Deep Learning Techniques for Geospatial Data Analysis

TL;DR

This paper surveys how deep learning techniques are transforming geospatial data analysis, improving applications like remote sensing, GPS, and RFID data interpretation over traditional methods.

Contribution

It provides a comprehensive overview of deep learning algorithms applied to various geospatial data types and tasks, highlighting recent advancements and applications.

Findings

Deep learning outperforms traditional methods in geospatial analysis

Neural networks enhance object recognition and scene understanding

Deep learning enables more accurate remote sensing and GPS data interpretation

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.