Artificial intelligence (AI) has grown exponentially in recent years. It’s able to match and, in some cases, exceed human accuracy at such tasks as reading comprehension, image recognition, and text translation. However, an area that is seeing massive opportunities that weren’t possible previously is GIS (geographic information systems). GIS is a computer system that displays and analyzes geographically referenced data.

In broad terms, AI is the capacity for computers to perform tasks that usually require some degree of human intelligence. Machine learning is an approach that can perform this method. It utilizes algorithms to acquire information from the data to provide the necessary answers. For instance, machine learning can help with automated territory map generation.

Although machine learning has been a crucial part of GIS software in Clustering, Classification, and Geographically Weighted Regression, spatial analysis can now go further by using deep learning tools. Let’s look at some cases of deep learning’s application in geographically referenced information.

Deep Learning’s Application in Geographically Referenced Information

• Image Classification

Deep Learning can be used to determine whether a photo is type A or B so as to categorize geotagged photos.

• Instance Segmentation

Instance segmentation is a more exact Object Detection method from which the precise shape of an object in an image can be derived. Using this method, GIS can be combined with LiDAR data to recreate buildings in 3D.

• Semantic Segmentation

This process classifies each image pixel, so it belongs to a specific class. In GIS, this approach can be used for Land Cover Classification.

• Object Detection

Object detection is a computational approach that finds objects within an image by coding and locating them. In GIS, combining this process with aerial photography, satellite imaging, or drone photography makes it possible to map objects of interest.

Machine Learning and Location Intelligence for Real-World Applications of GIS 

Using location intelligence, GIS technology, and Machine Learning automation, industries are becoming more innovative and gaining real-time insight. By combining these methods, businesses are gaining the ability to map, analyze, and share data in the context of location. For instance, they can spot trends and make predictions to support market assessments, site selection, asset tracking, risk management, and various other central business needs. Simply put, machine learning manages complex data, and location intelligence provides the data with crucial location context. 

Let’s look at some real-world examples of how these tools are being applied in various industries.

• Retail Industry

In the retail industry, machine learning and location intelligence have many applications. Retailers can use these tools for site selection, optimizing their supply chain, and location-based advertising. These tools can also help with customer support, providing personalized customer experiences, and setting prices.

• Government Agencies

Government agencies apply machine learning algorithms on georeferenced satellite and drone imagery. This allows them to automate model growth scenarios and fieldwork, predict crop yields and assess the health of crops in real-time.

• Logistics

Drivers, route planners, and operations managers can use AI to track assets in real-time, anticipate future supply needs, accurately predict arrival times, and fill in the gaps in road network databases.

• Finance

Machine learning helps banks and financial analysts detect fraud, perform predictive risk assessments, and plan either one branch location or a network of multiple locations.about:blank

• Manufacturing

When it comes to the manufacturing industry, manufacturers can use AI systems to automate inspections and quality control, optimize supply chain logistics, plan predictive maintenance, and flag any unusual activities that can slow production.

Conclusion

Artificial Intelligence is changing the GIS landscape by using deep machine learning to improve the analysis of geographically referenced information. AI, specifically machine learning and location intelligence, is also being used to help various industries analyze and improve their processes.

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Big data in GIS has critical ramifications for how we procure and leverage spatial data

In the midst of the surge of data we gather and fight with consistently, geospatial information possesses an interesting spot. Because of the networks of GPS satellites and cell towers and the rising Internet of Things, we’re able to track and correlate the location of people and items in exact manners that were impractical up to this point. Yet, putting this geospatial information to use is more difficult than one might expect.

It is frequently said that 80% of data has a spatial part. Once in a while it is a coordinate gathered from a GPS application, or essentially an address that gets geocoded to a location along a street centerline. Regardless, it is surprisingly simple to get the location of an item. With moving items, location and time are imperative to follow the article alongside some other applicable attributes (temperature, point, size, shading, and so forth). As sensors and devices become increasingly connected, data is being gathered at an uncommon rate.

The Big data pattern has drastically affected each industry, so it is little amazement that big data in GIS has critical ramifications for how we procure and leverage spatial data. Big data is definitely not a new pattern. Notwithstanding, it is turning into a bigger part of geographic data science.

Maybe perhaps the greatest change in the discussion around big data has been in the relationship between software, hardware, and expertise. One of the foremost utilizations of geospatial big data analytics has been in the humanitarian area. GIS IoT gadgets are currently being utilized across the world to gather information in conditions which were previously hard for aid workers to access and thus hard to work in.

For an illustration of the manner by which geospatial big data analytics can function admirably in this area, consider by DigitalGlobe, a non-profit organization that sources satellite information and coordinates it with different sources like social  media notion and aerial imagery, use a GIS machine learning algorithm to follow activity in explicit areas and identify anomalies.

Geospatial information is not simply an area, nonetheless. Geospatial information likewise tracks how things are connected and where they are in relation to other objects. Realizing how an object changes over the long run corresponding to different items can give critical insights. For instance, how truck maintenance recommendations change depending on where a truck is found and how it is driven in the field? Utilizing all of your data to drive more intelligent maintenance plans sets aside cash, time and assets.

Robots, or unmanned aerial vehicles (UAVs) as the business calls them, have been everywhere on the news of late. What’s more, as you may expect, there’s a big data angle to them, particularly with regards to location intelligence and geographic information systems (GIS) products.

UAVs are emerging as an astounding method to accumulate data from the air. As per the Flightline Geographics auxiliary of ESRI partner Waypoint Mapping, UAVs can capture pictures with goals down to one inch, and convey that data in no time, compared to the days regularly needed by manned aircraft.

A couple of years ago, it was hard to envision how the financial sector and geospatial information would cooperate – there seemed, by all accounts, to be little value to a bank or other financial services company in knowing where their customers traveled and when.

Incidentally, this data is just as valuable to the financial sector as it is in other sectors. Truth be told, geospatial big data in the financial area presently plays a role in the progressing startup boom that plans to bring geospatial analysis procedures to the core of business decisions.

The applications are as yet being explored, however, as of now appear to be encouraging. Geospatial information has already been valuable, for example, in figuring out which branches to merge, as well as how satellite imagery over time can all the more likely foresee a property’s risk of flooding when it comes time to decide insurance rates.

Financial services firms are driving with regards to utilizing GIS and business intelligence tools together. For the financial industry, geospatial big data is playing a part in making a blast of a boom of startup companies. So many financial startups here have been advertising themselves for their capacity to use non-traditional data sources, for example, satellite imagery, for deciding the possible danger of offering insurance or a loan. For instance, satellite imagery throughout a range of time could more readily anticipate a property’s risk of flooding for determining insurance rates.

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