Geospatial Artificial Intelligence for Fun and Profit
Avatar photo Nash Prado

Geospatial Artificial Intelligence for Fun and Profit

Artificial Intelligence | Infrastructure | Oil

Geospatial Artificial Intelligence is already being used to automate tasks and improve understanding. This article will explain exactly how the most technically capable companies are leveraging geospatial AI.

 

The reality is that once artificial intelligence products become mainstream, they get assimilated as an everyday technology, and the definition of what is artificial intelligence, changes. Most people don’t realize that they’re already surrounded by artificial intelligence.

The personal assistant built into your smartphone, can read emails, turn on appliances, or find the nearest coffee place near you. AI has become a seamless addition to your life and is used even more in business. Amazon uses a recommendation engine to increase order volume where Netflix tailors content recommendations to increase content consumption. Good companies know AI exists. Great companies actively utilize AI in their business practices. But you don’t need to have a billion-dollar development team to utilize AI. We’ve compiled a list of ways you can leverage AI & geospatial intelligence in your business to make better decisions using deeper insights.

 

What is Artificial Intelligence (AI)

AI refers to machine intelligence that is programmed to think like humans or mimic their actions. These are not just robots or virtual assistants but a machine that can be programmed to have the ability to learn and solve problems. AI is a powerful tool that is already used in the geospatial community. Here’s how practitioners already leverage AI in their workflows.

 

Geospatial and artificial intelligence

When Artificial intelligence is applied to geospatial datasets like satellite imagery or drone imagery, the power of AI can assist in decision making and provide new insights.

Capital-intensive industries like telecommunications and electricity distribution need their executive teams to have accurate information to make strategic decisions. Stakeholders need to be informed when deciding where and when to plan infrastructure upgrades and this can be a long, tedious, and costly process. Expanding coverage means surveying land, installing cables, and mapping out coverage areas. This takes time, money, and talent, resources that are all in high demand.

This is an opportunity where AI can reduce costs & systematize output to create a consistent output. With improvements in the quality & frequency of remote sensing data, matched with cost reductions from increased satellite capacity built over the past several years, we can now gather sufficient data to run advanced analytics with accuracy, precision, and scale without the need to visit each shortlisted site.

 

Artificial intelligence and its practical applications to geospatial products

Remotely sensed data like satellite imagery and drone imagery have become groundbreaking data sources for environment conservation, humanitarian effort, and poverty reduction projects. Computers can now extract value-added information from remotely sensed data using a technique called computer vision. Computer vision algorithms recognize objects and patterns in images in a fraction of the amount of time that it would take a team of humans. This is particularly useful with big data, where it is unfeasible to have humans review each data point. Today, change detection algorithms are routinely used to quantify deforestation, pinpoint humanitarian needs, and discover population migration patterns.

 

Computer vision in a nutshell and its practical applications in remotely sensed products

There are four most common computer vision tasks which aim to solve or identify object outlines at the pixel level of remotely sensed data.

 

remote sensing machine learning

Four common computer vision tasks.

 

Image Classification tells that there is a balloon in the image. For humans, this is a very simple task. For computers, it cannot answer this question unless you will train it to distinguish what object it can see. With the use of computer vision algorithms, machines can recognize and classify images.  Here’s a deeper explanation of how to leverage artificial intelligence image classification using ArcGIS.

Applying the same idea to remotely sensed data will have the ability to assign all kinds of labels for satellite or drone imagery based on user needs. These categories could be specific kinds of trees, land use classification, mapping, and settlement detection.

 

Object detection tells that there are 7 balloons in the image at this location. With the use of object detection, we can determine where a certain object is located.

Now let us apply this technique to practical use. Let’s say a company that supplies raw materials extracted from palm trees. With available remotely sensed data, object detection can be used to locate & count specific trees saving companies the need to physically survey their field. With enough data, algorithms can even distinguish between healthy vs. diseased crops or quantify harvests. Commodities traders benefit from a more accurate estimate of regional harvests.

 

AI models can also be used to train or to look for other distinguishable objects, such as cars, houses, oil tanks, farm fields, or swimming pools.

These algorithms can be useful to governments for forecasting urbanization rates, taxation forecasts, and policy research.

 

Semantic Segmentation tells you tells us what each pixel in an image represents. For the balloon image earlier in this article, it could tell us whether a pixel is part of the balloon or not. The following image leverages semantic segmentation to classify each pixel as belonging to a road, vegetation, built-up or bare soil.

 

Instance Segmentation tells that there are 7 balloons in the earlier balloon image and the pixels that belong to each one of them. With this algorithm, it can combine the benefits of both object detection and semantic segmentation so that it not only identifies the location of the object you wanted to detect but also the pixel mask of each particular object.

One of the practical applications of instance segmentation applies in the industry of real estate, construction, and architecture where building footprints of structures can be extracted, analyzed, and improved.

 

Conclusion

With these basic concepts of computer vision and practical applications of artificial intelligence in geospatial products, companies can customize cutting-edge solutions for strategic and data-driven investment decisions. Artificial Intelligence tools revolutionize cross-industry customers’ collection, use, study, and analysis data regardless of the customers you serve. Geospatial artificial intelligence will help decision-making be data-driven, reliable, and easier. If you want to see how your business can benefit from geospatial artificial intelligence, please reach out to us!