Abstract
During the last decades, massive amounts of satellite images are becoming available that can be enriched with semantic annotations for the creation of value-added earth observation products. One challenge is to extract knowledge from the raw satellite data in an automated way and to effectively manage the extracted information in a semantic way, to allow fast and accurate decisions of spatiotemporal nature in a real operational scenario. In this work, we present a framework that combines supervised learning for crop type classification on satellite imagery time-series with semantic web and linked data technologies to assist in the implementation of rule sets by the European common agricultural policy (CAP). The framework collects georeferenced data that are available online and satellite images from the Sentinel-2 mission. We analyze image time-series that cover the entire cultivation period and link each parcel with a specific crop. On top of that, we introduce a semantic layer to facilitate a knowledge-driven management of the available information, capitalizing on ontologies for knowledge representation and semantic rules, to identify possible farmers noncompliance according to the Greening 1 (crop diversification) and SMR 1 rule (protection of waters against pollution caused by nitrates) rules of the CAP. Experiments show the effectiveness of the proposed integrated approach in three different scenarios for crop type monitoring and consistency checking for noncompliance to the CAP rules: the smart sampling of on-the-spot checks; the automatic detection of CAP's Greening 1 rule; and the automatic detection of susceptible parcels according to the CAP's SMR 1 rule.
Highlights
I N RECENT years, a massive quantity of georeferenced data is generated from many different sources like human activity and earth observation (EO), in situ sensors, satellite missions (e.g., Copernicus), and mobile phones
2) We demonstrate the use of spatial relationships in LOD (GeoSPARQL) toward assessing vulnerable parcels according to common agricultural policy (CAP) SMR-1 specifications
Listing 12: Semantic Query to Calculate the Smart Sampling Threshold According to the Number of Different Parcel Classes, That Correspondto Different Times of the Year, Which Exist in the Triplestore
Summary
I N RECENT years, a massive quantity of georeferenced data is generated from many different sources like human activity and earth observation (EO), in situ sensors, satellite missions (e.g., Copernicus), and mobile phones. The semantic enrichment and linking of these free and open data of this scale, frequency, and quality constitute a fundamental challenge for interoperability and automation in decision-making. EO data become useful only when analyzed together with other sources of data (e.g., geospatial data, in situ data) and turned into actionable information and knowledge for decision-making. In this context, linked data is a data paradigm that studies how one can make resource description framework (RDF) [1], [2] data available on the web and interconnect it with other data with the aim to increase its value. The scalability to accommodate big linked EO data remains an open issue [5]
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