Geospatial AI

SuperMap AI GIS

Name: SuperMap AI GIS
Brand: SuperMap AI GIS
Price: 2 USD
Availability: InStock

Geospatial artificial intelligence platform integrated with SuperMap's GIS software suite for advanced spatial data analysis and mapping.

Starting at~$2,000–$5,000/seat (estimated)

Visit SuperMap AI GIS →

Overview

SuperMap AI GIS is a Geospatial AI platform that integrates artificial intelligence with traditional GIS workflows to enable spatial data analysis, remote sensing interpretation, and geospatial machine learning, with enterprise-tier pricing available through SuperMap sales channels. It targets natural resource agencies, smart city planners, transportation authorities, and remote sensing analysts who need production-grade spatial intelligence.

Built around three pillars — GeoAI (spatial analysis algorithms enhanced with AI), AI for GIS (AI-driven UX improvements to the SuperMap suite), and GIS for AI (visualization and management of GeoAI outputs) — the platform spans server, desktop, component, and mobile terminals. It supports the full deep learning lifecycle: batch training data generation, automatic learning rate initialization, batch reasoning, and reasoning constrained to user-defined ranges. Built-in deep learning models cover object detection (Cascade R-CNN, Faster R-CNN, RetinaNet), binary and ground-object classification (FPN, DeepLabv3+, U-Net, D-LinkNet, SFNet, Segformer), scene classification (EfficientNet), object extraction (Mask R-CNN), and change detection (DSAMNet, Siam-SFNet, Siam-Segformer). Desktop users can train YOLO v7 series models for video AI, while server deployments handle intelligent image interpretation against live image services.

For statistical and machine learning workflows, the suite includes simple/systematic/stratified sampling, geospatial random and stratified sampling, sandwich sampling, SPA and B-Shade models, hotspot analysis, density clustering, k-means, mean shift, map matching, address element identification, forest-based classification and regression, geographically weighted regression, and spatiotemporal GWR. SuperMap AI GIS is one of the most comprehensive enterprise GeoAI suites alongside Esri ArcGIS — distinguished by its tighter coupling with the SuperMap GIS 2025 stack (Cloud GIS Server, Edge GIS Server, Terminal GIS for Desktop/Components/Web/Mobile) and its strength in the Asia-Pacific market. Compared to open-source alternatives like QGIS plugins, it offers a unified, vendor-supported pipeline from data prep to deployment.

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Key Features

Comprehensive deep learning model zoo+

Ships with 15+ pre-integrated architectures across detection (Cascade R-CNN, Faster R-CNN, RetinaNet), segmentation (FPN, DeepLabv3+, U-Net, D-LinkNet, SFNet, Segformer), scene classification (EfficientNet), object extraction (Mask R-CNN), and change detection (DSAMNet, Siam-SFNet, Siam-Segformer). Teams can train and infer without building models from scratch. YOLO v7 video AI is also supported on desktop.

End-to-end AI GIS workflow tooling+

Covers the full ML lifecycle inside GIS: batch generation of training samples at the data preparation stage, automatic learning rate initialization during model construction, and batch reasoning plus range-constrained reasoning at the application stage. This reduces hand-tuning and lets analysts iterate on models without leaving the SuperMap environment.

Geospatial sampling and statistical inference+

Provides simple random, systematic, and stratified sampling, plus geospatial random sampling, geospatial stratified sampling, and sandwich sampling. Inference is backed by SPA and B-Shade models, which is unusually rigorous for an AI GIS suite and supports policy and scientific workflows that require defensible statistical methodology.

Geospatial machine learning library+

Includes hotspot analysis, density clustering, k-means, mean shift, map matching, address element identification, forest-based classification and regression, geosimulation, and both standard and spatiotemporal Geographically Weighted Regression. Together these cover clustering, classification, and regression workflows specifically tuned for spatial data.

Cross-terminal deployment across the SuperMap GIS 2025 stack+

AI capabilities are available across Cloud GIS Server, Edge GIS Server, Terminal GIS for Desktop, Components, Web, and Mobile. Server-side handles image-service interpretation, components train and evaluate remote sensing models, desktop runs video AI training, and mobile performs on-device object detection and classification — enabling consistent AI from datacenter to field device.

