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Oral presentation; International audience; This article presents the achievements of the Candela project. This project aims to develop a platform and new algorithms for the handling, analysis and interpretation of earth observation data. The platform is hosted on the CREODIAS cloud ensuring the proximity of data and its processing. To ensure good performances the platform can scale up or down its computing resources. New algorithms based on machine learning methods for change detection and classification have been developed in the project. The results of these new algorithms are transformed into semantic data used to enrich earth observation products and provide new ways of exploitation. Finally, an end-to-end use of the platform is presented with a use case study of the impact of intense meteorological events on vineyards.

There is a variety of approaches for plant disease management, but they all converge in early and precise detection of stress as a major factor, in order to make applications more effective. In this work, a remote sensing approach for timely detection of stress caused by the fungal pathogen Verticillium dahliae in olive trees is presented. A detection model that calculates a vegetation index based on satellite spectral information (Sentinel-2) was developed and was used to estimate the stress levels in olive trees in the Halkidiki region, which are directly related to Verticillium infestations. Index values are calculated only for pixels within the boundaries of the parcels of interest. ArcGIS 10.5 software and Python 3 were used to process the data and to produce the resulting model together with the libraries concerned. This model has been implemented in an e-infrastructure as a cloud service that offers the end user the ability to create an account and store searches and results. The resulting map provides an overview of the situation regarding the levels of water stress on olive trees over a large area and in little time. Implementing this model as a cloud service ensures its automated and near real-time application and its viability, while webGIS facilitates the user's experience and icreases its usability by interested parties, such as agricultural consultants and scientists.

In the field of plant disease protection many approaches exist, but all acknowledge the necessity of fast and accurate identification of the source in order to make the most efficient applications. In this work, a remote sensing approach for the detection of Verticillium dahliae in olive fields is presented. A model was created that calculates a spectral index based on Sentinel-2 data and uses it to assess the levels of stress in olive trees in the region of Chalkidiki, Greece. The derived map provides an overview of the situation concerning stress levels in olive fields across a large area and in a small amount of time. Additionally, because of the constant flow of data from the Sentinel satellites, a time series of calculations can be used as an anomaly indicator for regions of interest. This model can be implemented in an e-Infrastructure as a cloud service to further enhance its usability by relevant parties, such as agricultural advisors and scientists.

This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images.