu 20.04+), Windows Server 2019+
Java: 17 LTS or 21 LTS
Application server: Tomcat 9+, Jetty 10+
Memory: 4GB minimum, 8GB+ recommended
Storage: Variable (data directory, tile cache)

Docker deployment:

docker pull docker.osgeo.org/geoserver:2.28.0
docker run -d -p 8080:8080 \
  -e GEOSERVER_DATA_DIR=/var/geoserver/data \
  -v geoserver-data:/var/geoserver/data \
  docker.osgeo.org/geoserver:2.28.0

Deployment complexity: Medium -requires Java application server knowledge Operational overhead: Medium -monitoring, cache management, security updates Upgrade path: Replace WAR file; data directory compatible across minor versions

Integration capabilities

API coverage: REST API covers all administrative functions

API details:

Key integrations:

Standards supported: WMS 1.1.1/1.3.0, WFS 1.0/1.1/2.0, WCS 1.0/1.1/2.0, WMTS 1.0, WPS 1.0, OGC API Features/Tiles, SLD 1.0/1.1, SLD-SE 1.1

Security assessment

Authentication: HTTP Basic, digest; extensions for LDAP, OAuth2, SAML Authorisation: Layer, service, and rule-based security with GeoFence integration Data protection: TLS termination at reverse proxy recommended Audit logging: GeoServer logging; detailed request logging available Security track record: Active security response; CVE tracking; timely patches

Cost analysis

Direct costs:

• Licence: Free
• Support: Community free; commercial from GeoSolutions, Boundless (archived), others
• Training: Workshops, commercial training available

Infrastructure costs:

Organisational fit

Best suited for:

• Spatial data infrastructure deployments requiring OGC compliance
• Organisations sharing data with external partners via standard services
• Web mapping applications requiring server-rendered or cached tiles
• Environments with PostGIS databases requiring web publication

Less suitable for:

• Simple static map embedding (consider MapLibre GL JS direct)
• Organisations without Java application server experience
• Resource-constrained environments (high memory requirements)

GeoNode

Type

Open source geospatial content management system

Licence

GPL-3.0 -requires source disclosure if distributing modified versions

Current version

5.0 (2025); 4.4 (January 2025 stable)

Deployment options

Docker (required); Kubernetes

Source repository

https://github.com/GeoNode/geonode

Documentation

https://docs.geonode.org/

Overview

GeoNode provides a complete spatial data infrastructure (SDI) platform combining data management, map creation, and catalogue services in a unified web interface. The platform integrates GeoServer (for OGC services), PostGIS (for storage), pycsw (for catalogue), and MapStore (for web mapping) with Django-based user and content management.

Originally developed by the World Bank and UNEP for the OpenDRI initiative, GeoNode serves organisations requiring spatial data portals without assembling individual components. The software is widely deployed by international organisations, governments, and NGOs for disaster risk management, environmental monitoring, and development planning.

Version 5.0 introduces a redesigned interface, customisable metadata schemas, and vector dataset constraints. The 3D Tiles support added in 4.4 expands capabilities for urban and infrastructure visualisation.

Capability assessment for mapping and GIS

GeoNode excels at spatial data cataloguing and sharing. Users upload datasets through a web interface; GeoNode handles publication to GeoServer, style assignment, metadata extraction, and catalogue registration automatically. The search interface enables discovery across organisational data holdings.

The map composer allows non-technical users to combine layers with basemaps and save shareable map configurations. Permission controls enable public, registered-user, or role-based access to individual resources.

For mission-driven organisations, GeoNode provides immediate value for spatial data sharing without custom development. The self-service upload and metadata editing reduce IT bottlenecks.

Key strengths:

• Complete SDI platform requiring no component assembly
• Self-service data upload with automatic publication
• Integrated metadata catalogue with search
• Fine-grained permission control on resources
• Web-based map creation without GIS software
• Active development with growing feature set
• Wide deployment in humanitarian and development sector

Key limitations:

• Docker-only deployment increases operational complexity
• Resource-intensive (16GB RAM minimum for production)
• Customisation requires Django development skills
• Upgrade process between major versions requires careful planning
• Limited offline functionality
• Analysis capabilities rely on GeoServer WPS

Deployment and operations

Self-hosted requirements:

Operating system: Linux (Ubuntu 20.04+)
Container runtime: Docker 20.10+, Docker Compose 2.0+
Memory: 16GB minimum, 32GB recommended
Storage: 100GB minimum (expandable for data)
CPU: 8 cores recommended