Pricing Plans

SuperMap iDesktop (AI GIS Desktop)

~$2,000–$5,000/seat (estimated)

✓Desktop GIS with AI GIS module
✓Full deep learning model zoo (15+ architectures)
✓YOLO v7 video AI model training
✓Batch training data generation and auto learning rate initialization
✓Geospatial sampling and statistical inference (SPA, B-Shade)
✓Geospatial ML library (clustering, GWR, forest-based classification)
✓Single-seat perpetual or annual license options
✓Standard vendor support and software updates

SuperMap iServer (AI GIS Server)

~$8,000–$25,000/node (estimated)

✓Cloud GIS Server with AI GIS capabilities
✓Intelligent image interpretation against live image services
✓Batch and range-based reasoning at scale
✓Server-side deep learning inference for remote sensing
✓REST API access for AI model deployment
✓Multi-user concurrent access
✓Priced per CPU core or server node
✓Premium vendor support with SLA options

SuperMap iEdge (Edge AI GIS)

~$3,000–$8,000/node (estimated)

✓Edge GIS Server for latency-sensitive AI inference
✓On-device object detection and classification
✓Lightweight deployment for field and in-vehicle systems
✓Integration with Cloud GIS Server for model management
✓Optimized for bandwidth-constrained environments

SuperMap iObjects (Component AI GIS)

~$5,000–$15,000/seat (estimated)

✓Developer components for embedding AI GIS in custom applications
✓Remote sensing model training, reasoning, and evaluation APIs
✓Available for .NET, Java, and C++ development
✓Deep learning model integration via component interfaces
✓OEM and redistribution licensing available

SuperMap AI GIS Enterprise Bundle

~$50,000–$200,000+ (estimated)

✓Full SuperMap GIS 2025 stack with AI GIS across all terminals
✓Cloud GIS Server + Edge GIS Server + Desktop + Components + Web + Mobile
✓All deep learning models and geospatial ML algorithms included
✓Multilingual UI support for global deployments
✓Industry solution packages (Smart City, Natural Resources, Transportation, Public Safety, BIM+GIS)
✓Dedicated account management and priority support
✓Volume and multi-year discount pricing (typically 15–30% off)
✓On-prem, private cloud, or hybrid deployment options
✓Training and onboarding services available

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Ready to get started with SuperMap AI GIS?

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Best Use Cases

🎯

Natural resource monitoring agencies running AI + remote sensing pipelines for land-cover classification, target detection, and multi-temporal change detection across satellite or aerial imagery

⚡

Smart city teams deploying density clustering, hotspot analysis, and address element identification across municipal datasets to inform planning and public services

🔧

Transportation authorities applying map matching, GWR, and spatiotemporal GWR to model traffic patterns and route demand at scale

🚀

Public safety and disaster response groups training YOLO v7 video AI models on desktop for surveillance feeds and deploying object detection to mobile terminals for field operations

💡

Water conservancy and BIM+GIS engineering teams combining 3D GIS with deep-learning change detection (DSAMNet, Siam-Segformer) to monitor infrastructure over time

🔄

Enterprises already standardized on SuperMap GIS 2025 (Cloud/Edge/Desktop/Components/Web/Mobile) that need to add AI capabilities without migrating their core spatial stack

Limitations & What It Can't Do

We believe in transparent reviews. Here's what SuperMap AI GIS doesn't handle well:

⚠No transparent self-serve pricing — buyers must engage SuperMap sales for quotes and licensing
⚠Best value is realized only when paired with the wider SuperMap GIS suite; standalone adoption is limited
⚠English-language documentation, training files, and community Q&A are thinner than the Chinese-language equivalents
⚠Deep learning offering centers on raster/imagery tasks; users needing graph neural networks or trajectory-specific transformers may need to extend the platform
⚠Cloud-native, fully managed SaaS deployment is not the default model — most installations are on-prem or private cloud, which adds infrastructure overhead

Pros & Cons

✓ Pros

✓Comprehensive deep learning model zoo with 15+ pre-built architectures spanning detection, classification, segmentation, and change detection
✓Tightly integrated across the full SuperMap GIS 2025 stack — Cloud GIS Server, Edge GIS Server, and four terminal types (Desktop, Components, Web, Mobile)
✓Includes both classical geospatial statistics (SPA, B-Shade, GWR) and modern deep learning, which is rarer in pure-AI GIS tools
✓Workflow automation for the full ML lifecycle: batch training data generation, auto learning rate init, and batch/range-based reasoning
✓Available in multiple languages including English, Chinese, Spanish, French, Arabic, Russian, Japanese, and Korean — strong fit for global enterprise rollouts
✓Vendor-supported solution with industry-specific verticals (Smart City, Natural Resources, Public Safety, Water Conservancy, Transportation, BIM+GIS)

✗ Cons

✗No public pricing — requires direct sales contact, making evaluation slower than self-serve competitors
✗Steep learning curve tied to the broader SuperMap GIS ecosystem; not a standalone AI tool
✗Documentation and community resources skew toward Chinese-language audiences despite the multilingual UI
✗Deep learning model list emphasizes image/remote sensing tasks — fewer first-class options for vector-only or graph-based geospatial AI
✗Smaller global third-party plugin ecosystem compared to ArcGIS or QGIS

Frequently Asked Questions

What is the difference between GeoAI, AI for GIS, and GIS for AI in SuperMap?+

SuperMap frames AI GIS as three integrated layers. GeoAI refers to spatial analysis algorithms and process tools enhanced with AI — for example, density clustering or address element identification powered by ML. AI for GIS uses AI to improve the SuperMap software itself, such as smarter interactive UX and intelligent automation inside the desktop and server products. GIS for AI is the inverse: using GIS capabilities to manage, visualize, and analyze the outputs of AI models, like displaying detection results from a remote sensing model on a map. Together they make AI a first-class citizen across the SuperMap GIS 2025 stack.

