On the semantics of big Earth observation data for land classification
- URL: http://arxiv.org/abs/2204.11082v1
- Date: Sat, 23 Apr 2022 14:45:53 GMT
- Title: On the semantics of big Earth observation data for land classification
- Authors: Gilberto Camara
- Abstract summary: We argue for sound theories when working with big data.
We show concepts that are being used for analyzing satellite image time series as instances of events.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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. We argue for sound theories
when working with big data. After revising existing classification schemes such
as FAO's Land Cover Classification System (LCCS), we conclude that LCCS and
similar proposals cannot capture the complexity of landscape dynamics. We then
investigate concepts that are being used for analyzing satellite image time
series; we 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.
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