Soundscapes have been studied by researchers from various disciplines, each with different perspectives, approaches, and terminologies. Consequently, the research field determines the actual concept of a specific soundscape with the associated components and also affects the definition itself. This complicates interdisciplinary communication and comparison of results, especially when research areas are involved which are not directly focused on soundscapes. For this reason, we present a formalization that aims to be independent of the concepts from the various disciplines, with the goal of being able to capture the heterogeneous data structure in one layered model. Our model consists of time-dependent sound sources and geodata that influence the acoustic composition of a soundscape represented by our sensor function. Using a case study, we present the application of our formalization by classifying land use types. For this we analyze soundscapes in the form of recordings from different devices at 23 different locations using three-dimensional convolutional neural networks and frequency correlation matrices. In our results, we present that soundscapes can be grouped into classes, but the given land use categories do not have to correspond to them.
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