Anna Rampini

National Research Council, Roma, Latium, Italy

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Publications (65)26.08 Total impact

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    ABSTRACT: The paper analyses the challenges and problems posed by the use of Volunteered Geographic Information (VGI) in citizen science and a proposal is formulated for assessing VGI quality based on a linguistic decision making approach so as to allow its feasible use for scientific purposes. VGI quality is represented by indicators at distinct levels of granularity which take into account the distinct components of the VGI items. The quality indicators represent both the extrinsic quality, depending on the characteristics and reputation of the sources of information; the intrinsic quality, depending on the distinct accuracy and precision of information; and, last but not least, the pragmatic quality, depending on the user needs and intended purposes. In order to assess the pragmatic quality of VGI items, a linguistic decision making approach is defined that allows users to rank and finally filter the VGI items based on the satisfaction of distinct criteria expressed by means of both linguistic terms, defining soft constraints on the distinct quality indicators, and linguistic aggregators, defining fuzzy operators which combine the satisfaction degrees of the soft constraints at distinct hierarchical levels to yield the final satisfaction of the VGI items. Finally, an example of quality assessment in a glaciological citizen science project is discussed.
    Information Sciences: an International Journal. 02/2014; 258:312-327.
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    ABSTRACT: In 2005, the EU made the strategic choice of developing a space-based programme, called Global Monitoring for Environment and Security (GMES). GMES is an independent Earth monitoring initiative led by the European Union and carried out in partnership with the Member States and the European Space Agency (ESA). Its primary objective is to provide information services that give access to accurate data and information in the field of the environment and security and are tailored to the needs of users. However, at the regional level, stakeholders are often not aware about the potential benefits of services Europe's GMES initiative can provide; yet Europe's ca. 350 regions represent a large reservoir of potential GMES users where GMES services can add value to existing services. Refining data, products and services from global GMES services in the various domains (i.e. land, marine, atmosphere, emergency response, security and climate change), GMES downstream services may be customised to individual user needs, many of which are to be found a the regional level. Within a number of regions, links between the different types of stakeholders have grown over the years. Often, individual actors have developed inter-regional links but their linkage is in most cases not formalised. When looking at the European scale, that overall awareness of GMES downstream opportunities is still very low with respect to the potential benefits regions could draw from a wider participation. However, being aware of the potential of GMES, of the important role they can play and of the need for exchanging experiences, pioneering Local and Regional Authorities (LRAs) intending to retrieve benefit from space technologies, including GMES, have now started to collaborate within structured networks, NEREUS being the most advanced example. The logically next step is that LRAs engage in a dialogue with service-industry and European decision-makers to maximize the benefits from these innovative tools which have significant impact on the economy, environment and the quality of life of the citizens To this aim since 2011 the system of Regional Contact Offices (RCOs) was promoted by the EU FP7 DORIS_Net (Downsteam Observatory organized by Regions Active in Space - Network, http://www.doris-net.eu/) project as the regional link to the services provided by the European GMES programme. Since then a first nucleus of 12 pilot European Regions were working together establishing 6 first RCOs around Europe. This paper will present RCOs network goals, achievements and perspectives as well as its planned actions devoted to improve quality of Space Technology products from one side, to promote awareness and use of them by potential end-users (and particularly LRAs), from the other side.
    04/2013;
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    ABSTRACT: Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree‐day‐based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow‐covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE‐SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright © 2011 John Wiley & Sons, Ltd.
    Hydrological Processes 10/2012; 26(22):3405-3415. · 2.50 Impact Factor
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    ABSTRACT: The paper illustrates the potentials of geospatial data and services to access historical digital atlas for landscape analysis and territorial government. The experience of a historical geo-portal, the ‘Atl@nte dei Catasti Storici', in the management of geo-referenced and non-geo-referenced maps - ancient cadastral and topographic maps of Lombardy Region - can be considered a case study with common aspects to many European regions having an extensive cartographic heritage. The development of downstream web based services to integrate other data sources (current maps, satellite and UAV airborne photogrammetry, multi-spectral images and derived products) provides new scenarios for retrieving geospatial knowledge of territory, bridging the gap in supporting a sustainable management of the territory.
    Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II; 06/2012
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    ABSTRACT: This study deals with the evaluation of accuracy benefits offered by a fuzzy classifier as compared to hard classifiers using satellite imagery for thematic mapping applications. When a crisp classifier approach is adopted to classify moderate resolution data, the presence of mixed coverage pixels implies that the final product will have errors, either of omission or commission, which are not avoidable and are solely due to the spatial resolution of the data. Theoretically, a soft classifier is not affected by such errors, and in principle can produce a classification that is more accurate than any hard classifier. In this study we use the Pareto boundary of optimal solutions as a quantitative method to compare the performance of a fuzzy statistical classifier to the one of two hard classifiers, and to determine the highest accuracy which could be achieved by hard classifiers. As an application, the method is applied to a case of snow mapping from Moderate-Resolution Imaging Spectroradiometer (MODIS) data on two alpine sites, validated with contemporaneous fine-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The results for this case study showed that the soft classifier not only outperformed the two crisp classifiers, but also yielded higher accuracy than the maximum theoretical accuracy of any crisp classifier on the study areas. While providing a general assessment framework for the performance of soft classifiers, the results obtained by this inter-comparison exercise showed that soft classifiers can be an effective solution to overcome errors which are intrinsic in the classification of coarse and moderate resolution data.
    International Journal of Remote Sensing 01/2010; 31(23):6189-6203. · 1.36 Impact Factor
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    ABSTRACT: The objective of this study is to propose and evaluate a method for snow cover mapping during clouds using the daily MODIS/Terra snow cover product. The proposed SNOWL approach is based on reclassifying pixels assigned as clouds to snow or land according to their relative position to the regional snow-line elevation. The accuracy of the SNOWL approach is evaluated over Austria, using daily snow depth measurements at 754 climate stations and daily MODIS/Terra images in the period July 2002–December 2005. The results indicate that the SNOWL method provides a robust snow cover mapping over the entire region even if the MODIS/Terra cloud cover is as large as 90%. Cloudiness is decreased from 60% (MODIS/Terra) to 10% (SNOWL) without hardly any change in mapping accuracy. Sensitivity analyses indicate that the estimation of the regional snow-line elevation is particularly sensitive to the misclassification of cirrus clouds as snow in the period between May and October.
    Journal of Hydrology. 01/2010;
  • Journal of Irrigation and Drainage Engineering-asce - J IRRIG DRAIN ENG-ASCE. 01/2010; 136(4).
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    ABSTRACT: In this paper we analyze the limitations of current recommendations of the INSPIRE (Infrastructure for Spatial Information in Europe) Directive as far as the temporal metadata definition for discovery purposes, and propose its extension so as to allow the representation and management of imperfect spatio- temporal metadata. We propose to extend the metadata in order to cope with the requirements of both metadata producers, who often are unable to specify precise values, and users who submit queries to catalog services for discovering interesting data, who may express soft selection conditions on metadata values. The proposal is illustrated and explained through an example taken from an active Spatial Data Infrastructure (SDI).
    Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference, Lisbon, Portugal, July 20-24, 2009; 01/2009
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    ABSTRACT: When looking for geodata in a Spatial Data Infrastructure (SDI), the user expresses precise selection conditions on the values of metadata in discovery services. In order to obtain a list of results, corresponding values of metadata must exactly match such conditions. This practice suffers from several drawbacks. First of all, with respect to the temporal characterization, available recommendations for metadata specification of the INSPIRE Directive are inadequate to satisfy the several semantics of the temporal conditions. To this aim, we propose to extend the metadata to enrich the description of geodata, with the possibility to indicate temporal metadata related to both the observations and the observed event as well as of specifying the temporal resolution of observations. Furthermore, we introduce a proposal to manage temporal series of geodata observed at different dates. In order to represent the uncertain and incomplete time knowledge on the available geodata, we allow the specification of imperfect temporal values which is not considered nor managed within INSPIRE. Last but not least, as far as the discovery service providing searching facilities on metadata catalogs, we propose to allow expressing flexible selection conditions, i.e. tolerant to under-satisfaction, so as to retrieve geodata in decreasing order of relevance to the user needs, as it usually occurs on the Web when using search engines. This contribution discusses the above limitations and the related solutions, expressed in terms of formal and operational methods taking into account imperfect temporal metadata values and flexible search conditions. Proposals will be illustrated with examples taken from an already existing European SDI.