Docker deployment:

git clone https://github.com/GeoNode/geonode.git
cd geonode
cp .env.sample .env
# Edit .env with configuration
docker-compose up -d

Deployment complexity: High -multi-container orchestration, configuration management Operational overhead: Medium-High -container management, backup coordination Upgrade path: Database migration scripts; backup before major version upgrades

Integration capabilities

REST API: Full CRUD for resources, users, groups, maps

Key integrations:

Security assessment

Authentication: Local, LDAP, OAuth2, SAML 2.0, social authentication Authorisation: Resource-level permissions; layer permissions via GeoServer Data protection: TLS termination at reverse proxy; database encryption configurable Audit logging: Django audit logging; GeoServer request logs

Cost analysis

Direct costs:

• Licence: Free
• Support: Community free; commercial from GeoSolutions and others
• Training: Workshops available

Infrastructure costs:

Organisational fit

Best suited for:

• Organisations requiring spatial data portals with cataloguing
• Projects sharing data across teams or with external partners
• Humanitarian and development organisations (strong sector adoption)
• Use cases requiring self-service data upload and management

Less suitable for:

• Simple web mapping without data management requirements
• Resource-constrained infrastructure environments
• Organisations requiring extensive customisation without Django expertise

uMap

Type

Open source collaborative mapping platform

Licence

AGPL-3.0 -requires source disclosure for network use

Current version

3.4.2 (November 2025)

Deployment options

Python/Django application; Docker

Source repository

https://github.com/umap-project/umap

Documentation

https://docs.umap-project.org/

Overview

uMap enables creation of collaborative web maps using OpenStreetMap basemaps without requiring GIS expertise. Users draw features, import data, and share interactive maps through a browser interface. The software prioritises simplicity and accessibility over advanced GIS functionality.

Developed within the OpenStreetMap France community since 2012, uMap powers the public instance umap.openstreetmap.fr hosting over one million maps. The AGPL licence ensures improvements to hosted instances benefit the community.

uMap serves non-technical users creating lightweight interactive maps for communication, community engagement, and simple data visualisation. The software does not compete with GIS platforms for analysis or complex data management.

Capability assessment for mapping and GIS

uMap excels at rapid collaborative map creation. The interface enables drawing points, lines, and polygons with popups containing text, images, and links. Data import supports GeoJSON, GPX, KML, and CSV with coordinates. OpenRouteService integration (version 3.4+) adds route drawing and isochrone generation.

Layer management organises features with separate styling and permissions. Map owners can share editing access via secret URLs or require authentication. Version history tracks changes with rollback capability.

For mission-driven organisations, uMap provides immediate value for project communication, field location mapping, and community engagement without IT infrastructure investment. The public hosted instance eliminates deployment requirements.

Key strengths:

• Minimal learning curve for non-technical users
• No account required to create maps (optional for persistence)
• Collaborative editing with permission controls
• Data layer permissions for granular sharing
• Route and isochrone tools via OpenRouteService
• Public hosted instance available (umap.openstreetmap.fr)
• Lightweight self-hosting option

Key limitations:

• No spatial analysis capabilities
• Limited to OpenStreetMap basemap projections (Web Mercator)
• Maximum dataset size constrained by browser performance
• No attribute table or data editing interface
• Feature queries and filtering limited
• No OGC service publishing

Deployment and operations

Self-hosted requirements:

Operating system: Linux (Ubuntu 20.04+)
Python: 3.10+
Database: PostgreSQL 12+ with PostGIS (or SQLite for development)
Memory: 2GB minimum
Storage: 10GB+ depending on upload volume

Docker deployment:

docker pull umap/umap
docker run -d -p 8000:8000 umap/umap

Deployment complexity: Low-Medium Operational overhead: Low Upgrade path: pip install —upgrade umap-project

Integration capabilities

API: Limited API for map and data layer access

Key integrations:

Security assessment

Authentication: Local accounts, OpenStreetMap OAuth Authorisation: Map-level and layer-level sharing permissions Data protection: TLS termination at reverse proxy

Cost analysis

Direct costs:

• Licence: Free
• Hosting: Public instance free; self-hosted infrastructure only

Infrastructure costs:

Organisational fit

Best suited for:

• Rapid creation of communication-focused maps
• Community engagement and participatory mapping
• Field teams marking locations without GIS training
• Projects requiring simple, shareable web maps
• Organisations preferring hosted services

Less suitable for:

