A collection of tools for training and using AI models that work with geospatial and tabular data, integrating machine learning and deep learning techniques with GIS for classification, regression, and natural language processing tasks.
ArcGIS GeoAI Toolbox is a Geospatial AI toolset built into ArcGIS Pro that combines machine learning, deep learning, and natural language processing with GIS workflows for classification, regression, object detection, and time-series forecasting, with pricing bundled into an ArcGIS Pro license (typically starting at $700/year for a Basic named user subscription). It targets GIS analysts, remote sensing specialists, urban planners, and data scientists who need production-grade geospatial AI without leaving their mapping environment.
The toolbox is organized into four specialized toolsets: Feature and Tabular Analysis (which applies automated machine learning to train, fine-tune, and ensemble the best models for categorical and continuous prediction tasks), Imagery AI (which runs object detection and pixel classification deep learning algorithms on raster and imagery data), Text Analysis (which performs NLP tasks like classification, transformation, and entity extraction such as addresses from unstructured text), and Time Series AI (which forecasts future values at specific locations within a space-time cube). Models created here are fully interoperable with the ArcGIS API for Python arcgis.learn module and pretrained models available through ArcGIS Living Atlas of the World, so teams can fine-tune existing models on their labeled data rather than training from scratch.
Compared to the other geospatial AI tools in our directory, GeoAI Toolbox is distinguished by its deep integration with the Esri ecosystem â the dominant enterprise GIS platform used by over 350,000 organizations worldwide â rather than being a standalone ML library. Based on our analysis of 870+ AI tools, it sits at the intersection of enterprise GIS and modern deep learning: more accessible than raw PyTorch workflows for GIS professionals, but requires installing separate deep learning framework libraries (PyTorch, TensorFlow, fastai) through Esri's Deep Learning Libraries Installers. It is best suited to organizations already invested in ArcGIS Pro, rather than teams starting fresh with open-source stacks like QGIS plus scikit-learn.
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Applies automated machine learning to feature classes and tables, training, tuning, and ensembling multiple models to predict categorical variables (classification) and continuous variables (regression). The automation handles algorithm selection and hyperparameter tuning so GIS analysts without ML backgrounds can still produce competitive models on their data.
Runs deep learning object detection and pixel classification on raster and imagery data, supporting workflows such as building footprint extraction, road network mapping, and land cover classification. It leverages pretrained models from ArcGIS Living Atlas and custom models trained in the arcgis.learn module.
Performs natural language processing directly on text fields in feature classes and tables, including classification, transformation, and entity extraction for items like addresses and named places. Users can fine-tune pretrained NLP models from ArcGIS Living Atlas or train custom models using labeled text data.
Forecasts and estimates future values at specific locations inside a space-time cube, which is Esri's native structure for spatiotemporal data. This enables forecasting applications like predicting crime rates, sales, or environmental measurements at a neighborhood or sensor level rather than just in aggregate.
Models created by the GeoAI tools are fully compatible with the arcgis.learn module in the ArcGIS API for Python, so data scientists can continue training, fine-tuning, and deploying models in code. This bridges the gap between GUI-driven GIS analysts and Python-driven ML engineers working on the same project.
$700/year
$1,400/year
$2,800/year
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ArcGIS Pro 3.4 (released late 2025) expanded the GeoAI Toolbox with improved AutoML model explainability, additional pretrained deep learning models in ArcGIS Living Atlas for building footprint and land cover classification, enhanced support for transformer-based NLP models in the Text Analysis toolset, and performance optimizations for GPU-accelerated inference in the Imagery AI toolset. The 2026 update cycle has focused on tighter integration with ArcGIS Online for publishing trained GeoAI models as hosted web tools, broader compatibility with newer PyTorch and ONNX runtime versions in the Deep Learning Libraries Installer, and expanded time series forecasting methods in the Time Series AI toolset.
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