The rapid integration of machine learning (ML) into critical decisionmaking systems has heightened concerns over fairness, particularly regarding spatial biases often tied to sensitive socioeconomic factors. In response, we propose a model-agnostic post-processing method for spatial bias mitigation that operates without access to the original training data. Our approach formulates an optimization problem that minimizes a fairness measure robust to gerrymandering, subject to a constraint specifying the allowable deviation from the original model's performance ensuring spatial fairness while preserving accuracy. This measure has a 0–1 scale, offering an intuitive way to quantify spatial bias. Comprehensive evaluations on real-world datasets show that our framework effectively reduces spatial bias and achieves fairer outcomes with minimal performance loss, outperforming other state-of-the-art post-processing methods. This work advances spatial fairness methodologies, offering practitioners an efficient, interpretable, and adaptable post-processing solution to mitigate location-based discrimination in ML applications.

With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated and analyzed, especially in IoT domains like energy and smart homes. Consequently, applications that require accurate short-term forecasts and predictions are also steadily increasing. In this paper, we provide an overview of a novel end-to-end platform that provides efficient ingestion, compression, transfer, query processing, and machine learning-based analytics for high-frequency and high-volume time series from IoT. The performance of the platform is evaluated using real-world dataset from RES installations. The results show the importance of high-frequency analytics and the surprisingly positive impact of error bounded lossy compression on machine learning in the form of AutoML. For example, when detecting yaw misalignments in wind turbines, an improvement of 9% in accuracy was observed for AutoML models on lossy compressed data compared to the current industry standard of 10-minute aggregated data. Thus, these small-scale experiments show the potential of the platform, and larger pilots are planned.

In this paper, we introduce an approach for improving the quality of the geocoding process. Geocoding refers to the procedure of mapping an address of textual form to a pair of accurate spatial coordinates. While there is a variety of available geocoders, both open source and commercial, that curate this mapping in either a semi-automated or fully-automated way, there is no one-size-fits-all system. Depending on the underlying algorithm of each geocoder, its output may be very accurate for some addresses, districts or countries, while failing to properly locate some others. Given that, our setup can be thought of as a meta-geocoding pipeline, built on top of the available geocoders. We propose a machine learning approach, which, given an address and a sequence of coordinate pairs suggested by standalone geocoders, it is able to identify the most accurate one. In order to achieve this, we formulate the task as a multi-class classification problem and introduce a series of domain specific training features, capturing essential information about each coordinate pair suggestion, as well as computing comparative metrics among different suggestions. These features are fed into several classification algorithms and are evaluated on a proprietary address dataset of a geo-marketing company. Furthermore, we present LGM-GC, a QGIS plugin, which provides the functionality of our approach through a user-friendly interface.

An important task on land registration processes is to be able to determine the prevalent data provenance for a finalized polygon that represents a cadastral parcel, since the finalized polygon is derived by the examination of a set of initial polygons, drawn from several individual registers (databases). These registers might contain different, partially similar or conflicting information regarding the ownership, usage and polygon geometry of a cadastral parcel. In such cases, the cadastration expert either select one of of the initial geometries, or (in cases none of the initial accurately represents the finalized land parcel) creates a new geometry. Maintaining this provenance information is of high importance for further cadastration and validation/quality assessment processes; however, due to the gradual and long lasting nature of cadastration procedures, this information is absent from large parts of cadastral databases. In this paper, we present an approach for effectively classifying such land parcel polygons with respect to their provenance information. We propose a method that can produce highly accurate provenance recommendations based only on attributes derived from the geometry of a land parcel. In particular, we implement a set of spatial training features, capturing polygon properties and relations. These features are fed into several classification algorithms and are evaluated on a proprietary dataset of a cadastration company.

Interlinking spatio-textual data comprises a core problem within the research literature, as well as a task of high practical importance in a plethora of industrial applications involving GIS systems. In its general form, it consists in identifying, between two sources of spatio-texual entities, pairs of entities that match, i.e. correspond to the same real-world entities. In this paper, we focus on interlinking spatio-textual entities based solely on their name, that is we handle the problem of toponym interlinking. To solve the problem, works in the literature exploit generic string similarity measures and either apply them as is, or integrate them as training features in classification models, without adapting/extending them based on the specific characteristics of toponyms. In this work, we showcase that domain knowledge can significantly improve the accuracy of toponym interlinking, by proposing domain specific similarity measures that take into account specificities of toponyms. We assess the implemented measures on Geonames and demonstrate significant increases in interlinking accuracy compared to baseline methods.