• Spatial analysis requirements
• Large dataset handling (10,000+ features)
• Custom basemap requirements
• Integration with enterprise systems

Google Earth Engine

Type

Commercial cloud geospatial platform

Licence

Proprietary -free for noncommercial research use

Current version

Cloud service (continuous updates)

Deployment options

SaaS only

Source repository

N/A (proprietary)

Documentation

https://developers.google.com/earth-engine

Overview

Google Earth Engine provides planetary-scale analysis of satellite imagery and geospatial datasets using Google Cloud infrastructure. The platform hosts a multi-petabyte archive including Landsat (1972-present), Sentinel, MODIS, and hundreds of other datasets. Users write analysis scripts that execute on Google’s distributed computing infrastructure.

Earth Engine launched in 2010 and remained free for research and nonprofit use until 2022, when commercial pricing was introduced. Nonprofit organisations, academics, and government users from Least Developed Countries retain free access for noncommercial work.

The platform addresses analysis challenges intractable with desktop GIS: continental-scale land cover classification, multi-decadal change detection, and real-time environmental monitoring. Google’s infrastructure handles data storage, processing, and scaling transparently.

Capability assessment for mapping and GIS

Earth Engine excels at raster analysis at scale. The functional programming model applies operations to imagery collections; Earth Engine handles parallel execution across thousands of images. Built-in machine learning algorithms support supervised and unsupervised classification directly on imagery.

For mission-driven organisations working on environmental monitoring, agriculture, or disaster response, Earth Engine provides capabilities unavailable in traditional GIS. Change detection across decades of imagery requires a single script rather than manual processing.

Vector support exists but is secondary to raster capabilities. Earth Engine handles feature collections for analysis (e.g., zonal statistics) but provides limited vector editing or management.

Key strengths:

• Planetary-scale analysis without infrastructure management
• Multi-petabyte curated data archive with continuous updates
• Parallel processing transparent to users
• Interactive code editor for rapid development
• Built-in machine learning classifiers
• Python and JavaScript APIs
• Free for qualifying nonprofit and research use

Key limitations:

• SaaS only; no self-hosting option
• Vendor lock-in with proprietary API
• Data residency concerns (US-headquartered; CLOUD Act applies)
• Learning curve for functional programming model
• Limited vector editing and management
• Export quotas and processing limits
• Commercial use requires paid subscription

Integration capabilities

API: Python and JavaScript client libraries; REST API

API details:

Key integrations:

Security assessment

Authentication: Google OAuth 2.0, service accounts Authorisation: Cloud project-based access control Data protection: Google Cloud security controls; data encrypted at rest and in transit Certifications: Google Cloud certifications (SOC 2, ISO 27001, etc.)

Jurisdictional considerations: Google LLC is US-headquartered. Data processing occurs in Google data centres globally. The CLOUD Act authorises US government access to data regardless of storage location. This presents compliance challenges for organisations with data sovereignty requirements.

Cost analysis

Direct costs:

• Noncommercial use: Free (verified eligibility required)
• Commercial use: Platform fee ($1,000-12,000/month) plus usage charges

Commercial pricing (2025):

Organisational fit

Best suited for:

• Environmental monitoring, land cover mapping, climate research
• Organisations qualifying for free noncommercial access
• Projects requiring analysis of archived satellite imagery
• Applications needing continental or global scale analysis

Less suitable for:

• Organisations with data sovereignty requirements
• Primarily vector-focused workflows
• Projects requiring self-hosted infrastructure
• Commercial applications with unpredictable processing needs

Mapbox

Type

Commercial mapping platform

Licence

Proprietary -usage-based pricing

Current version

Mapbox GL JS v3.x (continuous updates)

Deployment options

SaaS (APIs and hosted services)

Source repository

https://github.com/mapbox/mapbox-gl-js (source-available)

Documentation

https://docs.mapbox.com/

Overview

Mapbox provides web and mobile mapping APIs, SDKs, and design tools for developers embedding maps in applications. The platform includes vector tile hosting, geocoding, routing, and the Mapbox GL rendering engine enabling dynamic styling and smooth interaction.

Founded in 2010, Mapbox grew from OpenStreetMap-based mapping services to a comprehensive location platform. The company serves developers building consumer applications rather than traditional GIS users.

Mapbox GL JS (version 3) renders vector tiles with WebGL, enabling runtime style changes, 3D terrain, and complex data visualisation. The style specification is openly documented, and the rendering engine fork MapLibre GL JS provides an open source alternative.

