Remote sensing applications pan across various domains, like disaster management, climate studies, socio-economic analysis, geology, astronomy and astrophysics, and many more. A far greater number of satellites are orbiting the Earth than ever. These satellites inspect many different areas of the Earth, as a result of which a large amount of scientific data is downlinked on the ground stations every day. Scientists, researchers, and agencies are always trying to make full use of this data. But this analysis is quite challenging due to the high quantity of data.

If you're a Geospatial analytics enthusiast, this article is for you. Have you ever come across terminologies like Digital Elevation Model, Digital Surface Model, Elevation or Height Maps, when reading about Geospatial data? Today, we'll learn the basics of these digital models that aid geospatial data  analytics. Maybe the naming sounds intuitive enough, implying that all these terms represent the heights of the world locations.

The word ‘Geospatial’ indicates that data has some Geographic component to it. For us humans, it means the Earth’s surface, anywhere on the globe. It can be a city’s map, its street lines, junctions, or your address. This data comes with endless possibilities and once you get access to the Geospatial data, you can do mapping (of roads, canals, train routes, etc.), disaster management (flood / wild-fire damage estimation, etc.), and much more.

The tech world is witnessing rapid growth in Geographic Information System (GIS) applications powered by satellite data. The data community is increasingly relying upon these space-bound instruments to solve problems related to environmental monitoring, hazard detection, asset management and more.

Our series on Satellite data applications will help you get started with understanding data, performing analysis and building interesting applications through demos.