Earth‐Observation Data Access: A Knowledge Discovery Concept for Payload Ground Segments

In recent years the ability to store large quantities of Earth Observation (EO) satellite images has greatly surpassed the ability to access and meaningfully extract information from it. The state-of-the-art of operational systems for Remote Sensing data access (in particular for images) allows queries by geographical location, time of acquisition or type of sensor. Nevertheless, this information is often less relevant than the content of the scene (e.g. specific scattering properties, structures, objects, etc.). Moreover, the continuous increase in the size of the archives and in the variety and complexity of EO sensors require new methodologies and tools - based on a shared knowledge - for information mining and management, in support of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In addition, the current Payload Ground Segments (PGS) are mainly designed for Long Term Data Preservation (LTDP), in this article we propose an alternative solution for enhancing the access to the data content. Our solution presents a knowledge discovery concept, whose intention is to implement a communication channel between the PGS (EO data sources) and the end-user who receives the content of the data sources coded in an understandable format associated with semantics and ready for the exploitation. The first implemented concepts were presented in Knowledge driven content based Image Information Mining (KIM) and Geospatial Information Retrieval and Indexing (GeoIRIS) system as examples of data mining systems. Our new concept is developed in a modular system composed of the following components 1) the data model generation implementing methods for extracting relevant descriptors (low-level features) of the sources (EO images), analyzing their metadata in order to complement the information, and combining with vector data sources coming from Geographical Information Systems. 2) A database management system, where the database structure supports the knowledge management, feature computation, and visualization tools because of the modules for analysis, indexing, training and retrieval are resolved into the database. 3) Data mining and knowledge discovery tools allowing the end-user to perform advanced queries and to assign semantic annotations to the image content. The low-level features are complemented with semantic annotations giving meaning to the image information. The semantic description is based on semi-supervised learning methods for spatio-temporal and contextual pattern discovery. 4) Scene understanding counting on annotation tools for helping the user to create scenarios using EO images as for example change detection analysis, etc. 5) Visual data mining providing Human-Machine Interfaces for navigating and browsing the archive using 2D or 3D representation. The visualization techniques perform an interactive loop in order to optimize the visual interaction with huge volumes of data of heterogeneous nature and the end-user

Spatial decision support system for coastal flood management in Victoria, Australia

Coastal climate impact can affect coastal areas in a variety of ways, such as flooding, storm surges, reduction in beach sands and increased beach erosion. While each of these can have major impacts on the operation of coastal drainage systems, this thesis focuses on coastal and riverine flooding in coastal areas. Coastal flood risk varies within Australia, with the northern parts in the cyclone belt most affected and high levels of risk similar to other Asian countries. However, in Australia, the responsibility for managing coastal areas is shared between the Commonwealth government, Australian states and territories, and local governments. Strategies for floodplain management to reduce and control flooding are best implemented at the land use planning stage. Local governments make local decisions about coastal flood risk management through the assessment and approval of planning permit applications. Statutory planning by local government is informed by policies related to coastal flooding and coastal erosion, advice from government departments, agencies, experts and local community experts. The West Gippsland Catchment Management Authority (WGCMA) works with local communities, Victorian State Emergency Services (VCSES), local government authorities (LGAs), and other local organizations to prepare the West Gippsland Flood Management Strategy (WGFMS). The strategy aims at identifying significant flood risks, mitigating those risks, and establishing a set of priorities for implementation of the strategy over a ten-year period. The Bass Coast Shire Council (BCSC) region has experienced significant flooding over the last few decades, causing the closure of roads, landslides and erosion. Wonthaggi was particularly affected during this period with roads were flooded causing the northern part of the city of Wonthaggi to be closed in the worst cases. Climate change and increased exposure through the growth of urban population have dramatically increased the frequency and the severity of flood events on human populations. Traditionally, while GIS has provided spatial data management, it has had limitations in modelling capability to solve complex hydrology problems such as flood events. Therefore, it has not been relied upon by decision-makers in the coastal management sector. Functionality improvements are therefore required to improve the processing or analytical capabilities of GIS in hydrology to provide more certainty for decision-makers. This research shows how the spatial data (LiDAR, Road, building, aerial photo) can be primarily processed by GIS and how by adopting the spatial analysis routines associated with hydrology these problems can be overcome. The aim of this research is to refine GIS-embedded hydrological modelling so they can be used to help communities better understand their exposure to flood risk and give them more control about how to adapt and respond. The research develops a new Spatial Decision Support System (SDSS) to improve the implementation of coastal flooding risk assessment and management in Victoria, Australia. It is a solution integrating a range of approaches including, Light Detection and Ranging (Rata et al., 2014), GIS (Petroselli and sensing, 2012), hydrological models, numerical models, flood risk modelling, and multi-criteria techniques. Bass Coast Shire Council is an interesting study region for coastal flooding as it involves (i) a high rainfall area, (ii) and a major river meeting coastal area affected by storm surges, with frequent flooding of urban areas. Also, very high-quality Digital Elevation Model (DEM) data is available from the Victorian Government to support first-pass screening of coastal risks from flooding. The methods include using advanced GIS hydrology modelling and LiDAR digital elevation data to determine surface runoff to evaluate the flood risk for BCSC. This methodology addresses the limitations in flood hazard modelling mentioned above and gives a logical basis to estimate tidal impacts on flooding, and the impact and changes in atmospheric conditions, including precipitation and sea levels. This study examines how GIS hydrological modelling and LiDAR digital elevation data can be used to map and visualise flood risk in coastal built-up areas in BCSC. While this kind of visualisation is often used for the assessment of flood impacts to infrastructure risk, it has not been utilized in the BCSC. Previous research identified terrestrial areas at risk of flooding using a conceptual hydrological model (Pourali et al., 2014b) that models the flood-risk regions and provides flooding extent maps for the BCSC. It examined the consequences of various components influencing flooding for use in creating a framework to manage flood risk. The BCSC has recognised the benefits of combining these techniques that allow them to analyse data, deal with the problems, create intuitive visualization methods, and make decisions about addressing flood risk. The SDSS involves a GIS-embedded hydrological model that interlinks data integration and processing systems that interact through a linear cascade. Each stage of the cascade produces results which are input into the next model in a modelling chain hierarchy. The output involves GIS-based hydrological modelling to improve the implementation of coastal flood risk management plans developed by local governments. The SDSS also derives a set of Coastal Climate Change (CCC) flood risk assessment parameters (performance indicators), such as land use, settlement, infrastructure and other relevant indicators for coastal and bayside ecosystems. By adopting the SDSS, coastal managers will be able to systematically compare alternative coastal flood-risk management plans and make decisions about the most appropriate option. By integrating relevant models within a structured framework, the system will promote transparency of policy development and flood risk management. This thesis focuses on extending the spatial data handling capability of GIS to integrate climatic and other spatial data to help local governments with coastal exposure develop programs to adapt to climate change. The SDSS will assist planners to prepare for changing climate conditions. BCSC is a municipal government body with a coastal boundary and has assisted in the development and testing of the SDSS and derived many benefits from using the SDSS developed as a result of this research. Local governments at risk of coastal flooding that use the SDSS can use the Google Earth data sharing tool to determine appropriate land use controls to manage long-term flood risk to human settlement. The present research describes an attempt to develop a Spatial Decision Support System (SDSS) to aid decision makers to identify the proper location of new settlements where additional land development could be located based on decision rules. Also presented is an online decision-support tool that all stakeholders can use to share the results

Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Outsourcing and acquisition models comparison related to IT supplier selection decision analysis

This paper presents a comparison of acquisition models related to decision analysis of IT supplier selection. The main standards are: Capability Maturity Model Integration for Acquisition (CMMI-ACQ), ISO / IEC 12207 Information Technology / Software Life Cycle Processes, IEEE 1062 Recommended Practice for Software Acquisition, the IT Infrastructure Library (ITIL) and the Project Management Body of Knowledge (PMBOK) guide. The objective of this paper is to compare the previous models to find the advantages and disadvantages of them for the future development of a decision model for IT supplier selection

Realising intelligent virtual design

This paper presents a vision and focus for the CAD Centre research: the Intelligent Design Assistant (IDA). The vision is based upon the assumption that the human and computer can operate symbiotically, with the computer providing support for the human within the design process. Recently however the focus has been towards the development of integrated design platforms that provide general support irrespective of the domain, to a number of distributed collaborative designers. This is illustrated within the successfully completed Virtual Reality Ship (VRS) virtual platform, and the challenges are discussed further within the NECTISE, SAFEDOR and VIRTUE projects

Using ERP as a basis for Enterprise application integration

Architecting and implementing e-Business supply chain solutions across and within the modern day enterprise, is now becoming a necessity in order to maintain competitive and be adaptable to market needs. As such, the integration of information and processes is a vital step, using technologies such as using Enterprise Resource Planning (ERP), Supply Chain Management (SCM) and enterprise portal platforms. The effective sharing of resource planning and other enterprise related data across and within the enterprise is typically seen as a facet of a business to business (B2B) platform. However, such infrastructures typically involve a tight integration across intra and inter-organisational systems. This paper examines an Enterprise Application Integration (EAI) initiative taken by a global manufacturer of industrial automation products, which attempted to utilise ERP as an integration tool across its internal B2B infrastructure, to achieve such an aim. This paper discusses those integration considerations and complexities, experienced by the case company upon embarking on an EAI integration programme through the adoption of a core ERP as a catalyst for organizational change. In doing so the authors present an analysis of the inherent risks and limitations of this approach in terms of previously published literature in the field, relating to technology-driven organizational change and EAI impact and adoption frameworks