Capability assessment for mapping and GIS

Mapbox excels at interactive web and mobile mapping for applications. The vector tile approach delivers smooth zooming, fast load times, and small bandwidth requirements compared to raster tiles. Client-side rendering enables data-driven styling without server round trips.

For mission-driven organisations, Mapbox provides developer-friendly APIs for embedding maps in websites and applications. The Studio design tool enables custom basemap creation without coding. Directions and geocoding APIs add location features to applications.

Mapbox does not provide GIS analysis capabilities. The platform focuses on display and navigation rather than spatial analysis or data management.

Key strengths:

• High-performance vector tile rendering
• Smooth mobile and web experience
• Comprehensive design tools (Mapbox Studio)
• Well-documented APIs with examples
• Navigation SDK for turn-by-turn directions
• Global basemap data with regular updates
• Generous free tier for development

Key limitations:

• No spatial analysis capabilities
• Proprietary; no self-hosting option
• Data stored on Mapbox infrastructure (US)
• No nonprofit pricing programme
• Custom data upload requires MTS (Mapbox Tiling Service)
• Licence restrictions on forking GL JS v2+

Integration capabilities

APIs:

SDKs: JavaScript, iOS, Android, Qt, React Native, Unity

Key integrations:

Security assessment

Authentication: Access tokens (public, secret, with scope restrictions) Authorisation: Token-based; URL restrictions available Data protection: TLS encryption; uploaded data stored encrypted Certifications: SOC 2 Type II

Cost analysis

Pricing model: Usage-based; map loads or API requests

Web maps pricing (2025):

Estimated monthly costs:

Organisational fit

Best suited for:

• Web and mobile application development requiring embedded maps
• Projects prioritising user experience and performance
• Developers comfortable with API-based services
• Applications requiring geocoding and routing

Less suitable for:

• Desktop GIS or spatial analysis workflows
• Organisations requiring self-hosted infrastructure
• Projects with data sovereignty requirements
• Budget-constrained projects expecting high traffic

ArcGIS (Esri)

Type

Commercial GIS platform

Licence

Proprietary -subscription-based

Current version

ArcGIS Pro 3.4; ArcGIS Online (continuous updates)

Deployment options

Desktop (ArcGIS Pro), SaaS (ArcGIS Online), Self-hosted (ArcGIS Enterprise)

Source repository

N/A (proprietary)

Documentation

https://developers.arcgis.com/ and https://doc.arcgis.com/

Overview

ArcGIS represents the industry-leading commercial GIS platform, providing integrated desktop, server, and cloud capabilities. Esri, founded in 1969, maintains dominant market share in government, utilities, and enterprise markets. The platform encompasses desktop GIS (ArcGIS Pro), cloud GIS (ArcGIS Online), self-hosted server (ArcGIS Enterprise), and mobile solutions.

The ArcGIS ecosystem includes specialised tools for every GIS domain: geodatabase management, spatial analysis, 3D visualisation, real-time data, field data collection, and web application development. Integration across components enables workflows spanning desktop analysis to web publication.

For mission-driven organisations, Esri provides a Nonprofit Program with significant discounts. Enterprise agreements bundle core products, training, and support at reduced rates for qualifying organisations.

Capability assessment for mapping and GIS

ArcGIS provides comprehensive GIS capabilities exceeding any single open source alternative. ArcGIS Pro offers advanced spatial analysis, 3D modelling, imagery processing, and cartography. ArcGIS Online enables web mapping, hosted feature services, and ready-to-use applications without server management.

The platform includes specialised capabilities unavailable elsewhere: network analysis with sophisticated turn modelling, utility network management, parcel fabric for cadastral data, and deep learning model integration for imagery.

For organisations requiring complete GIS solutions with vendor support, ArcGIS delivers unmatched breadth. The cost premium reflects comprehensive capabilities, support, and ecosystem.

