Geographical data and map views are defined in a Coordinate Reference Systems (CRS). There are many different coordinate systems, which can be used for different purposes. It is for example very common to use a Mercator coordinate projection for visualization of worldwide data on a map. In contrast, Mercator is not recommended for calculations or data storage, because of risk for lack of precision, especially near the poles.
An application often needs to handle geodata in different CRSs or visualize it in another CRS than the CRS of the data source. This means that the data needs to be reprojected at some point. These projections can both affect performance and cause a loss of accuracy if they are not performed efficiently.
Carmenta Engine has in-depth support for efficient coordinate transformations. In this article, we will describe how geographical coordinates are generally handled in the Carmenta Engine workflow, as well as go into some more advanced use cases and best practices.
Analyzing visibility within a geographical area is often key for a better understanding of the environment. Line-of-sight coverage analyses are often applied to highly dynamic objects with varied characteristics, such as a set of sweeping security cameras in an urban environment, a soldier with a pair of binoculars, or radar systems and other sensors. This guide will help you configure the visibility operators in Carmenta Engine to tackle your specific use cases and handle dynamic application data.
For an introduction on the different visibility analyses available, you can read the Visibility Analysis article in the Carmenta Engine SDK documentation. For this guide, we will use the LineOfSightOperator in a 2D view to get an easy example, but keep in mind that the behavior of the other visibility operators is the same. There are some additional data inputs that will need configuring, and the results you get are different, but the core concepts of dynamic visibility analysis are the ones presented in the following sections.