GeoAI is the integration of artificial intelligence (AI) with spatial data, science, and geospatial technology to increase understanding and solve spatial problems. GeoAI includes the application of traditional AI techniques to generate spatial data through the extraction, classification, and detection of information from structured and unstructured data. GeoAI is also the use of spatially explicit AI techniques that are designed to solve spatial problems through the analysis of spatial data, and includes techniques for detecting patterns, making predictions, spatiotemporal forecasting, and more.
Key concepts
The following terms will help you understand the use of GeoAI in ArcGIS Pro:
- Spatial analysis—The process of examining the locations, attributes, and relationships in spatial data through a series of techniques from simple overlays to advanced spatial statistics and other analytical techniques.
- AI—The ability of a machine (for example, a computer) to perform tasks that traditionally require human intelligence, such as perception, reasoning, and learning. AI applications are everywhere now in our daily lives—in smart assistants on phones, in recommendations on social media feeds, and in self-driving cars and robots. AI encompasses both machine learning and deep learning.
- Machine learning (ML)—A subset of AI referring to a set of techniques that allow computers to learn patterns within data and acquire knowledge without being explicitly programmed. ML techniques typically come in the form of statistical methods or data-driven algorithms that solve classification, clustering, and prediction (for example, regression and forecasting) problems. ML can be thought of as an approach to achieve AI.
- Deep learning (DL)—A subset of ML that uses trainable and learned algorithms in the form of artificial neural networks. The multilayered architecture of these networks is inspired by how the human brain operates—humans learn about and understand the world around them as a nested hierarchy of concepts. DL algorithms can be thought of as functioning similar to the human brain, in which the computer learns complex patterns and concepts by piecing together simpler concepts. Raw input data is analyzed through different layers of the network, with each successive layer learning and capturing the definitions of more complex and specific features and patterns in the data.
GeoAI is embedded throughout ArcGIS across a wide variety of geoprocessing and exploratory analysis tools. ML algorithms in ArcGIS are used in the analysis of spatial data to perform clustering, prediction (classification and regression), and spatiotemporal forecasting. DL is used in ArcGIS for the generation of geospatial information from sensor data (including imagery and point clouds) using techniques and tools for pixel classification and image segmentation, detecting objects and extracting features, object tracking, change detection, and image simulation. DL is also used to generate geospatial data from unstructured text using a variety of natural language processing techniques. DL can also be used for the analysis of spatial data to make predictions and forecasts. Many challenging problems, however, require bringing together GeoAI and other spatial analysis techniques to both understand and effectively address these challenges.
Problem solving
GeoAI can play a role in spatial problem solving in a wide range of application areas.
An important aspect of GeoAI is the application of traditional AI techniques in the generation of spatial data through extraction, classification, and detection of information from structured and unstructured data. This data includes tabular data, remotely sensed data (including raster, imagery, lidar point clouds, video, and more), and text data. This spatial data generation includes applications such as finding and cataloging objects in imagery, creating 3D data from lidar, or extracting location information from unstructured text for subsequent geocoding. ArcGIS also includes a set of pretrained DL models that reduce some of the most time-consuming and resource-intensive aspects of the training process. The use of DL to automate these previously tedious processes of spatial data extraction and creation is valuable in many workflows and can lead to significant time and resource savings. This spatial data can also become valuable input to downstream workflows, for everything from spatial data management to advanced spatial analysis of patterns and relationships.
The other key aspect of GeoAI is the application of ML and DL techniques, including spatially explicit statistical and machine learning techniques, to the analysis of spatial data for applications such as detecting spatial patterns and making predictions and spatiotemporal forecasts. The use of emerging ML and DL tools with spatial data provides new alternatives to explore difficult problem spaces. The use of ML methods on spatial data, as well as the incorporation of spatially explicit models that incorporate some aspect of geography (location, shape, proximity, and more) directly into the algorithm can not only make models more efficient, but also often more accurate and representative of the reality you're trying to model. These techniques can be used to allocate resources based on meaningful spatial patterns, to find trends and anomalies in space and time, and to incorporate spatial relationships into predictions and forecasts.
Learn more about and see an example of machine learning methods applied to spatial data
Ultimately, GeoAI concepts are used in tools that work best when used by thoughtful analysts and data scientists. As with other tools, the analyst must have a thorough understanding of the problem, an inclination to iterate and refine an analysis, and transparency in the process when providing the information product to the decision maker. The use of GeoAI does not change these principles; it makes the need for conscientious analysis more important than ever. ArcGIS Pro provides a way to run GeoAI tools, evaluate their results, and communicate with stakeholders effectively and responsibly.
Learn more
See the following resources for more information:
- Spatial data generation and information extraction
- Introduction to deep learning
- Deep learning in ArcGIS Pro
- ArcGIS pretrained models
- Classification and Pattern Recognition toolset (Image Analyst toolbox)
- Deep Learning toolset (Image Analyst toolbox)
- Point Cloud toolset (3D Analyst toolbox)
- Text Analysis toolset (GeoAI toolbox)
- Interactive object detection basics
- Spatial problem solving
- Clustering and pattern detection
- Mapping Clusters toolset (Spatial Statistics toolbox)
- Space Time Pattern Analysis toolset (Space Time Pattern Mining toolbox)
- Multidimensional Analysis toolset (Image Analyst toolbox)
- Prediction
- Modeling Spatial Relationships toolset (Spatial Statistics toolbox)
- Feature and Tabular Analysis toolset (GeoAI toolbox)
- Multidimensional Analysis toolset (Image Analyst toolbox)
- Forecasting
- Time Series Forecasting toolset (Space Time Pattern Mining toolbox)
- Time Series AI toolset (GeoAI toolbox)
- Multidimensional Analysis toolset (Image Analyst toolbox)
- Clustering and pattern detection