Key strengths:

• Most complete GIS capabilities available
• Integrated desktop, server, and cloud components
• Comprehensive support and training resources
• Regular updates with new capabilities
• Strong documentation and community
• Nonprofit program with significant discounts
• Standards compliance (OGC services)

Key limitations:

• Highest cost among evaluated options
• Vendor lock-in with proprietary formats and APIs
• Complex licensing with multiple tiers and add-ons
• US-headquartered (CLOUD Act applies to Online)
• Desktop requires Windows (macOS support limited)
• Learning curve for full platform capabilities

Deployment and operations

ArcGIS Pro requirements:

Operating system: Windows 10/11 (64-bit)
Memory: 8GB minimum, 32GB recommended
Graphics: Dedicated GPU recommended for 3D
Storage: 10GB for installation

ArcGIS Enterprise requirements:

Operating system: Windows Server 2019+, Linux (select components)
Memory: 32GB minimum
CPU: 16 cores recommended
Storage: Variable (100GB+ typical)

Integration capabilities

APIs: REST APIs, JavaScript API, Python API (ArcPy, ArcGIS API for Python)

SDKs: JavaScript, Python, .NET, Java, Swift, Kotlin

Key integrations:

Standards supported: WMS, WFS, WMTS, WCS, OGC API Features, GeoPackage, KML

Security assessment

Authentication: Local, LDAP, SAML 2.0, OIDC, PKI Authorisation: Role-based access control, feature-level security Data protection: Encryption at rest and in transit; regional data centres Certifications: SOC 2, ISO 27001, FedRAMP (US government)

Cost analysis

Pricing model: Per-user subscription (user types)

User type pricing (standard, 2025):

Nonprofit pricing: 20-50% discount through Esri Nonprofit Program

Enterprise agreement: Bundled pricing for organisations with multiple users

Organisational fit

Best suited for:

• Organisations requiring comprehensive GIS capabilities
• Projects with budget for commercial licensing
• Environments valuing vendor support and training
• Use cases requiring specialised tools (utilities, transportation, etc.)
• Integration with enterprise systems (Microsoft, SAP)

Less suitable for:

• Budget-constrained organisations (despite nonprofit discounts)
• Organisations prioritising open source or open standards
• Projects with strict data sovereignty requirements (for Online)
• Linux-only environments (Pro requires Windows)

Selection guidance

Decision framework

                           +---------------------------+
                           | Primary use case?         |
                           +---------------------------+
                                      |
           +------------+-------------+-------------+------------+
           |            |             |             |            |
           v            v             v             v            v
    +----------+  +----------+  +----------+  +----------+  +----------+
    | Desktop  |  | Spatial  |  | Web map  |  | Data     |  | Satellite|
    | analysis |  | database |  | sharing  |  | portal   |  | analysis |
    +----+-----+  +----+-----+  +----+-----+  +----+-----+  +----+-----+
         |             |             |             |             |
         v             v             v             v             v
    +----------+  +----------+  +----------+  +----------+  +----------+
    | Budget?  |  | PostGIS  |  | Technical|  | GeoNode  |  | Earth    |
    |          |  |          |  | capacity?|  |          |  | Engine   |
    +----+-----+  +----------+  +----+-----+  +----------+  +----------+
         |                           |
    +----+----+                +-----+-----+
    |         |                |           |
    v         v                v           v
+-------+ +-------+       +-------+   +-------+
| QGIS  | |ArcGIS |       | uMap  |   |Mapbox |
| (free)| | (paid)|       |(simple)|  |GeoSrvr|
+-------+ +-------+       +-------+   +-------+

Recommendations by context

Minimal IT capacity

Primary recommendation: uMap (hosted instance)

• No infrastructure required
• Immediate use without installation
• Suitable for simple mapping needs

Alternative: ArcGIS Online with Nonprofit Programme

• Managed service with comprehensive capabilities
• Support and training included
• Higher cost but lower operational burden

Avoid: GeoNode, self-hosted GeoServer

• Container orchestration expertise required
• Ongoing maintenance demands

Established IT function

Primary recommendation: QGIS + PostGIS + GeoServer stack

• Complete open source SDI
• No licensing costs
• Full control over data and infrastructure

Alternative: GeoNode

• Pre-integrated SDI platform
• Self-service data management
• Reduces component integration effort

Data sovereignty requirements

Primary recommendation: Self-hosted open source stack

• Complete control over data location
• No cloud dependencies
• Avoid: Google Earth Engine, Mapbox, ArcGIS Online

Configuration: PostGIS (database) + GeoServer (services) + QGIS (desktop)

Field operations with limited connectivity

Primary recommendation: QGIS with offline data (GeoPackage)

• Full desktop GIS without network
• Offline basemaps via QuickMapServices
• Sync with PostGIS when connected

Alternative: ArcGIS Field Maps with offline areas

• Managed offline workflows
• Sync handling built-in
• Requires ArcGIS Online subscription

Migration paths

External resources

Official documentation

Relevant standards

See also

• Data Collection Tools
• M&E Platforms
• Beneficiary Identity Systems
• Data Platform Architecture