On the semantics of big Earth observation data for land classification

TL;DR

This paper emphasizes the importance of sound theoretical frameworks for analyzing big Earth observation data in land classification, advocating for event-based semantics over traditional object identification methods.

Contribution

It introduces an event-based semantic approach for land change analysis, challenging existing object-centric classification schemes like LCCS.

Findings

Object identification schemes cannot capture landscape dynamics.

Event recognition provides a better paradigm for continuous land monitoring.

Event semantics can enhance data-driven land classification methods.

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.