Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Decentralized Detection of Topological Events in Evolving Spatial Regions

Qualitative information about topological events, like the merging or splitting of spatial regions, has many important applications in environmental monitoring. Examples of such applications include detecting the emergence of "hot spots" in sea temperature around a coral reef; or the break up and dispersion of an environmental pollution spill. This paper develops and tests an efficient, decentralized spatial algorithm capable of detecting high-level topological events occurring to spatial regions monitored by a wireless sensor network. The algorithm, called INQUIRE, is decentralized because at no point does any single system element possess global knowledge of the entire system state. Instead, INQUIRE relies purely on a sensor node's local knowledge of its own state and the state of its immediate network neighbors. Experimental evaluation of the INQUIRE algorithm demonstrates that our decentralized approach can substantially improve scalability of communication when compared with efficient centralized alternatives

Intuitive Direction Concepts

Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants\u27 competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences

Machine Learning for Multi-Robot Semantic Simultaneous Localization and Mapping

RÉSUMÉ L’automatisation et la robotique prennent une place de plus en plus importante dans notre vie quotidienne, avec de nombreuses utilisations possibles. Les robots pourraient nous épargner des tâches dangereuses et pénibles, ou rendre des choses impossibles jusqu’à maintenant possibles. Pour que les robots s’intègrent en toute sécurité dans notre monde et dans de nouveaux environnements inconnus, il est clef qu’ils soient équipés d’une capacité de per-ception, et en particulier qu’ils puissent se localiser par rapport à leur entourage. Afin d’être réellement indépendants, les robots doivent pouvoir le faire en se basant uniquement sur leurs propres capteurs, les plus couramment utilisés étant les caméras. Une solution pour obtenir de telles estimations est d’utiliser un algorithme de cartographie et localisa-tion simultanée (SLAM), dans lequel le robot va simultanément construire une carte de son environnement et estimer son propre état. Le SLAM avec un seul robot a fait l’objet de nombreux travaux scientifiques, et est désormais considéré comme un domaine de recherche mature. Cependant, l’utilisation d’une équipe de robots peut o˙rir plusieurs avantages en termes de robustesse, d’eÿcacité et de performances pour de nombreuses tâches. Dans ce cas, des algorithmes de SLAM multi-robots sont nécessaires pour permettre à chaque robot de bénéficier de l’expérience de toute l’équipe. Le SLAM multi-robot peut s’appuyer sur des solutions SLAM classiques, mais nécessite des adaptations et fait face à des contraintes de calculs et de communications supplémentaires. Un défi particulier dans le SLAM multi-robots est la nécessité pour les robots de trouver des fermetures de boucles inter-robots: des liens entre les trajectoires de di˙érents robots qui peuvent être trouvés lorsqu’ils visitent le même endroit. Deux catégories d’approches sont possibles pour détecter les fermetures de boucles inter-robots. Dans les méthodes indirectes, les robots communiquent pour vérifier s’ils ont cartographié un espace commun, puis tentent de trouver des fermetures de boucles à partir des données recueillies par chacun des robots dans cet espace. Dans les méthodes directes, les robots s’appuient directement sur les données de leurs capteurs pour estimer les fermetures de boucles. Chaque approche a des avantages et des inconvénients, mais les méthodes indi-rectes ont été plus étudiées récemment. Ce mémoire s’appuie sur les avancées récentes de la vision par ordinateur pour présenter des contributions à chaque catégorie d’approches pour la détection de fermetures de boucles inter-robots. Une première contribution est présentée pour la détection de fermetures de boucles indirecte dans une équipe de robots entièrement en communication. Elle utilise des constellations, une représentation sémantique compacte de l’environnement basée sur les objets qui le compose.----------ABSTRACT Automation and robotics are becoming more and more common in our daily lives, with many possible applications. Deploying robots in the world can extend what humans are capable of doing, and can save us from dangerous and strenuous tasks. For robots to be safely sent out in our real world, and in new unknown environments, one key capability they need is to perceive their environment, and particularly to localize themselves with respect to their surroundings. To truly be able to be deployed anywhere, robots should be able to do so relying only on their sensors, the most commonly used being cameras. One way to generate such an estimate is by using a simultaneous localization and mapping (SLAM) algorithm, in which the robot will concurrently build a map of its environment and estimate its state within it. Single-robot SLAM has been extensively researched and is now considered a mature field. However, using a team of robots can provide several benefits in terms of robustness, eÿciency, and performance for many tasks. In this case, multi-robot SLAM algorithms are required to allow each robot to benefit from the whole team’s experience. Multi-robot SLAM can build on top of single-robot SLAM solutions, but requires adaptations and faces computation and communication constraints. One particular challenge that arises in multi-robot SLAM is the need for robots to find inter-robot loop closures: relationships between trajectories of di˙erent robots that can be found when they visit the same place. Two categories of approaches are possible to detect inter-robot loop closures. In indirect methods, robots communicate to find if they have mapped the same area, and then attempt to find loop closures using data gathered by each robot in the place that was jointly visited. In direct methods, robots directly rely on data they gather from their sensors to estimate the loop closures. Each approach has its own benefits and challenges, with indirect methods being more popular in recent works. This thesis builds on recent computer vision advancements to present contributions to each category of approaches for inter-robot loop closure detection. A first approach is presented for indirect loop closure detection in a team of fully connected robots. It relies on constellations, a compact semantic representation of the environment based on objects that are in it. Descriptors and comparison methods for constellations are designed to robustly recognize places based on their constellation with minimal data exchange. These are used in a decentralized place recognition mechanism that is scalable as the size of the team increases. The proposed method performs comparably to state-of-the-art solutions in terms of performance and data exchanges require, while being more meaningful and interpretable

