There is a growing need for the collection, processing, management, and efficient use of reliable spatial information. This growing demand is caused due to businesses, governments, and researchers working on making more sophisticated developments to ease human-to-human communication, commutation, and urbanization. The best example for better understanding this growth is the use of spatial data to visualize the current location to predict events and enhance service delivery. As governments and businesses across the globe are working on how to tackle the escalating social, economic, and environmental challenges to offer more scalable solutions to people. Machine learning has been a vital solution -How?
Geospatial information systems perform tasks related to collecting, managing, manipulating, analyzing, and visualizing spatial data as a system. This data set usually consists of satellite images, which is the observatory information about latitudes, longitudes, areas, perimeters, centroids, lengths, etc. Unlike any tabular data, spatial data is hard to compute due to the availability of spatial attributes for each observation. The velocity, variety, and types of spatial data for analysis or geometric manipulation to perform techniques like the clip, erase, buffer, union, interpolation, can be optimized using machine learning techniques.
Machine Learning, a part of AI, allows computers to perform tasks that typically require human monitoring, intervention, or intelligence to make decisions. Machine Learning helps computers to read data-driven algorithms to learn from data and deliver the answers that one required for identifying a solution or sometimes delivering a solution directly to a complex challenge. Applying machine learning for spatial data analysis is opening up more massive opportunities than ever before. Machine Learning models that match or even surpass human accuracy at performing tasks such as image recognition, image identification, semantic segmentation, reading comprehension, translating text to speech, etc., can evolve geographic information systems to study, analyze and understand spatial data.
How Machine Learning can work magic for Spatial Data Analysis?
Using machine learning tasks such as regression, classification, and clustering for spatial data can ease the time consumed with performing critical spatial data analysis techniques like interpolating and Union. Especially, applying Machine learning algorithms like linear regression, tree-based regression, or support vector machine regression to predict target variables to check the relevancy of having more similar values can help in performing better analysis.
Similarly, the machine learning classification algorithms like maximum likelihood, support vector machine, and decision tree can be leveraged for spatial classification. Particularly for classifying the land cover class from satellite images. Another machine learning advantage for spatial data analysis is clustering. The clustering technique applies the same meaning as conventional machine learning for spatially constrained data clustering. We can group a large number of observations into a few clusters according to the pattern similarity between those variables.
The use of Deep Learning
Deep learning closely resembles the architecture and work functioning of the human brain, which provides it an ability to make decisions based on past knowledge. Deep learning can be attributed to three primary developments in GIS advancement including data, computing, and algorithmic improvements. One of the important uses of deep learning is digital mapping. The deep learning models can seamlessly work with satellite images on large geographic areas and arrive at a map containing all the roads for the region, to help with solutions like driving navigations, location information sharing, the digital blueprint of towns, etc. Apart from digital maps, deep learning can be taken a step further to advance computer vision models with this information collected, analyzed, and predicted.
Applying Computer Vision to Geospatial Analysis
Computer vision is a dream technology for everyone looking forward to being more productive when it is impossible to analyze and derive insights through traditional applications. Computer vision tasks and applications that can be applied to GIS range from Image classification, object detection, semantic segmentation, instance segmentation, and more.
1. Image Classification
Image classification for geospatial data analysis can be leveraged to categorize geotagged photos, videos, QR codes, etc. which based on location, time, and activities can help governments and businesses in making better decisions. For instance, a business can seek opportunities to run their billboards advertising based on the location, time, and activities of people in a particular location.
2. Object Detection
With object detection practice, the computer can identify objects within an image as well as their location based on geotagging. This can be a very important task for GIS—finding what information resides in satellite, aerial, or drone imagery, locating it, and plotting it on a map. This can be further used for infrastructure mapping, anomaly detection, and feature extraction.
3. Semantic Segmentation
Semantic segmentation allows machines to classify each pixel of an image and identify if it belongs to a particular class. Within GIS data, the semantic segmentation can be leveraged for land-cover classification or to extract transportation networks from satellite imagery.
4. Instance Segmentation
Instance segmentation is another type of image segmentation task related to computer vision that allows for more precise object detection. During this segmentation, after identifying every object, a precise boundary for each object is marked out. Which can be used to improve the user experience while understanding digital maps.
Application of Machine Learning in GIS
Geographic information systems are powerful tools for effective decision-making for any business or industry. As it allows the analysis of environmental, demographic, and typographic information. Machine learning compiled with GIS applications allows businesses to consumers to make real-time informed decisions that are beneficial. Here are a few use cases that define how machine learning can scale GIS data is experiencing increased outcomes.
1. Digital Mapping: GIS can be used to provide a visual interpretation of data. Google Maps is an excellent example of a web-based GIS mapping solution that people use for everyday navigation purposes. However, smart mapping technology powered by machine learning has significantly advanced and is used by governments and various industries to take an in-depth look at natural resources, availability of electricity, and water supplies, more.
