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Deep Learning Techniques for Geospatial Data Analysis

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Machine Learning Paradigms

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 18))

Abstract

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics (iv) Deep-learning techniques for RFID data analytics.

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Acknowledgements

The authors acknowledge the funding provided by Ministry of Human Resource Development (MHRD), Government of India, under the Pandit Madan Mohan National Mission on Teachers Training (PMMMNMTT). The work presented in this chapter is based on the course material developed to train engineering teachers on the topics of Geospatial Analysis and Product Design Engineering.

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Authors and Affiliations

  1. Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, 402103, Raigad, India

    Arvind W. Kiwelekar, Geetanjali S. Mahamunkar & Laxman D. Netak

  2. Department of Information and Technology, Veermata Jijabai Technical Institute, Mumbai, 4000019, India

    Valmik B Nikam

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  1. Arvind W. Kiwelekar
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Correspondence to Arvind W. Kiwelekar .

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  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  2. University of Technology Sydney, NSW, Australia

    Lakhmi C. Jain

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Kiwelekar, A.W., Mahamunkar, G.S., Netak, L.D., Nikam, V.B. (2020). Deep Learning Techniques for Geospatial Data Analysis. In: Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Learning and Analytics in Intelligent Systems, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-49724-8_3

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