Which deep learning models does SuperMap AI GIS include out of the box?+

The platform ships with a wide model zoo organized by task. Object detection includes Cascade R-CNN, Faster R-CNN, and RetinaNet. Semantic segmentation for binary and ground-object classification includes FPN, DeepLabv3+, U-Net, D-LinkNet, SFNet, and Segformer. Scene classification uses EfficientNet, object extraction uses Mask R-CNN, and change detection is handled by DSAMNet, Siam-SFNet, and Siam-Segformer. On desktop, users can also train YOLO v7 series models for video AI, giving teams 15+ architectures without writing model code from scratch.

How does SuperMap AI GIS compare to Esri ArcGIS for geospatial AI?+

Both are enterprise-grade and offer deep learning toolkits, but they differ in ecosystem and reach. SuperMap is tightly bound to the SuperMap GIS 2025 stack (Cloud, Edge, Desktop, Components, Web, Mobile) and has particularly strong adoption across Asia-Pacific markets, with multiple localized UI languages. Esri ArcGIS has a larger global community, more third-party extensions, and deeper US/EU government adoption. Choose SuperMap if you already run SuperMap GIS or need an Asia-Pacific–optimized stack; choose ArcGIS for the broader plugin ecosystem and partner network.

Can SuperMap AI GIS run on mobile and edge devices, or only on servers?+

Yes, SuperMap AI GIS is explicitly cross-platform across the SuperMap GIS 2025 architecture. Server-side capabilities include augmented intelligent image interpretation against image services. Component-terminal workflows support remote sensing model training, reasoning, and evaluation. Desktop adds video AI with YOLO v7 training, while the mobile terminal supports AI object detection and classification on the device. Edge GIS Server is also part of the stack, so inference can be deployed close to data sources for latency-sensitive applications like field surveys or in-vehicle systems.

What industries and use cases is SuperMap AI GIS designed for?+

SuperMap markets the platform across eight industry solutions: Smart City, Natural Resources, Land Management, Facility Management, Public Safety, Natural Disasters, Transportation, Water Conservancy, and BIM+GIS. Typical use cases include AI plus remote sensing for natural resource monitoring (target detection, category segmentation, multi-temporal change), urban land-use classification, traffic and transportation analytics, and disaster response mapping. The combination of geospatial sampling, statistical inference (SPA, B-Shade), and deep learning makes it a fit for both operational monitoring and policy-grade spatial research.

How much does SuperMap AI GIS cost?+

SuperMap does not publish public pricing. The figures below are rough estimates based on industry benchmarks and limited reseller data, and actual prices may differ significantly depending on region, volume, and negotiation. Estimated ranges: iDesktop with the AI GIS module may cost approximately $2,000–$5,000 per seat annually; iServer may range from approximately $8,000–$25,000 per server node annually depending on core count and capacity; iEdge deployments may run approximately $3,000–$8,000 annually. Component (iObjects) licensing varies widely based on OEM terms and may run approximately $5,000–$15,000 per developer seat. Enterprise bundles spanning the full stack are typically negotiated in the $50,000–$200,000+ range annually depending on scale, user count, and included industry solution packages. Multi-year and volume discounts of 15–30% are common. These are unverified estimates — contact SuperMap sales directly or request an evaluation license to get a precise quote for your deployment scenario.

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More about SuperMap AI GIS

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Overview

Key Features

Comprehensive deep learning model zoo+

End-to-end AI GIS workflow tooling+

Geospatial sampling and statistical inference+

Geospatial machine learning library+

Cross-terminal deployment across the SuperMap GIS 2025 stack+

Pricing Plans

SuperMap iDesktop (AI GIS Desktop)

~$2,000–$5,000/seat (estimated)

✓Desktop GIS with AI GIS module
✓Full deep learning model zoo (15+ architectures)
✓YOLO v7 video AI model training
✓Batch training data generation and auto learning rate initialization
✓Geospatial sampling and statistical inference (SPA, B-Shade)
✓Geospatial ML library (clustering, GWR, forest-based classification)
✓Single-seat perpetual or annual license options
✓Standard vendor support and software updates

