This paper discusses the challenges of using big Earth observation data for land classification. The approach taken is to consider pure data-driven methods to be insufficient to represent continuous change. I argue for sound theories when working with big data. After revising existing classification schemes such as FAO's Land Cover Classification System (LCCS), I conclude that LCCS and similar proposals cannot capture the complexity of landscape dynamics. I then investigate concepts that are being used for analyzing satellite image time series; I show these concepts to be instances of events. Therefore, for continuous monitoring of land change, event recognition needs to replace object identification as the prevailing paradigm. The paper concludes by showing how event semantics can improve data-driven methods to fulfil the potential of big data.
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
"On the semantics of big Earth observation data for land classification,"
Journal of Spatial Information Science:
Available at: https://digitalcommons.library.umaine.edu/josis/vol2020/iss20/7