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What are Geospatial AI Services?

To better understand what geospatial services are all about, it’s important to understand what we mean by “geospatial,” and then how artificial intelligence (AI) is being applied to it. Geospatial refers to data that has a geographical element to it – it describes specific features of the earth relating to space or size. 

Geospatial AI combines the power of AI with geographic data to extract meaningful insights from what we see on earth. Since the geospatial data that is collected from satellite imagery, drones and other forms of technology can be immense, machine learning, deep learning and computer vision can be applied to automate the analysis of this data – finding patterns in it, measuring changing conditions and turning it into insights.

Geospatial AI services, therefore, help organizations leverage AI to automate the process of analyzing geospatial data. There are service providers that are experts in AI and can work with you to solidify what you are hoping to learn from geospatial data and articulate what the problem is that you are hoping to solve. It does this by getting the data in the best form possible to train an AI algorithm and then they build the solution that best meets your needs.

What Industries Use Geospatial AI Services?

While previously focused on government sectors, today many industries are finding opportunities to leverage AI to support the insights gained from geospatial data. Virtually any industry that can benefit from understanding trends, changing conditions or patterns from objects on earth could be supported in their efforts with AI.  Generally speaking, geospatial AI services can be divided into four core areas:

  • Geospatial Data Analysis. To automate the extraction of insights from geospatial data, such as satellite imagery, remote sensors and aerial photographs. They can identify patterns, trends, and anomalies in areas like land use, traffic flow, and environmental changes.
  • Predictive Modeling. To forecast future events based on historical data. They can be used to predict changing weather patterns, crop yields or the migration patterns of whales.
  • Location Intelligence. To analyze geospatial data to understand customer behavior, demographics and market trends based on location. Businesses can use this information to determine optimum store locations, traffic flow patterns or areas of insurance risk.
  • Route Optimization. To leverage AI to find the most efficient routes for supply chains, emergency response vehicles or subway systems.

Consider some of the specific industries where geospatial AI services are being used.

Civil services:  Public organizations such as the Federal Emergency Management Agency (FEMA) leverage geospatial AI to monitor the impact of regions affected by natural disasters, such as floods, fire or hurricanes. By automating the analysis of swaths of land or sea, it also can help predict the likelihood of disasters occurring to proactively address them. 

Agriculture. Farming industries need to understand soil or crop conditions, livestock grazing patterns or other information.  Geospatial data gathered from satellite imagery can help them improve crop yields or the health of livestock, as well as allocate resources in different ways by understanding changing patterns.

Retail. Brick & mortar retailers can use geospatial AI services to identify urban communities with high foot traffic, for example, to ascertain prime locations for new stores.  They’re also able to use these services to plan merchandise targeted to the specific demographics and needs of the area. 

Environmental services. Geospatial AI can be used to monitor deforestation trends, water quality and air pollution levels; map changes to shorelines or wetland degradation; or even track the size of glaciers. 

Mapping. Most people are well aware of navigation apps, such as Waze or Google Maps, which leverage geospatial data, as well as AI to monitor changing traffic conditions and the best routes for drivers, as well as step-by-step directions to specific locations.

 

  1. What are some specific use cases of Geospatial AI services?

Geospatial AI is not a futuristic technology but something that is very much in use today. Below are some interesting use cases.

  • Public dataset enables greater geospatial analysis. Maxar Intelligence created the largest satellite imagery dataset on the planet using geospatial AI services. Wovenware (A Maxar Intelligence company) played a key role in annotating and labelling satellite imagery that would become a core part of the Maxar Deep Workbench platform of easy -to-use annotation tools. The team also built computer vision models to automatically detect and classify objects based on the xView dataset, one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world.

 

  • Global Forest Watch tracks changing forest conditions. Thousands of people around the world use geospatial AI services from Global Forest Watch to monitor and manage forests, stop illegal deforestation, identify activities that are not environmentally friendly or conduct research to improve conservation efforts. Through its free, AI-driven platform, anyone can access near real-time information about where and how forests are changing around the world.

 

  • Tracking airport activities around the world. In collaboration with Maxar Intelligence, Wovenware helped to enrich the capabilities of the Maxar Geospatial Platform (MGP Pro) formerly SecureWatch, a cloud-based subscription service providing access to earth intelligence. Together, they implemented advanced computer vision models for automated aircraft detection in airports around the world. This application showcases the transformative impact of computer vision in geospatial analysis, which ultimately leads to safer and more efficient airports, air traffic and improved monitoring of the skies.

 

  • Using Geospatial AI to monitor climate change. NASA teamed up with IBM Research to create an AI geospatial foundation model, trained on vast amounts of NASA’s data to help users tackle environmental challenges. The model is used to detect burn scars, to delineate flood water, and to classify crop and other land use categories, among other things.

How Does Geospatial AI Work?

AI enables better and faster analytics of data collected in geospatial data by generating new data from existing datasets, such as satellite images. Geospatial AI is the integration of AI with  geospatial data to develop deeper insights into what is happening on certain areas of the earth. AI algorithms can handle large and complex geospatial datasets more efficiently than traditional methods. AI can learn patterns and relationships in geospatial data, enabling image classification, object detection and predictive modelling.

Geospatial AI converts geospatial data into numbers and mathematical calculations that reveal a certain pattern in the data, helping AI identify buildings, forests, air traffic and other things, and subsequently, issues like climate change, hurricanes or urban heat islands.

 

What are Best Practices for getting started with AI geospatial AI services?

Below are considerations organizations and entities need to address before implementing a technology initiative as intense and complex as geospatial AI.

  • Clearly articulate your end-goal. Each project should begin with an understanding of what you hope to accomplish with AI. Whether it’s to manage overpopulation in crowded cities or determine the best location for a new mall, the initiative need to involve all stakeholders and clearly identify the problem that needs to be solved.
  • Gather the data. As with any form of AI, geospatial AI relies on large amounts of quality data in order to be successful. The challenge with geospatial AI is that this data is typically in the form of images from satellites, drones and sensors, and not as readily available as text-based data. As a first step organizations should determine where they can harness their geospatial data — whether from sites such as Maxar’s Open Data program or other U.S. sites.
  • Work with a trusted partner. With a clear understanding of the problem you are working to solve and the data needed to solve it, an AI service provider can help to deliver the AI solution that will help you derive the valuable insights needed. Computer visions solutions, leveraging deep learning can analyze images automatically and come up with the conclusions and insights that make sense of massive amounts of data. Geospatial AI service providers have the data science expertise and infrastructure needed to build and maintain complex AI solutions.
  • Ensure secure and ethical AI. Since data is the lifeblood to effective AI, it’s important to make sure data collected doesn’t invade privacy and that the AI solutions that are built upon it are explainable and built upon diverse datasets.
  • Keep humans in the loop. While geospatial AI can deliver rapid insights, sorting through massive amounts of data, it always will play a supporting role to human intellect. Humans must always be the ones to make final decisions based on what the data is telling them.

What are the benefits of using Geospatial AI Services?

Geospatial AI services represent the next generation in data insights, providing a deep-dive into the images and objects across the world for the benefit of business, government and mankind.  It enables better and more objective decision making across a growing number of commercial and public sectors. It also enables increased efficiency, by automating tasks and processes that previously were handled manually by humans, leading to significant time, resource and cost savings. And finally, geospatial AI services play an enormous role in mitigating risk, because of the ability to predict and prepare for natural disasters and criminal activities, and to enable more data-driven approaches to business decisions.  Geospatial AI services are providing deeper insights from what is all around us – on the earth and in the sea, in our cities and civilizations.

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