SuperMap iServer (AI GIS Server)

~$8,000–$25,000/node (estimated)

✓Cloud GIS Server with AI GIS capabilities
✓Intelligent image interpretation against live image services
✓Batch and range-based reasoning at scale
✓Server-side deep learning inference for remote sensing
✓REST API access for AI model deployment
✓Multi-user concurrent access
✓Priced per CPU core or server node
✓Premium vendor support with SLA options

SuperMap iEdge (Edge AI GIS)

~$3,000–$8,000/node (estimated)

✓Edge GIS Server for latency-sensitive AI inference
✓On-device object detection and classification
✓Lightweight deployment for field and in-vehicle systems
✓Integration with Cloud GIS Server for model management
✓Optimized for bandwidth-constrained environments

SuperMap iObjects (Component AI GIS)

~$5,000–$15,000/seat (estimated)

✓Developer components for embedding AI GIS in custom applications
✓Remote sensing model training, reasoning, and evaluation APIs
✓Available for .NET, Java, and C++ development
✓Deep learning model integration via component interfaces
✓OEM and redistribution licensing available

SuperMap AI GIS Enterprise Bundle

~$50,000–$200,000+ (estimated)

✓Full SuperMap GIS 2025 stack with AI GIS across all terminals
✓Cloud GIS Server + Edge GIS Server + Desktop + Components + Web + Mobile
✓All deep learning models and geospatial ML algorithms included
✓Multilingual UI support for global deployments
✓Industry solution packages (Smart City, Natural Resources, Transportation, Public Safety, BIM+GIS)
✓Dedicated account management and priority support
✓Volume and multi-year discount pricing (typically 15–30% off)
✓On-prem, private cloud, or hybrid deployment options
✓Training and onboarding services available

Ready to get started with SuperMap AI GIS?

View Pricing Options →

Best Use Cases

🎯

Natural resource monitoring agencies running AI + remote sensing pipelines for land-cover classification, target detection, and multi-temporal change detection across satellite or aerial imagery

⚡

Smart city teams deploying density clustering, hotspot analysis, and address element identification across municipal datasets to inform planning and public services

🔧

Transportation authorities applying map matching, GWR, and spatiotemporal GWR to model traffic patterns and route demand at scale

🚀

Public safety and disaster response groups training YOLO v7 video AI models on desktop for surveillance feeds and deploying object detection to mobile terminals for field operations

💡

Water conservancy and BIM+GIS engineering teams combining 3D GIS with deep-learning change detection (DSAMNet, Siam-Segformer) to monitor infrastructure over time

🔄

Enterprises already standardized on SuperMap GIS 2025 (Cloud/Edge/Desktop/Components/Web/Mobile) that need to add AI capabilities without migrating their core spatial stack

Limitations & What It Can't Do

We believe in transparent reviews. Here's what SuperMap AI GIS doesn't handle well:

⚠No transparent self-serve pricing — buyers must engage SuperMap sales for quotes and licensing

⚠Best value is realized only when paired with the wider SuperMap GIS suite; standalone adoption is limited

⚠English-language documentation, training files, and community Q&A are thinner than the Chinese-language equivalents

⚠Deep learning offering centers on raster/imagery tasks; users needing graph neural networks or trajectory-specific transformers may need to extend the platform

⚠Cloud-native, fully managed SaaS deployment is not the default model — most installations are on-prem or private cloud, which adds infrastructure overhead

Pros & Cons

✓ Pros

✓Comprehensive deep learning model zoo with 15+ pre-built architectures spanning detection, classification, segmentation, and change detection
✓Tightly integrated across the full SuperMap GIS 2025 stack — Cloud GIS Server, Edge GIS Server, and four terminal types (Desktop, Components, Web, Mobile)
✓Includes both classical geospatial statistics (SPA, B-Shade, GWR) and modern deep learning, which is rarer in pure-AI GIS tools
✓Workflow automation for the full ML lifecycle: batch training data generation, auto learning rate init, and batch/range-based reasoning
✓Available in multiple languages including English, Chinese, Spanish, French, Arabic, Russian, Japanese, and Korean — strong fit for global enterprise rollouts
✓Vendor-supported solution with industry-specific verticals (Smart City, Natural Resources, Public Safety, Water Conservancy, Transportation, BIM+GIS)

✗ Cons

✗No public pricing — requires direct sales contact, making evaluation slower than self-serve competitors
✗Steep learning curve tied to the broader SuperMap GIS ecosystem; not a standalone AI tool
✗Documentation and community resources skew toward Chinese-language audiences despite the multilingual UI
✗Deep learning model list emphasizes image/remote sensing tasks — fewer first-class options for vector-only or graph-based geospatial AI
✗Smaller global third-party plugin ecosystem compared to ArcGIS or QGIS