Applications of Machine Learning in Geography: Present and future trends | Adamas University

Applications of Machine Learning in Geography: Present and future trends

Applications of Machine Learning in Geography Geography, Machine Learning

Applications of Machine Learning in Geography: Present and future trends

Machine learning is one word at present time machine learning can be defined as a system where computers can perform tasks that require human intervention and intelligence. Machine learning is part of artificial intelligence which is growing rapidly in the present era. Artificial intelligence (AI) is the broader umbrella of computer-driven applications where Human support is hardly required. Deep learning is a subset of machine learning. AI is also associated with the development and design of algorithms.  Artificial intelligence and machine learning have reduced the role of humans in many activities Like in industry public affairs research and academia. Geography as a discipline has also benefited from the applications of Artificial Intelligence and machine learning. Geographical information system Remote Sensing and GIScience as a whole get the benefit of machine learning which is termed GeoAI. Different organizations like the American Association of Geographers (AAG) in Environment System Research Institute (ESRI) and Microsoft are collaborating to develop a GeoAI-based platform for the analysis and visualization of different data. 

The advantage of machine learning algorithms lies in the fact that the algorithm can use the data pattern to train itself and come up with better predictive or classification solutions. Machine learning is classified into different groups supervised learning, unsupervised learning, Semi-supervised learning, and reinforced learning.

In recent years automation in geography and location-based study is taking place at a quick pace. Mechanisms to monitor different spheres (terrestrial, atmospheric, and aquatic) are growing at high speed. This leads to a large quantum of data availability of different types and formats. Machine learning is helping to analyze this data using different advanced algorithms like Support vector machine, Random Forest, Artificial neural network (ANN), or even one step ahead deep learning methods like Convolution Neural network (CNN) and dynamic learning methods like Q- Learning.

In the area of human geography, machine learning has been used to explore the distribution of products and goods, population genetics, detecting, characterizing, and evaluating human epidemiology, tourism projections, detecting military patterns, international politics, and agricultural production systems. Within physical geography, ML is used to advance research on environmental diversity, biogeography, and abundance, water resource management, climate modeling, climate change, etc.

In the Geographical Information System (GIS), users can use it to comprehend the spatial elements of their work and connect it to data such as population data. The GIS data can be utilized for a variety of reasons, including transportation, drought analysis, agricultural, disease outbreak analysis, land occupation, and so on. At the same time, GIS enables the storing of a large volume of data in a secure manner, as well as access to it at any time and on a rapid basis. So, the purpose of this chapter is to examine previous works and research in this area to better understand the current status and capabilities; also, it will be an attempt to prepare for future improvements in the field of GIS. 

There are also many challenges faced by machine learning in the part of lack of data and as well as the quality of data. Quality issues in machine learning lead to improper predictions and classification. As the technology is still evolving real-world complexities are sometimes mishandled by the algorithms. Ethical issues are very much prominent in countering human geographical contents. They’re also voids left for spatially explicit machine learning and other spatiotemporal models. This vacuum of Spatio-temporal models hinders the growth of machine learning applications in geography. 

In the future, machine learning will be more robust with advanced GeoAI algorithms which can encounter issues like handling large and complex data. More spatial explicit models and algorithms will be available to provide solutions to complex and multifaceted real-world problems. The emergence of automatic machine learning (AutoML) is showing the path for such a solution. In solving geographical problems of the future integration is required between the conceptualisation and implementation of models. As artificial intelligence has started touching down nearly all spheres of life, machine learning is going to play a key role in unlocking the different mysteries of nature near future.

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