In this demonstration, we present LGM-PC, a QGIS plugin for automatic recommendation of categories for new POIs. LGM-PC allows users to train classification models on individual areas of POIs and, then, use these models in order to classify new POIs into categories. The tool produces ranked category recommendations, based solely on the name of the POI, its coordinates and properties of its surrounding POIs, which are already annotated with categories. The user is then required to validate the produced recommendations, by selecting the most fitting category. Being implemented as a QGIS plugin, LGM-PC allows the visualization of POIs on map layers, for further assisting the user in the final category selection task.

State of the art works for Point-Of-Interest (POI) classification use either traditional feature extraction methods, or, more recently, deep learning (DL) methods, in order to train classification models on historical data, i.e. POIs already annotated with categories, and then deploy these models to classify new, unannotated POIs. These methods are either inherently limited to learning simple, rather than complex, relationships, or learn embeddings trivially and without taking into account domain knowledge or semantic information, thus yielding disappointing classification results. In this vision paper, we discuss the problem of POI classification, identify limitations in the state of the art and propose novel research directions. We propose a framework for learning meaningful context embeddings for POIs, that incorporate domain knowledge and semantic information extracted from external data sources, and we discuss how the proposed approach can overcome inherent limitations of the state of the art. Specifically, we prescribe the construction of fine-grained embeddings, by refining the consideration of spatial neighborhoods of POIs, assessing and selecting the proper attributes to be used as context and target information in the embedding learning process, as well as by properly enriching these embeddings with external, semantic knowledge. Additionally, we advocate the joint utilization of traditional training features and embedding-derived features for POI classification, and argue towards the usefulness of the proposed approach.

Point of Interest (POI) data is widely used in many modern applications and services related to navigation, tourism, social networking, logistics, and many more. In this paper, we propose a comprehensive and vendor-agnostic data model to represent multi-faceted and enriched POI profiles. Harnessing the versatility of Linked Data technologies, this semantically rich ontology accommodates and extends existing POI formats for assembling and managing POI data from heterogeneous sources. Furthermore, we have developed the open-source software TripleGeo, which can effectively transform POI data from diverse sources and formats (geographical files, databases, and semi-structured data) to their RDF representations and vice versa. Thus, it is possible to import POI data from various existing systems and products, transfer and address the data integration challenges in the Linked Data domain, and export back the results. Our empirical study confirms the validity and efficiency of this framework for a variety of real-world POI assets and formats, underscoring its robustness to cope with scalable data volumes.

An immense wealth of data is already accessible through the Semantic Web and an increasing part of it also has geospatial context or relevance. Although existing technology is mature enough to integrate a variety of information from heterogeneous sources into interlinked features, it still falls behind when it comes to representation and reasoning on spatial characteristics. It is only lately that several RDF stores have begun to accommodate geospatial entities and to enable some kind of processing on them. To address interoperability, the OGC has recently adopted the GeoSPARQL standard, which defines a vocabulary for representing geometric types in RDF and an extension to the SPARQL language for formulating queries. In this paper, we provide a comprehensive review of the current state-of-the-art in geospatially-enabled semantic data management. Apart from an insightful analysis of the available architectures in industry and academia, we conduct an evaluation study on prominent RDF stores with geospatial support. We also compare their performance and attested capabilities to renowned DBMSs widely used in geospatial applications. We introduce a methodology suitable to assess RDF stores for robustness against large geospatial datasets, and also for expressiveness on a variety of queries involving both spatial and thematic criteria. As our findings demonstrate, the potential for query optimization, advanced indexing schemes, and spatio-semantic extensions is significant. Towards this goal, we point out several challenging issues for joint research by the GIS and Semantic Web communities.

Personalized retrieval models aim at capturing user interests to provide personalized results that are tailored to the respective information needs. User interests are however widely spread, subject to change, and cannot always be captured well, thus rendering the deployment of personalized models challenging. We take a different approach and study ranking models for user intent. We exploit user feedback in terms of click data to cluster ranking models for historic queries according to user behavior and intent. Each cluster is finally represented by a single ranking model that captures the contained search interests expressed by users. Once new queries are issued, these are mapped to the clustering and the retrieval process diversifies possible intents by combining relevant ranking functions. Empirical evidence shows that our approach significantly outperforms baseline approaches on a large corporate query log.