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

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva

Neural networks have been in use since the 1950s and are increasingly prevalent in various domains of human activity, including military applications. Notably, GoogLeNet and convolutional neural networks, when appropriately trained, are instrumental in identifying and detecting individual objects within a complex set. In military scenarios, neural networks play a crucial role in the fire support process, especially when receiving target descriptions from forward observers. These networks are trained on image datasets to recognize specific features of individual elements or military objects, such as vehicles. As a result of this training, when presented with a new image, the network can accurately determine the type of vehicle, expediting the targeting process and enhancing the ability to provide a suitable response. This paper describes the application of neural networks for detecting and classifying artillery targets. It presents a specific problem and proposes a scientific solution, including explaining the methodology used and the results obtained.Neuronske mreže u uporabi su od pedesetih godina prošlog stoljeća i sve su zastupljenije u različitim područjima ljudske aktivnosti, uključujući vojne primjene. Posebno, GoogleNet i konvolucijske mreže, kada su odgovarajuće utrenirane, ključne su u prepoznavanju i otkrivanju pojedinih objekata unutar složenog skupa. U vojnim scenarijima neuronske mreže imaju ključnu ulogu u postupku pružanja potpore vatrom, posebno kada primaju opise ciljeva od prednjih topničkih motritelja. Ove mreže trenirane su na slikovnim skupovima podataka kako bi prepoznale specifičnosti pojedinih elemenata ili vojnih objekata, kao što su vozila. Kao rezultat treniranja, kada se prikaže nova slika, mreža može točno odrediti tip vozila, ubrzati postupak ciljanja i poboljšati sposobnost pružanja prikladnog odgovora. U radu se opisuje primjena neuronskih mreža za otkrivanje i klasifikaciju topničkih ciljeva. Rad predstavlja poseban problem i predlaže rješenje primjenom znanstvenog pristupa, uključujući objašnjenje korištene metodologije i dobivene rezultate

Strategies for Handling Spatial Uncertainty due to Discretization

Geographic information systems (GISs) allow users to analyze geographic phenomena within areas of interest that lead to an understanding of their relationships and thus provide a helpful tool in decision-making. Neglecting the inherent uncertainties in spatial representations may result in undesired misinterpretations. There are several sources of uncertainty contributing to the quality of spatial data within a GIS: imperfections (e.g., inaccuracy and imprecision) and effects of discretization. An example for discretization in the thematic domain is the chosen number of classes to represent a spatial phenomenon (e.g., air temperature). In order to improve the utility of a GIS an inclusion of a formal data quality model is essential. A data quality model stores, specifies, and handles the necessary data required to provide uncertainty information for GIS applications. This dissertation develops a data quality model that associates sources of uncertainty with units of information (e.g., measurement and coverage) in a GIS. The data quality model provides a basis to construct metrics dealing with different sources of uncertainty and to support tools for propagation and cross-propagation. Two specific metrics are developed that focus on two sources of uncertainty: inaccuracy and discretization. The first metric identifies a minimal?resolvable object size within a sampled field of a continuous variable. This metric, called detectability, is calculated as a spatially varying variable. The second metric, called reliability, investigates the effects of discretization on reliability. This metric estimates the variation of an underlying random variable and determines the reliability of a representation. It is also calculated as a spatially varying variable. Subsequently, this metric is used to assess the relationship between the influence of the number of sample points versus the influence of the degree of variation on the reliability of a representation. The results of this investigation show that the variation influences the reliability of a representation more than the number of sample points

