GeoAI: Effectively Combine Geospatial and Artificial Intelligence
GeoAI (geospatial artificial intelligence) is a fast-growing field that fuses geospatial data analysis and artificial intelligence technologies. Companies recognize the potential of GeoAI to solve real-world challenges, from the automation of data classification to image analysis and route optimization. Yet, to be successful, GeoAI projects require a thorough data strategy and an understanding of advanced AI concepts. Focusing on data quality and integrity helps steer your GeoAI projects in the right direction.
What Exactly is GeoAI?
GeoAI (geospatial artificial intelligence) is the combination between geographic information systems (GIS) and artificial intelligence (AI). This expanding field leverages advanced spatial analysis with AI techniques such as machine learning (ML) to obtain meaningful insights through predictive analytics. GeoAI has a wide range of applications in various markets. It can perform market potential assessment and forecasting, retail site selection, white space analysis, routing optimization, and prescriptive customer analytics. GeoAI is also essential for flood zone predictions, and climate change impact projections
Another AI breakthrough that became a household name with ChatGPT is generative artificial intelligence (GenAI). In fact, GeoAI can be seen as an extension of GenAI. Indeed, ChatGPT and other technologies have paved the way for further geospatial applications, like interactive mapping, 3D modeling and automation processes.
Larger volumes of data are required to perform GeoAI analytics. This implies big data management and additional processing capabilities. It is therefore critical to choose the most appropriate technologies to operate in this multivariate environment.
Why is GeoAI Important for Businesses?
New technologies and techniques related to artificial intelligence continue to progress exponentially. As a result, companies in various industries such as retail, insurance, real estate and telco must adapt quickly to stay competitive. In this context, Geospatial Artificial Intelligence (GeoAI) is an essential tool as it gives them the agility and resilience they need to face a constantly changing reality. GeoAI helps them reach their objectives by proactively improving decision-making procedures and maximizing their efficiency.
Methods and techniques used in GeoAI projects
There are 4 key approaches to use AI and data analysis in geospatial projects:
- Machine Learning (ML) algorithms can be applied to geospatial data for tasks like land cover classification, object detection, and predictive modeling. Machine Learning techniques can help to enrich geospatial data for predictive and prescriptive modeling. ML uses a wide range of algorithms from classical linear regression to more complex methods. For instance, nonparametric additive models, decision trees and autoregressive/ARIMA time series models.
- Deep Learning (DL) is a subset of ML. It is used to determine complex patterns and structures in geospatial datasets, notably with Neural networks algorithms. In GIS, DL already improves image analysis, in particular for satellite images or object detection.
- Spatial Analysis Algorithms incorporate spatial relationships and patterns. It helps data scientists to discover hidden or unknown patterns in geospatial data. They can use approaches like data clustering/segmentation (k-means, partitioning around medoids (PAM), spatial regression (Ordinary Least Squares (OLS) and Geographically Weighted (GWR) regressions, spatial autocorrelation and interpolation, network analysis and raster analysis for image treatment purposes.
- Natural Language Processing (NLP) enables computers to understand human language and is making geospatial technologies more accessible with conversational GIS interfaces. Geospatial projects also frequently require writing complex code. Generative AI can accelerate the design of those geospatial solutions, particularly when open-source libraries need to be used. NLP can also help to perform advanced feature labeling, automated documentation, and automated data analysis.
The Value of Geospatial Data in AI Projects
The complementarity between AI and geospatial technologies works both ways. Geospatial imagery and data can increase the accuracy of AI/machine learning models. Moreover, it can enhance the level of understanding in complex mechanisms that are occurring in GIS applications.
GeoAI methodologies are involved in processes that include:
- geocoding (translating a physical address into a geographical location)
- geo-enrichment (adding location-based socio-demographic information about georeferenced population, like behavioral, consumption or business data)
- geospatial relationships (identifying zones that intersect, touch or overlap each other).
However, geospatial data in its basic form is rarely suitable for feeding into artificial intelligence models. Yet, with the right expertise it is possible to transfer this geospatial data in a suitable format to feed into existing models and make them more efficient.
Why is Data Quality and Integrity Important for GeoAI?
Artificial intelligence requires data that is both unified and accurate, because it needs a comprehensive and reliable view of what is happening in the real world. This is true for automation, analytics, predictive models, GenAI and GeoAI projects. Accessing this data is a first challenge. Ensuring its quality and integrity throughout the entire data life cycle is even more complex.
Only 4% of organizations have AI-ready data (Gartner). Without the right infrastructure, processes, and tools to ensure data quality, they can’t use AI to its full potential. Today, less than half of employees (48%) completely trust AI’s results. And it’s only 38% for CEOs (Avanade AI Readiness Report 2024). This impacts the short-term ROI of AI projects, but also the long-term profitability and competitiveness of companies. Yet, most companies do not lack data, they just don’t know their data or how to tap its potential. As a result, a staggering 55% of data collected by organizations goes unused (Splunk estimates), including geospatial imagery and data.
How to Improve Data Quality to Feed GeoAI?
The first step to improving the quality of your data is to build a centralized data repository. Data is generated and stored in a variety of databases and applications, from legacy systems to cloud platforms. Consequently, you lack a unified view of your business and can’t feed your AI and ML models. Moreover, those data can be structured or unstructured, and in different formats that you must normalize and standardize, to avoid analysis errors.
To store and access your data in a unique centralized environment, you can use a cloud data warehouse solution like Snowflake. More than 70% of Snowflake’s 8500+ customers already have access to its geospatial storage or processing capabilities. However, they don’t know how to leverage their data. Connections between Snowflake and leading GIS platforms like Carto, Precisely, and Mapbox enable advanced geospatial analytics directly from your data warehouse.
The second step involves data quality rules and tools. Data governance determines who uses data in your company and how. Governance also calls for a specific attention to quality, security, and compliance. Regarding tools, AI is now integrated in proactive data quality tools which can monitor your data pipelines and identify anomalies and outliers. For instance, to spot unusual coordinates for geographic locations or unusual values.
Precisely’s Data Integrity Suite offers an interesting approach with a SaaS platform that minimizes bias, improves accuracy and reliability of data. It includes a shared data catalog, AI with machine learning insights, and automated data integrity processes. You can find more tips for getting your data AI-ready in this eBook by Precisely and Korem.
How Does Korem Help its Clients Leverage GeoAI?
Korem has the most diversified geospatial expert team in North America, including data and analytics specialists and experts in system design and integration. With this expertise, we can help organizations integrate geospatial data in their existing models to improve them and automate complex data transformation processes (ETL). Korem can also create predictive AI models for its clients. Moreover, we secure GeoAI projects with efficient data management processes. We also build IT architectures that scale (cloud-native and hybrid).
For instance, Korem provides Ticon (a Traffic Information Consolidator) with a customized Data as a Service (DaaS) solution to determine which services and goods store managers should offer based on their location. Ticon uses the data to feed its artificial intelligence model to improve traffic analysis for site selection.
Korem also worked with insurance clients to integrate flood risk factors in its qualification model, based on location data. In addition, we support telecommunication companies determine where they should extend their wireless network to optimize investments and coverage.
Korem’s software and data benchmark, combined with an independent agnostic viewpoint, provide a solid foundation for your GeoAI project. Our custom data delivery services provide data format conversion, custom area extraction, geo-enrichment, data cleansing and more.
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