Neural Network Guided Variability Detection in Geospatial Data

Abstract

Geospatial data refers to data associated with a specific location on the earth's surface. It plays an important role in a wide range of applications, including environmental monitoring, agriculture planning, mapping, and routing. With the increasing availability of geospatial data from various sources, there is a growing need for methods to validate and verify the accuracy and consistency of this data. Variabilities in such data can have significant impacts on the reliability of the derived information and decision-making processes. Thus, detecting these variabilities is of extreme importance for ensuring the quality of geospatial data. This PhD dissertation focuses on the development of deep neural network methods for detecting variabilities in geospatial data. Variabilities refer to differences between datasets that are otherwise expected to be consistent. Variabilities in geospatial data can occur due to various reasons such as measurement errors, misalignments between datasets, different algorithms used in processing metadata, and changes in real-world phenomena over time. The main objective of this dissertation is to present methods for evaluating the accuracy and consistency of geospatial data, detecting and reporting variabilities found in such data, and providing insights into how data is evolving over time. The effectiveness of the proposed methods will be evaluated using real-world datasets in various applications. This dissertation contributes to advancing the field of geospatial data management by providing new and innovative methods for detecting and reporting variabilities in geospatial data empowering decision-making and future planning.