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

AUTOMATED ANALYSIS OF NATURAL-LANGUAGE REQUIREMENTS USING NATURAL LANGUAGE PROCESSING

Natural Language (NL) is arguably the most common vehicle for specifying requirements. This dissertation devises automated assistance for some important tasks that requirements engineers need to perform in order to structure, manage, and elaborate NL requirements in a sound and effective manner. The key enabling technology underlying the work in this dissertation is Natural Language Processing (NLP). All the solutions presented herein have been developed and empirically evaluated in close collaboration with industrial partners. The dissertation addresses four different facets of requirements analysis: • Checking conformance to templates. Requirements templates are an effective tool for improving the structure and quality of NL requirements statements. When templates are used for specifying the requirements, an important quality assurance task is to ensure that the requirements conform to the intended templates. We develop an automated solution for checking the conformance of requirements to templates. • Extraction of glossary terms. Requirements glossaries (dictionaries) improve the understandability of requirements, and mitigate vagueness and ambiguity. We develop an auto- mated solution for supporting requirements analysts in the selection of glossary terms and their related terms. • Extraction of domain models. By providing a precise representation of the main concepts in a software project and the relationships between these concepts, a domain model serves as an important artifact for systematic requirements elaboration. We propose an automated approach for domain model extraction from requirements. The extraction rules in our approach encompass both the rules already described in the literature as well as a number of important extensions developed in this dissertation. • Identifying the impact of requirements changes. Uncontrolled change in requirements presents a major risk to the success of software projects. We address two different dimen- sions of requirements change analysis in this dissertation: First, we develop an automated approach for predicting how a change to one requirement impacts other requirements. Next, we consider the propagation of change from requirements to design. To this end, we develop an automated approach for predicting how the design of a system is impacted by changes made to the requirements

A Knowledge-based Approach for Creating Detailed Landscape Representations by Fusing GIS Data Collections with Associated Uncertainty

Geographic Information Systems (GIS) data for a region is of different types and collected from different sources, such as aerial digitized color imagery, elevation data consisting of terrain height at different points in that region, and feature data consisting of geometric information and properties about entities above/below the ground in that region. Merging GIS data and understanding the real world information present explicitly or implicitly in that data is a challenging task. This is often done manually by domain experts because of their superior capability to efficiently recognize patterns, combine, reason, and relate information. When a detailed digital representation of the region is to be created, domain experts are required to make best-guess decisions about each object. For example, a human would create representations of entities by collectively looking at the data layers, noting even elements that are not visible, like a covered overpass or underwater tunnel of a certain width and length. Such detailed representations are needed for use by processes like visualization or 3D modeling in applications used by military, simulation, earth sciences and gaming communities. Many of these applications are increasingly using digitally synthesized visuals and require detailed digital 3D representations to be generated quickly after acquiring the necessary initial data. Our main thesis, and a significant research contribution of this work, is that this task of creating detailed representations can be automated to a very large extent using a methodology which first fuses all Geographic Information System (GIS) data sources available into knowledge base (KB) assertions (instances) representing real world objects using a subprocess called GIS2KB. Then using reasoning, implicit information is inferred to define detailed 3D entity representations using a geometry definition engine called KB2Scene. Semantic Web is used as the semantic inferencing system and is extended with a data extraction framework. This framework enables the extraction of implicit property information using data and image analysis techniques. The data extraction framework supports extraction of spatial relationship values and attribution of uncertainties to inferred details. Uncertainty is recorded per property and used under Zadeh fuzzy semantics to compute a resulting uncertainty for inferred assertional axioms. This is achieved by another major contribution of our research, a unique extension of the KB ABox Realization service using KB explanation services. Previous semantics based research in this domain has concentrated more on improving represented details through the addition of artifacts like lights, signage, crosswalks, etc. Previous attempts regarding uncertainty in assertions use a modified reasoner expressivity and calculus. Our work differs in that separating formal knowledge from data processing allows fusion of different heterogeneous data sources which share the same context. Imprecision is modeled through uncertainty on assertions without defining a new expressivity as long as KB explanation services are available for the used expressivity. We also believe that in our use case, this simplifies uncertainty calculations. The uncertainties are then available for user-decision at output. We show that the process of creating 3D visuals from GIS data sources can be more automated, modular, verifiable, and the knowledge base instances available for other applications to use as part of a common knowledge base. We define our method’s components, discuss advantages and limitations, and show sample results for the transportation domain

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

The 1988 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies