Artificial intelligence is not on its way, because it is already here and a seamless part of our everyday lives

Artificial Intelligence (AI) is a technology that most outside observers believe to be a fairy tale that is years from maturity. But the reality is that once artificial intelligence products become mainstream, they get assimilated as everyday technology and the definition of what is artificial intelligence, changes. Most people don’t realize that they’re already surrounded by artificial intelligence.

From the personal assistant built into your phone, can read emails, turn on appliances, or find the nearest coffee place near you, you’re using AI every day. AI has already many practical applications. AI isn’t just used for gimmicks, either. 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.

How about we combine AI with remote sensing technology?

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 aerial 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 which falls under the field of Artificial Intelligence. Computer Vision gives your computer the ability to recognize objects in images and recognize patterns in a fraction of the amount of time that it would take a team of humans. This is particularly useful when there is a large quantity of image data captured but not enough human eyes to scan the data in detail. 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 a remotely sensed data.

Four common computer vision tasks.

Image Classification tells that there is a balloon in the image. For humans this is a very simple task but 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 are able to recognize and classify images

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.

(a)Palm trees _________________(b) Residential Buildings __________________(c) Fish ponds

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.

Detecting and counting Palm trees on a remotely sensed data using object detection

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

Not just companies who want data driven rollout strategies, these algorithms can be useful as well for urbanization rate, taxation forecast and government policy research.

Semantic Segmentation tells you that these are all the balloon pixels. With semantic segmentation you can know which pixels fall under a certain label. And by feeding algorithm images to your computer, you can also determine whether or not each pixel of your image belongs to a road, vegetation, built-up or bare soil.

Semantic segmentation to determine which pixels of the imagery are bare-soil, residential, road or trees.

Instance Segmentation tells that there are 7 balloons in the image at these locations 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.

Instance segmentation applied on satellite imagery to determine the building footprints on a residential area.

Effective planning and making better decisions for business

With these basic concepts of Computer Vision and practical applications of Artificial Intelligence in geospatial products, companies and businesses can customize leading-edge solutions for strategic and data-driven investment decisions. With tons of challenges depending on the industry you belong to, manual data collection and analysis will require resources to complete. Using Artificial Intelligence, these tools can revolutionize cross-industry customers’ collection, use, study and analysis data regardless of the customers you serve. Geospatial Artificial intelligence will help decision-making data-driven, reliable, and easier.