Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are insufficiently interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap through space tiling techniques, which significantly restrict the search space. Yet, the state-of-the-art techniques operate exclusively in a batch manner that produces results only after processing all their geometries. In this work, we address this issue by defining the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose a static progressive algorithm, which employs a fixed processing order, and a dynamic one, whose processing order is updated whenever new topological relations are discovered. We equip both algorithms with a series of weighting schemes and explain how they can be adapted to massive parallelization with Apache Spark. We conduct a thorough experimental study over a six large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art. Special care is also taken to analyze the performance of the various weighting schemes.

Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner, and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, that enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this paper, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this paper are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs.

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Geospatial data constitutes a considerable part of (Semantic) Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling, which determines the approach that reduces the search space, (ii) Budget-awareness, which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode, which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial's architecture and capabilities and perform thorough experiments to provide interesting insights about the relative performance of its algorithms.

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