What Is Geospatial Artificial Intelligence?

Geospatial artificial intelligence (GeoAI) is the process of combining geospatial data like location, coordinates, satellite imagery, and demographics with intelligent technologies like deep learning, machine learning, and AI. Location information adds more context to business intelligence and analytics, revealing how insights interact with real-world factors. Geospatial artificial intelligence takes this one step further, using AI to improve the quality of location information, predict future geographic patterns, and discover hidden trends in geospatial data.

Geospatial AI offers many benefits that allow organizations to make better use of their resources.

Improved decision-making

Geospatial AI offers comprehensive insights derived from geospatial data. Organizations can process information in real-time to respond better in situations where time is of the essence, like in a disaster event. Instant data analysis is a critical part of effective resource allocation, equipment maintenance, and response protocol. In numerous fields, this form of spatial science guides best practices, creates opportunities for better resource planning, and helps with management.

Optimized land usage

Geospatial analysis allows organizations to effectively analyze land and determine the best possible usage and distribution. Organizations can optimize land usage in use cases like urban planning, disaster management and planning, and traffic control. The use of AI technologies helps to streamline this process and increase its efficiency.

Environmental monitoring

Geospatial AI is a vital technology when monitoring our environment. It supports use cases like tracing vegetation over time, monitoring the real-world impacts of climate change, and optimizing farming processes. Geospatial information helps to improve conservation efforts and points toward sustainable methods of managing natural resources.

Cost-saving infrastructure management

By identifying anomalies or failures in infrastructural systems before they become critical, geospatial AI contributes to cost-efficient infrastructure management. Monitoring infrastructure over time helps to extend the lifespan of urban and industrial infrastructure and minimize downtime.

There are various use cases of geospatial AI across many different industries. Below is a selection of examples.

Monitor deforestation

Geospatial data can provide insight into how land changes over time, pinpoint potential wildfire risk areas, and outline carbon credit projects. Over recent years, geospatial AI has been a core tool to monitor and identify deforestation trends.

For example, the Government of Pará, Brazil, partnered with Amazon Web Services to develop SeloVerde. By combining services and software in the AWS Cloud, SeloVerde utilizes geospatial data sources to monitor more than 20 million hectares of forest areas. With automatic updates each day, the high degree of monitoring helps to strongly discourage deforestation and trace permanent preservation areas.

Assess road infrastructure

Satellite imagery helps trace existing road infrastructure and plot new potential roads in line with flood risks and other geographical inhibitors. AI and geospatial data help local governments rehabilitate road infrastructures and increase connections to underserved communities.

An example of this in action comes from efforts by the World Bank. Using data analytics and geospatial imagery powered by AWS, the World Bank has constructed over 260,000 kilometers of roads since 2002. By collecting data and insights about potential road networks in remote locations and mapping new routes with ML models, the project was able to map 500,000 kilometers of roads in three weeks—a feat that would typically take 15 years of traditional work.

Manage drought

Global warming has caused global temperatures to increase and sea levels to rise. A changing climate can impact everything from biodiversity to ecosystems. Geospatial AI can collect data on a massive scale, visualizing changes and monitoring how climate change is affecting areas of land.

One impact of climate change has been a drought in Lake Mead. Although this reservoir supplies water to 25 million people in the United States, its water levels are at their lowest level since 1937. Using Amazon SageMaker, applied scientists have been able to monitor the shrinking shoreline and address real-world problems.

Several technologies work together to provide functionality and utility in geospatial AI.

GIS

Geographic information systems (GIS) are used to record, analyze, and visualize spatial data. GIS data underpins the creation of maps and allows analysts to understand patterns in geospatial information. In geospatial AI, GIS helps to collate data from several sources and derive insights from these datasets.

IoT

Internet of Things (IoT) is an umbrella term that refers to any electronic device that connects to the internet. IoT devices provide real-time location-based data and updates, such as air quality, traffic patterns, and weather conditions. In a geographic context, IoT devices are vital for monitoring environmental factors, logistics, and other real-world use cases.

Computer vision

Computer vision is the process that allows computers to identify people, places, and objects in images at much greater speeds than are possible with human analysis. Computer vision analyzes geospatial imagery, like aerial photographs or satellite images. It can help to detect change, identify key features, and monitor land over time.

Cloud computing

Cloud computing helps deliver limitless and scalable storage and compute services to applications and platforms. Cloud computing provides the on-demand resources that geospatial AI needs to process and analyze large data volumes. It can also be coupled with data mining to scale the amount of information an organization has to draw on.

Machine learning

ML is the process of using deep learning algorithms to process data and learn from it, guiding it toward making more accurate predictions without explicit planning. ML is useful in geospatial applications as it helps with image classification, predictive modeling, and identifying trends from spatial big data.

Amazon Location Service makes it easy to integrate geospatial data from multiple providers into your applications. Developers can quickly add location functionality—like maps, tracking, routing, and more—to applications without sacrificing user privacy. With Amazon Location Service, you can:

• Access high-quality geospatial data from leading data providers.

• Deploy and accelerate application development by integrating AWS services.

• Protect user privacy and shield information.

Amazon Lookout for Equipment helps avoid unplanned downtime in industrial equipment by automatically detecting abnormal behavior. It allows you to streamline issue resolution with immediate notifications and automatic actions when detecting an anomaly. With Amazon Lookout for Equipment, you can:

• Use data from existing sensors to create specific ML models for your equipment.

• Improve model performance with anomaly trends and feedback.

• Pinpoint anomalous sensors and streamline malfunction responses.

AWS Panorama helps organizations to improve operations with computer vision at the edge. Support CV functionality in everything—from improving supply chain logistics with advanced object recognition to optimizing traffic lane flow. With AWS Panorama, you can:

• Add computer vision to any existing camera in your fleet, seamlessly integrating CV tech into your local network.

• Generate high-accuracy and low-latency predictions from a centralized management interface.

• Process video feeds at the edge and control where you store your data.

Geospatial ML with Amazon SageMaker allows ML engineers to build, train, and deploy ML models using geospatial data. It offers full access to geospatial data sources, pre-trained visualization tools, and pre-trained ML tools to expedite geospatial ML. With Geospatial ML with Amazon SageMaker, you can:

• Access location data, maps, satellite imagery, and other geospatial information to make smarter decisions.

• Enrich large-scale geospatial datasets with open-source libraries.

• Accelerate model building using pre-trained deep neural network lodes that include cloud masking, land cover segmentation, and more.

Get started with geospatial artificial intelligence on AWS by creating a free account today.