What is GeoAI?

GeoAI refers to the integration of geospatial technology with artificial intelligence (AI), a discipline rapidly growing in popularity as a method of enhancing the efficiency of analyzing geographic information. 

Scaling GIS with AI

Since the 1960s, geographic information systems (GIS) have enabled professionals across industries to digitally visualize, analyze, create, and edit mapping data for a wide range of applications. While traditional GIS solutions unlock insights about our world that are otherwise inaccessible, they can often be time- and resource-intensive. 

Some of the most time-consuming and labor-intensive aspects of GIS are the creation and maintenance of mapping data. Traditional data creation methods require geospatial professionals to manually identify, trace, and classify individual geographic features from satellite or aerial imagery to ultimately produce a digital representation of the physical world. These datasets must constantly be reevaluated with new imagery to ensure currency with the real world, creating an endless cycle of resources being devoted to manual digitization instead of actual analysis.

Similarly, traditional geospatial analytics tools, while incredibly powerful, are often complex to execute. Many GIS solutions still require some knowledge of coding to perform common workflows such as hot spot analysis, object classification, or data modeling. As the need for the outputs of these operations grows, many geospatial professionals are seeking ways to reduce efforts spent on the inputs.

Fortunately, recent advancements in AI, machine learning, and computer vision are transforming the geospatial industry and making it easier to scale the creation, maintenance, and analysis of geographic data so professionals can focus on problem-solving.

Types of AI-based mapping

GeoAI is quickly revolutionizing the geospatial industry by providing leverage to GIS professionals as they create, maintain, and analyze data. The following are some of the most popular applications of AI in GIS:

AI object detection

GIS professionals are often tasked with detecting objects in geospatial imagery collected from satellites, planes, drones, or street-view cars. GeoAI can analyze vast amounts of imagery at once to locate relevant features without needing to manually review imagery and make note of individual objects.

AI vector digitization

Although geospatial imagery has a spatial reference, individual objects must be digitized into vector features to be used in most GIS analytics applications. AI-powered mapping engines have eliminated the need to manually trace individual features by automating the digitization of imagery into geometrically accurate vector layers.

AI feature classification

Another crucial component of geospatial data creation is feature classification, or the process of describing individual objects to differentiate between distinct data layers. GeoAI can efficiently classify thousands of features into like-feature classes so GIS professionals don’t need to manually review and tag each individual object.

AI change detection

As a digital representation of the physical world, geospatial data must constantly be updated to reflect dynamically changing landscapes, infrastructure, and populations. Multiple vintages of geospatial imagery can be analyzed by AI to detect change, producing updated data attributed with information about how an object has changed over the given time period.

AI modeling and analytics

Beyond data creation and maintenance, GeoAI has also enhanced the efficiency of analyzing geospatial information. AI-powered tools can now streamline common geospatial workflows such as network analysis, hot spot creation, isochrone development, and predictive modeling. 

Top 5 advantages of GeoAI

While GeoAI will never replace geospatial professionals, it does provide valuable leverage so individuals and teams can spend less time on manual tasks and more on critical problem-solving and other strategic initiatives. This leverage provides five main benefits to GIS professionals:

1. Efficiency

The speed with which high-quality GeoAI can perform essential GIS functions allows geospatial professionals to accomplish more in less time without compromising on the integrity of their work output. For example, prior to using AI-based mapping, it took the Collier County, Florida Sheriff’s Office GIS team four years to extract all driveways and access roads. With AI-powered mapping, all 132,000 features were digitized and classified in just under a month.

Learn more about the Collier County Sheriff’s Office's efficiency gains with GeoAI here.

2. Scale

GeoAI’s ability to produce high-precision mapping data efficiently also means more data can be created or analyzed at a time. As an example, it took civil engineers at Fehr & Peers six months to manually digitize 750 miles of sidewalk for the San Bernardino County Transportation Authority; AI-based mapping techniques were able to digitize all 17,000 miles of the County’s sidewalks - plus 16 other transportation features across the County’s 20,000 square mile area - in only three months.

See how GeoAI enables scalable mapping in America’s largest county.

3. Cost-savings

When considering the time and resources spent on manual mapping efforts, GIS operations can quickly become expensive. High-quality AI-powered mapping solutions help teams accomplish more with less by allocating headcount and budget for strategic projects and analytics. In fact, the City of Jacksonville found GeoAI to yield a 84% cost-savings per parcel of land cover data produced.