2. Telecom and Network Services: Organizations can incorporate geographic data into their complex network design with the help of machine learning to predict, forecast, optimize, plan, and maintain activities. This insight enhances telecom processes through better customer relationship management and location services.
3. Accident Analysis and Hot Spot Analysis: GIS data helps to identify accident locations, and road networks can be optimized using data intelligence and machine learning. This information-based decision helps to improve road safety measures and allows better traffic management during natural disasters, disease outbreaks, and major industrial accidents.
4. Urban planning: GIS complied with machine learning allows analysis of data on urban growth and the direction of expansion. Accurately forecasted and applied, it can help in discovering new sites for further development, considering various factors that are necessary for successful building.
5. Transportation Planning: GIS data is commonly used for managing transportation issues. With the addition of environmental and tropical data in a GIS platform, using machine learning classification and regression techniques to identify the possible locations companies can plan for a new road or rail route.
6. Environmental Impact Analysis: Data gathered via GIS applications is vital for conserving natural resources, wildlife, and protecting the environment. Impact statements assess the magnitude of human impact on the environment, and data intelligence systems warn us on making necessary changes to protect and propel.
7. Agricultural Applications: GIS powered with intelligence helps to create more efficient farming techniques, alongside analyzing soil data in an advanced fashion. This can increase food production in different parts of the world. Geologists use GIS data to analyze soil, assess seismic information, and create 3D displays of geographic features. It can also be used to analyze rock characteristics, and identify the best location for different functions. Pest control is essential to agricultural production, and GIS technology plays a vital role in mapping out infested areas.
8. Flood damage estimation: Governments use GIS data to map flood risk areas and can use the information to coordinate relief efforts. Using machine learning techniques to predict this flood damage percentage in advance allows for making real-time decisions to reduce the cost of destruction and potential life loss.
9. Banking: Banking has evolved in recent years. With the presence of advanced technology, it has become market-driven, and a bank’s success depends mainly on its ability to provide personalized customer-driven services. GIS data plays an essential role in planning, organizing, and decision-making in the banking industry to optimize outcomes.
10. Surveying: Surveying involves measuring the location of objects on earth, and more organizations are using Global Navigation Satellite Systems (GNSS) for this function. This data incorporated into a GIS system can estimate areas and can be further leveraged to prepare and escalate digital maps.
11. Dairy and Supply Chain Industry: The dairy industry can leverage GIS data for distribution, production, and identifying the location of shops. It is a useful tool for planning in the field of dairy farm management and allows for better decision-making.
12. Irrigation Water Management: The availability of water directly affects crop production in a given region. GIS data can identify significant crops and determine yield, involving efficient techniques for spatial and time domains.
The Importance of AI in GIS
Artificial Intelligence for GIS is an omnipresent technology with various benefits of GIS functions including spatial data processing, and data analysis algorithms (GeoAI) to empower the integration of AI and GIS at best. In recent years, the importance of AI in GIS has gradually become the main focus of geoscience research and development. Here we shared a few benefits below:
1. With the introduction of AI, Geo-intelligence can be lead to technological innovation and create greater value. Geo-intelligence itself provides the most unique value and distinguishes GIS from other information technologies. Geospatial visualization, analysis, decision making, design, and control based on GIS, remote sensing, and satellite positioning technologies are a few areas where AI can enhance geo-intelligence.
2. AI can improve the intelligence of the GIS software process tool to facilitate the application unit to retrain the model according to its own data characteristics and improve the success rate and accuracy of model reasoning.
3. AI can enhance data collecting, survey, mapping, cartography, and interaction to help software intelligently classify and identify multi-source targets to build more sophisticated solutions like convenient mapping solutions at lower costs, save users from the tedious process of manual mapping, provide rich application interactions through voice, gestures, etc.
4. GIS can be leveraged to empower AI to further processes and develop advanced solutions like real-time traffic flow analysis, real-time geofence alerts, vehicle tracking, city component management for making informed decisions.
5. AI can enable researchers and GIS developers to leverage technologies like voice recognition, and natural language processing to decode voice and text-based information for further improvement and development of AI GIS applications.
The benefits of AI and its techniques like Machine Learning for GIS are immense. AI helps GIS professionals properly manage the huge volumes, variety, and velocity of data at speed. Which in return strengthens decisions made from location-based data. Empowering decision-makers to locate, select, and expand into target-rich sites, provides various benefits from improving autonomous vehicle functionality to governments making decisions on infrastructure development, forecasting the next big changes in climate in real-time, etc.
At SoulPage, we help businesses in the immersion of AI, machine learning, deep learning, and computer vision technologies to be best applicable to your business needs for improving productivity. To know more about GIS technology or to know more about how you can upgrade GIS applications or know more about how to integrate AI-powered GIS to your advantage, connect with us.