    01/2009;
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    ABSTRACT: Current efforts for simulating or forecasting snowmelt are time-consuming and laborious; the AWARE project (A tool for monitoring and forecasting Available WAter REsource in mountain environments) has been motivated by the urgent need to facilitate the prediction of medium-term flows from snowmelt for an effective and sustainable water resources management. Its main goal is to provide innovative tools for monitoring and predicting water availability and distribution in drainage basins where snowmelt is a major component of the annual water balance. The particular objective of the effort reported here is to compare results obtained from the MODIS sensor on NASA Terra and Aqua satellite and next generation sensors AATSR and MERIS on board ESA Envisat satellite. The vehicle for this comparison is the AWARE Geoportal (http://www.aware- eu.info/eng/home.htm) which is a WWW implementation of the Snowmelt Runoff Model (SRM). The river basin chosen for analysis is the Upper Rio Grande of North America. The time period for analysis encompasses the Water Years 2005, 2006, and 2007 (October 2004 - September 2007). The reason for this is to ensure that data from all three sensors are available for use and to investigate variable climate conditions. A successful comparison between the various sensors will help demonstrate that the AWARE approach will facilitate future processing of several years' worth of snow cover data from a variety of sensors that covers large extremes in climate variability. This will allow greater success in developing forecasts and understanding of longer term climate change impacts.
    AGU Fall Meeting Abstracts. 11/2008; -1:0576.
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    ABSTRACT: In image analysis, the concept of similarity has been widely explored and various measures of similarity, or of distance, have been proposed that yield a quantitative evaluation. There are cases, however, in which the evaluation of similarity should reproduce the judgment of a human observer based mainly on qualitative and, possibly, subjective appraisal of perceptual features. This process is best modeled as a cognitive process based on knowledge structures and inference strategies, able to incorporate the human reasoning mechanisms and to handle their inherent uncertainties. This articlea proposes a general strategy for similarity evaluation in image analysis considered as a cognitive process. A salient aspect is the use of fuzzy logic propositions to represent knowledge structures, and fuzzy reasoning to model inference mechanisms. Specific similarity evaluation procedures are presented that demonstrate how the same general strategy can be applied to different image analysis problems. © 1993 John Wily & Sons, Inc.
    International Journal of Intelligent Systems 03/2007; 8(7):749 - 769. · 1.42 Impact Factor
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    ABSTRACT: This study is aimed at demonstrating the feasibility of the MODIS Land Surface Temperature (LST) product as a source for calculating spatially distributed daily mean air temperature to be used as input for hydrological or environmental models. The test area is located in the Italian Alpine area. The proposed procedure solves, by empirical approaches, the problem of relating LST to the Air Temperature (Tair) and instantaneous Tair values to daily mean values, exploiting ground data weather station measurements as a reference. The relationship between LST and Tair is deter- mined by correlation analysis and equation generalisation for spatial distribution. The extrapolation of daily mean values of Tair from instantaneous values is addressed again by correlation analyses taking into account the altitude variability and exploiting historical series. Validation was accom- plished by accuracy assessment procedures both punctual and spatially distributed, the latter per- formed by comparison with the Inverse Distance Weighting (IDW) interpolation method. The proposed methodology produced satisfactory results as related to the objective: The daily mean air temperatures derived by LST showing an overall RMSE of 1.89°C, and slightly outper- forms the interpolation method used as comparison.
    01/2007;
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    ABSTRACT: The accuracy of snow mapping from satellite remote sensing is affected by several inconveniences -topography, cloud cover, patchy snow pack -which cause high scene variability and uncertainties in classification. When a hard classifier approach is adopted, the presence of mixed coverage pixels implies that the final product will have errors. Errors are strictly connected to the spatial resolution of the data, that rules the presence of mixed pixels within the scene. Theoretically, a soft classifier is not affected by such errors, but in remote sensing applications this has not been demonstrated. In this study the efficiencies of a fuzzy statistical classifier and of hard classification approaches have been compared adopting the 'Pareto Boundary of optimal solutions' as assessing method. Low and high spatial resolution satellite images acquired over two Alpine landscapes were used for the inter-comparison exercise. Results proved the soft classifier to outperform the traditional approaches.
