Understanding Geospatial Data

Abstract

Geospatial data is described by its location on the Earth. There are many sources of geospatial data (e.g., remote sensing, point cloud data, LiDAR, GPS, Internet, IoT, etc.) that are acquired at different resolutions and characteristics. Two broad categories are raster data and vector data. The geographic features on the Earth surface can be represented in the form of point, line, and polygon, and their coordinates are used in GIS. Attribute data, which is the information about the geospatial data, helps in establishing the relationships between various objects on the Earth's surface in GIS. Maps and navigation are important uses for spatial data. Industries are using geospatial data analysis for enhancing their business, and governments are using them in various infrastructural projects, tracking the resources, etc. Geospatial data using GIS makes it easier to identify the patterns and visualize trends in location-based applications. It is expected that the use of AI and machine learning will further enhance the utility of geospatial data in automisation and real-time analysis.