Developing Error Handling Software for Object-Oriented Geographical Information

The inclusion of error handling capabilities within geographical information systems (GIS) is seen by many as crucial to the future commercial and legal stability of the technology. This thesis describes the analysis, design, implementation and use of a GIS able to handle both geographical information (GI) and the error associated with that GI. The first stage of this process is the development of an error-sensitive GIS, able to provide core error handling functionality in a form flexible enough to be widely applicable to error-prone GI. Object-oriented (OO) analysis, design and programming techniques, supported by recent developments in formal OO theory, are used to implement an error-sensitive GIS within Laser-Scan Gothic OOGIS software. The combination of formal theory and GIS software implementation suggests that error-sensitive GIS are a practical possibility using OO technology. While the error-sensitive GIS is an important step toward full error handling systems, it is expected that most GIS users would require additional high level functionality before use of error- sensitive GIS could become commonplace. There is a clear need to provide error handling systems that actively assist non-expert users in assessing, using and understanding error in GI. To address this need, an error-aware GIS offering intelligent domain specific error handling software tools was developed, based on the core error-sensitive functionality. In order to provide a stable software bridge between the flexible error-sensitive GIS and specialised error-aware software tools, the error-aware GIS makes use of a distributed systems component architecture. The component architecture allows error-aware software tools that extend core error-sensitive functionality to be developed with minimal time and cost overheads. Based on a telecommunications application in Kingston-upon-Hull, UK, three error-aware tools were developed to address particular needs identified within the application. First, an intelligent hypertext system in combination with a conventional expert system was used to assist GIS users with error-sensitive database design. Second, an inductive learning algorithm was used to automatically populate the error-sensitive database with information about error, based on a small pilot error assessment. Finally, a visualisation and data integration tool was developed to allow access to the error-sensitive database and error propagation routines to users across the Internet. While a number of important avenues of further work are implied by this research, the results of this research provide a blueprint for the development of practical error handling capabilities within GIS. The architecture used is both robust and flexible, and arguably represents a framework both for future research and for the development of commercial error handling GIS

Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation

Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines such as urban planning environmental sciences and health care. Thus remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result we propose a high level conceptual framework based on existing and standardized approaches offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Remote sensing information sciences research group

Research conducted under this grant was used to extend and expand existing remote sensing activities at the University of California, Santa Barbara in the areas of georeferenced information systems, matching assisted information extraction from image data and large spatial data bases, artificial intelligence, and vegetation analysis and modeling. The research thrusts during the past year are summarized. The projects are discussed in some detail

The development of object oriented Bayesian networks to evaluate the social, economic and environmental impacts of solar PV

Domestic and community low carbon technologies are widely heralded as valuable means for delivering sustainability outcomes in the form of social, economic and environmental (SEE) policy objectives. To accelerate their diffusion they have benefited from a significant number and variety of subsidies worldwide. Considerable aleatory and epistemic uncertainties exist, however, both with regard to their net energy contribution and their SEE impacts. Furthermore the socio-economic contexts themselves exhibit enormous variability, and commensurate uncertainties in their parameterisation. This represents a significant risk for policy makers and technology adopters. This work describes an approach to these problems using Bayesian Network models. These are utilised to integrate extant knowledge from a variety of disciplines to quantify SEE impacts and endogenise uncertainties. A large-scale Object Oriented Bayesian network has been developed to model the specific case of solar photovoltaics (PV) installed on UK domestic roofs. Three specific model components have been developed. The PV component characterises the yield of UK systems, the building energy component characterises the energy consumption of the dwellings and their occupants and a third component characterises the building stock in four English urban communities. Three representative SEE indicators, fuel affordability, carbon emission reduction and discounted cash flow are integrated and used to test the model s ability to yield meaningful outputs in response to varying inputs. The variability in the percentage of the three indicators is highly responsive to the dwellings built form, age and orientation, but is not just due to building and solar physics but also to socio-economic factors. The model can accept observations or evidence in order to create scenarios which facilitate deliberative decision making. The BN methodology contributes to the synthesis of new knowledge from extant knowledge located between disciplines . As well as insights into the impacts of high PV penetration, an epistemic contribution has been made to transdisciplinary building energy modelling which can be replicated with a variety of low carbon interventions

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

An Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies