67,184 research outputs found

    FLIAT, an object-relational GIS tool for flood impact assessment in Flanders, Belgium

    Get PDF
    Floods can cause damage to transportation and energy infrastructure, disrupt the delivery of services, and take a toll on public health, sometimes even causing significant loss of life. Although scientists widely stress the compelling need for resilience against extreme events under a changing climate, tools for dealing with expected hazards lag behind. Not only does the socio-economic, ecologic and cultural impact of floods need to be considered, but the potential disruption of a society with regard to priority adaptation guidelines, measures, and policy recommendations need to be considered as well. The main downfall of current impact assessment tools is the raster approach that cannot effectively handle multiple metadata of vital infrastructures, crucial buildings, and vulnerable land use (among other challenges). We have developed a powerful cross-platform flood impact assessment tool (FLIAT) that uses a vector approach linked to a relational database using open source program languages, which can perform parallel computation. As a result, FLIAT can manage multiple detailed datasets, whereby there is no loss of geometrical information. This paper describes the development of FLIAT and the performance of this tool

    ARTSCENE: A Neural System for Natural Scene Classification

    Full text link
    How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using multiple spatial scales to efficiently accumulate evidence for gist and texture. ARTSCENE embodies a coarse-to-fine Texture Size Ranking Principle whereby spatial attention processes multiple scales of scenic information, ranging from global gist to local properties of textures. The model can incrementally learn and predict scene identity by gist information alone and can improve performance through selective attention to scenic textures of progressively smaller size. ARTSCENE discriminates 4 landscape scene categories (coast, forest, mountain and countryside) with up to 91.58% correct on a test set, outperforms alternative models in the literature which use biologically implausible computations, and outperforms component systems that use either gist or texture information alone. Model simulations also show that adjacent textures form higher-order features that are also informative for scene recognition.National Science Foundation (NSF SBE-0354378); Office of Naval Research (N00014-01-1-0624

    Environmental Risks Monitoring of Shipwrecks in Italian Seas

    Get PDF
    After a description of the international regulatory framework, this paper examines the European project DE.E.P.P. and provides an overview of shipwreck databases in Italy. Afterwards, it reconstructs the recent history of the supertanker VLCC Haven which represents one of the largest Mediterranean shipwrecks. The findings of this paper emphasize the need to unify all the various agencies databases into a national Territorial Information System of potentially polluting wrecks. This System would be completed by all the information available in archives and press, to allow an adequate environmental risk monitoring and classification of shipwrecks in the Italian seas

    Database of historic ports and coastal sailing routes in England and Wales

    Get PDF
    This data paper presents a reconstruction of historical ports and coastal routes in England and Wales during the age of the sailing ship, ending at the beginning of the twentieth century. The dataset was created by an amalgamation of twenty different sources,including geographical data, primary sources and secondary literature. Ports found in historical documents were listed by year of appearance and georeferenced. Ports that appear in multiple sour-ces were listed only once. Coastal routes between ports were drawn based on navigation charts and bathymetry data, distinguishing five categories with different characteristics. Visibility from the coast was deduced from elevation rasters and lighthouse locations. Subsequently both ports and coastal routes were checked using topo-logical rules to ensure the connectivity of the network. The data is provided in shapefile format with all the attributes and can be analysed using Geographical Information Systems (GIS) for different types of geographical and historical studies

    Harmonising Chart and Navigation-related Information on ECDIS

    Get PDF
    ECDIS is a real-time navigation system that integrates a variety of chart and navigation-related information. More than simply a replacement for a paper nautical chart, ECDIS is capable of continuously determining a vessel\u27s position in relation to land, charted objects, aids-to-navigation, and unseen hazards. Increasingly, ECDIS is being used for both navigation and collision-avoidance tasks. There is growing concern about the display of ever-increasing amounts of both chart and navigation-related information. When it comes to using ECDIS, displaying more information is not necessarily better. Too much information (i.e., clutter) may only lead to confusion. In this regard, there is need to \u27harmonize\u27 the simultaneous display of both chart and navigation-related information

    Right for the Right Reason: Training Agnostic Networks

    Get PDF
    We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

    We All Live in a Virtual Submarine

    Get PDF
    Our seas and oceans hide a plethora of archaeological sites such as ancient shipwrecks that, over time, are being destroyed through activities such as deepwater trawling and treasure hunting. In 2006, a multidisciplinary team of 11 European institutions established the Venus (Virtual Exploration of Underwater Sites) consortium to make underwater sites more accessible by generating thorough, exhaustive 3D records for virtual exploration
    corecore