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Distributed Spatial Data Sharing: a new era in sharing spatial data

The advancements in information and communications technology, including the widespread adoption of GPS-based sensors, improvements in computational data processing, and satellite imagery, have resulted in new data sources, stakeholders, and methods of producing, using, and sharing spatial data. Daily, vast amounts of data are produced by individuals interacting with digital content and through automated and semi-automated sensors deployed across the environment. A growing portion of this information contains geographic information directly or indirectly embedded within it. The widespread use of automated smart sensors and an increased variety of georeferenced media resulted in new individual data collectors. This raises a new set of social concerns around individual geopricacy and data ownership. These changes require new approaches to managing, sharing, and processing geographic data. With the appearance of distributed data-sharing technologies, some of these challenges may be addressed. This can be achieved by moving from centralized control and ownership of the data to a more distributed system. In such a system, the individuals are responsible for gathering and controlling access and storing data. Stepping into the new area of distributed spatial data sharing needs preparations, including developing tools and algorithms to work with spatial data in this new environment efficiently. Peer-to-peer (P2P) networks have become very popular for storing and sharing information in a decentralized approach. However, these networks lack the methods to process spatio-temporal queries. During the first chapter of this research, we propose a new spatio-temporal multi-level tree structure, Distributed Spatio-Temporal Tree (DSTree), which aims to address this problem. DSTree is capable of performing a range of spatio-temporal queries. We also propose a framework that uses blockchain to share a DSTree on the distributed network, and each user can replicate, query, or update it. Next, we proposed a dynamic k-anonymity algorithm to address geoprivacy concerns in distributed platforms. Individual dynamic control of geoprivacy is one of the primary purposes of the proposed framework introduced in this research. Sharing data within and between organizations can be enhanced by greater trust and transparency offered by distributed or decentralized technologies. Rather than depending on a central authority to manage geographic data, a decentralized framework would provide a fine-grained and transparent sharing capability. Users can also control the precision of shared spatial data with others. They are not limited to third-party algorithms to decide their privacy level and are also not limited to the binary levels of location sharing. As mentioned earlier, individuals and communities can benefit from distributed spatial data sharing. During the last chapter of this work, we develop an image-sharing platform, aka harvester safety application, for the Kakisa indigenous community in northern Canada. During this project, we investigate the potential of using a Distributed Spatial Data sharing (DSDS) infrastructure for small-scale data-sharing needs in indigenous communities. We explored the potential use case and challenges and proposed a DSDS architecture to allow users in small communities to share and query their data using DSDS. Looking at the current availability of distributed tools, the sustainable development of such applications needs accessible technology. We need easy-to-use tools to use distributed technologies on community-scale SDS. In conclusion, distributed technology is in its early stages and requires easy-to-use tools/methods and algorithms to handle, share and query geographic information. Once developed, it will be possible to contrast DSDS against other data systems and thereby evaluate the practical benefit of such systems. A distributed data-sharing platform needs a standard framework to share data between different entities. Just like the first decades of the appearance of the web, these tools need regulations and standards. Such can benefit individuals and small communities in the current chaotic spatial data-sharing environment controlled by the central bodies

Intuitive Direction Concepts

Abstract Experiments in this article test the hypothesis that formal direction models used in artificial intelligence correspond to intuitive direction concepts of humans. Cognitively adequate formal models of spatial relations are important for information retrieval tasks, cognitive robotics, and multiple spatial reasoning applications. We detail two experiments using two objects (airplanes) systematically located in relation to each other. Participants performed a grouping task to make their intuitive direction concepts explicit. The results reveal an important, so far insufficiently discussed aspect of cognitive direction concepts: Intuitive (natural) direction concepts do not follow a one-size-fits-all strategy. The behavioral data only forms a clear picture after participants' competing strategies are identified and separated into categories (groups) themselves. The results are important for researchers and designers of spatial formalisms as they demonstrate that modeling cognitive direction concepts formally requires a flexible approach to capture group differences