    FUZZ-IEEE 2007, IEEE International Conference on Fuzzy Systems, Imperial College, London, UK, 23-26 July, 2007, Proceedings; 01/2007
  • E Binaghi, A Rampini
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    ABSTRACT: Knowledge acquisition is always a critical step in the development of a knowledge-based computing system. In the particular area of the interpretation of biomedical images, the assignement of meanings to image patterns is based on obscure and intrinsically vague criteria which are difficult to asses and transform into a suitable machine representation. Automatic learning tecniques may be a promising tool in addressing this problem. The paper illustrates a methodological procedure based on fuzzy set theory and using fuzzy logic for the automatic learning of classification rules for biomedical image interpretation systems. It also provides a detailed description of the application of the procedure in the development of a system for the automatic detection of preneoplastic and neoplastic lesions in colposcopic images. Plans to employ the system contemplate its use in educational applications, in diagnostic review for research purposes, and as an online support in clinical practice.
    04/2006: pages 434-443;
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    ABSTRACT: A general purpose knowledge-based system for biomedical image interpretation is presented. The system acquires knowledge directly from the experts by means of a user friendly dialogue. The knowledge introduced tailors the system to a particular biomedical application. Frame representation technique is used for the representation of descriptive knowledge and a fuzzy reasoning strategy, based on fuzzy production rules, is adopted to manipulate the certain and uncertain knowledge contained into Frame Slots and to deduce interpretations. A detailed description of the application of the system to the analysis of CT images of vertebrae for the quantity evaluation of the bone mineral content is provided.
    04/2006: pages 488-497;
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    ABSTRACT: The ENVISAT mission with a suite of high performance sensors offers some opportunities for mapping snow cover at regional and catchment scales. The spatial resolution of the Medium Resolution Imaging Spectrometer Instrument (MERIS) data and the spectral characteristics of the Advanced Along Track Scanning Radiometer (AATSR) data are suitable for these purposes. A new approach has been developed for the generation of snow cover products in Alpine regions, based on the combined use of ENVISAT optical data and topographic information. The Alpine region is selected as a test area to demonstrate the potential and the limitations of the novel approach. In particular, attention is focused on three regions of northern Italy (Valle d'Aosta, Piemonte, Lombardia). The first results obtained by the application of this new method to Earth Observation data will be presented and analysed.
    International Journal of Remote Sensing 01/2005; 26(21):4661-4667. · 1.36 Impact Factor
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    ABSTRACT: In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.
    Natural Hazards 04/2004; 32(1):135-154. · 1.64 Impact Factor
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    ABSTRACT: This paper is concerned with the problem of indexing remote sensing images. This kind of images has a semantics mainly related to the image spectral properties. For these reasons the spectral properties can be considered as effective image descriptors. The model proposed in this paper assumes that the image descriptors are spectral regions and their spectral signatures. By the application of a clustering algorithm each image is segmented into a set of spectral regions to be associated to basic (pre-defined) ground cover classes. To take into account the uncertainty that often affects the cluster labelling process the indexing model generates for each region and each reference class a possibility degree indicating the possibility that the region corresponds to that class. The uncertainty of this association is explicitly modelled, and allows the definition of a more flexible image representation with respect to a crisp approach.
    Image and Video Retrieval: Third International Conference, CIVR 2004, Dublin, Ireland, July 21-23, 2004. Proceedings; 01/2004
  • Inf. Process. Manage. 01/2003; 39:323-327.
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    ABSTRACT: This contribution aims to shortly describe a data structure for creating and managing archives of thematic maps derived by classifying remotely sensed images by soft techniques (soft maps). Unlike traditional models for managing spatial data, the main key feature for query formulation is time, thus improving the investigation on changes occurred in time ranges. The data structure originally extends time-based models to soft maps, thus promoting a retrieval by changes approach also in the monitoring of phenomena for which hard classifications are not sufficiently accurate nor complete.The data structure has been implemented in a system, which is also described, available today in a Windows version. It has been applied to the monitoring of variations of Alpine glaciers.
    Information Processing & Management 01/2003; · 0.82 Impact Factor