Learn how the City of Jacksonville saves resources with AI-powered data.

4. Data standardization

Because manual digitization is so labor-intensive, it usually involves multiple geospatial professionals. Differences in how individuals interpret imagery and digitize features can result in inconsistent datasets, which can have an adverse impact on analytics. GeoAI eliminates these discrepancies by employing the same methodology across an entire area of interest. As a real-world example, Airbus’ Facility Management and Real Estate division streamlines analytics by leveraging AI-powered mapping to generate standardized maps of 72 diverse sites around the world.

Read about Airbus’ AI-generated standardized global maps.

5. Update frequency

Resource-intensive traditional geospatial data creation and maintenance techniques often hinder timely data updates, especially in our dynamically changing world. Despite rapidly evolving landscapes, changing infrastructure, and moving populations, many GIS professionals are forced to work with stale data due to the time and cost investment required to update databases. GeoAI’s resource savings, scaling abilities, and standardized data outputs streamline data updating, making it possible to keep databases current with real-world conditions. For instance, AI-based mapping enables the Detroit Water & Sewerage Department to update their city-wide impervious surface database annually, when prior mapping methodologies only allowed for updates every two years at best.

See how the City of Detroit is able to update maps annually with AI.

GeoAI application examples

Geospatial data can be used for a wide variety of analytics use cases, and advancements in GeoAI are being increasingly leveraged to enhance GIS workflows across industries. Among the most common applications for AI-based mapping are:

Land use change analysis

AI feature extraction produces high-precision land cover maps more efficiently and cost-effectively than manual digitization, while AI change detection rapidly analyzes multiple vintages of data to identify where land use change has occurred. These insights are integral to stormwater management, urban planning, tax assessment, and more.

Risk assessment

Geospatial data produced by AI vector digitization, such as building footprints or trees, provides a basis for understanding hazard risks. Similarly, AI object detection helps identify potential hazards, like damaged roofs. AI modeling tools can ingest this data to quantify risk profiles and inform mitigation strategies.

Transportation planning

AI feature extraction and classification produce highly detailed transportation mapping data at scale to power traffic safety engineering, pedestrian access analytics, and other critical planning workflows.

How to identify a quality GeoAI solution

GeoAI is a powerful tool that can provide helpful leverage to geospatial professionals, enabling them to work more efficiently and devote more time to tasks that cannot be outsourced to AI. However, for AI-based mapping solutions to be useful, they must not sacrifice quality.

Unfortunately, some GeoAI solutions produce results data that cannot be relied on for analytics. In these cases, the leverage provided by increased efficiency, scale, and cost-savings are not worth the compromise. For the vast majority of geospatial analysis use cases, quality is always more important than time or cost.

When evaluating a GeoAI solution, be sure to consider:

• Completeness: Does the data produced cover the entire area expected? Are all features included, or are some missing?
  
  
• Accuracy: Is the data a precise representation of real-world objects? Are features classified correctly?
  
  
• Consistency: Was all of the data produced using the same methodology, or are there discrepancies between how like-features were interpreted?

As the saying goes, bad data in means bad data out, so it’s important to make sure geospatial analytics are fueled by quality AI solutions if you’re looking for leverage.

Building a digital twin of the world with GeoAI

At Ecopia AI (Ecopia), we’ve been laser-focused on GeoAI since 2013. Our entire company was founded to make our AI-based mapping systems accessible to geospatial professionals looking to scale their operations without sacrificing quality. 

Ingesting the latest imagery from our global partner network, Ecopia extracts vector features with a >95% geometric accuracy guarantee, all in just a fraction of the time it takes to manually digitize the same area of interest. Our AI-powered systems can also detect change and feature conditions to help keep geospatial databases up-to-date and appended with the real-world detail needed for analytics in government, insurance, civil engineering, telecom, and more.

A lot of companies claim to be experts in AI, but we’ve been in this space for over a decade, constantly refining our AI-powered mapping systems to meet the evolving needs of today’s geospatial professionals. Get in touch to learn more about our GeoAI capabilities and how they can provide your organization with the leverage to accomplish more with fewer resources.