Geospatial Data: Acquisition, Applications, and Challenges

Abstract

In the current era of big data, geospatial data plays a pivotal role in a wide spectrum of scientific and applied fields related to the Earth’s surface. Advances in sensor technologies, satellite imagery, and field surveys have enabled the collection and generation of vast amounts of geospatial data with ever-increasing temporal and spatial resolution. Therefore, geospatial information is critical for making well-informed decisions about the social, economic, and environmental aspects of sustainable development. Furthermore, geospatial data can be employed to analyze, model, and map sustainable development issues, providing a framework for collaboration, consensus, and evidence-based decision-making. However, the geospatial data strategy should be based on overarching goals that aim to create an environment in which geospatial data is credible, reliable, and serves and supports the purpose of its use. Despite the enormous potential benefits of utilizing geospatial data in various fields, there are still challenges to overcome that limit the ability to unlock its full potential. Geospatial data, when combined with advanced technologies such as remote sensing and geographic information systems, as well as advanced data analytics, deep learning, and machine learning techniques, serves as a vital and reliable source of information for decision-making in sustainable development for both the public and private sectors. Additionally, this integration helps in addressing challenges associated with this data type. This chapter addresses the fundamental aspects of geospatial data by discussing concepts, data acquisition, tools, data types, data quality, data management, data visualization, applications, and challenges to provide an overview of this big data type.

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