Ferrara, Italy
Ferrara, Italy
Time filter
Source Type

Baumann P.,Jacobs University Bremen | Baumann P.,Rasdaman GmbH | Mazzetti P.,CNR Institute of Atmospheric Pollution Research | Ungar J.,EOX IT Services GmbH | And 36 more authors.
International Journal of Digital Earth | Year: 2016

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains. © 2015 Taylor & Francis.

Nguyen T.N.T.,Hanoi University of Science and Technology | Ta V.C.,Hanoi University of Science and Technology | Le T.H.,Hanoi University of Science and Technology | Mantovani S.,MEEO S.r.l | Mantovani S.,SISTEMA GmbH
Advances in Intelligent Systems and Computing | Year: 2014

Estimation of Particulate Matter concentration (PM1, PM2.5 and PM10) from aerosol product derived from satellite images and meteorological parameters brings a great advantage in air pollution monitoring since observation range is no longer limited around ground stations and estimation accuracy will be increased significantly. In this article, we investigate the application of Multiple Linear Regression (MLR) and Support Vector Regression (SVR) to make empirical data models for PM1/2.5/10 estimation from satellite- and ground-based data. Experiments, which are carried out on data recorded in two year over Hanoi - Vietnam, not only indicate a case study of regional modeling but also present comparison of performance between a widely used technique (MLR) and an advanced method (SVR). © Springer International Publishing Switzerland 2014.

Hirtl M.,ZAMG Central Institute for Meteorology and Geodynamics | Mantovani S.,SISTEMA GmbH | Kruger B.C.,University of Natural Resources and Life Sciences, Vienna | Triebnig G.,EOX IT Services GmbH | And 3 more authors.
Atmospheric Environment | Year: 2014

Daily regional scale forecasts of particulate air pollution are simulated for public information and warning. An increasing amount of air pollution measurements is available in real-time from ground stations as well as from satellite observations. In this paper, the Support Vector Regression technique is applied to derive highly-resolved PM10 initial fields for air quality modeling from satellite measurements of the Aerosol Optical Thickness.Additionally, PM10-ground measurements are assimilated using optimum interpolation. The performance of both approaches is shown for a selected PM10 episode. © 2013 Elsevier Ltd.

Campalani P.,UNIFE | Campalani P.,MEEO Srl | Nguyen T.N.T.,UNIFE | Nguyen T.N.T.,MEEO Srl | And 3 more authors.
IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011 | Year: 2011

Daily monitoring of unhealthy particles suspended in the low troposphere is of major concern around the world, and ground-based measuring stations represent a reliable but still inadequate means for a full spatial coverage assessment. Advances in satellite sensors have provided new datasets and though less precise than insitu observations, they can be combined altogether to enhance the prediction of particulate matter. In this article we evaluate a methodology for automatic multi-variate estimation of PM 10 dry mass concentrations along with a comparison of three different cokriging estimators, which integrate ground measurements of PM 10, satellite MODIS-derived retrievals of aerosols optical thickness and further auxiliary data. Results highlight the need for further improvements and studies. The analysis employs the available data in 2007 over the Emilia Romagna region (Padana Plain, Northern Italy), where stagnant meteorological conditions further urge for a comprehensive air quality monitoring. Qualitative PM 10 full maps of Emilia Romagna are then automatically yielded on-line in a dynamic GIS environment for multi-temporal analysis on air quality. © 2011 IEEE.

Nguyen T.N.T.,University of Ferrara | Nguyen T.N.T.,MEEO S.r.l | Mantovani S.,MEEO S.r.l | Mantovani S.,SISTEMA GmbH | And 2 more authors.
IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011 | Year: 2011

As a result of great improvements in satellite technologies, satellite-based observations have provided possibilities to monitor air pollution at the global scale with moderate quality in comparison with ground truth measurement. In tradition, the inversion process that derives atmospheric parameters from satellite-based data is replied on simulated physics models of matter interactions. Recently, the usage of machine learning techniques in this field has been investigated and presented competitive results to the physical approach. In this paper, we present validation of Support Vector Regression (SVR) technique in estimating Aerosol Optical Thickness (AOT), one of the most important atmospheric variables, from satellite observations at 1x1 km2 of spatial resolution. Validation by different European countries is carried out on a large amount of datasets collected in three years, which aims at investigating prediction quality of SVR data models built up on discrete and sparse data around ground measurement sites on continuous data domain presented by maps. The validation results obtained from 172 datasets showed good performance of SVR over most of the 31 countries that were considered. © 2011 IEEE.

Natali S.,MEEO Srl | Beccati A.,MEEO Srl | Beccati A.,University of Ferrara | D'Elia S.,Earth Observation Directorate | And 4 more authors.
2011 6th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Multi-Temp 2011 - Proceedings | Year: 2011

The development of new technologies and tools for as-much-as-possible automatic multi-temporal data analysis has been a goal for most of the institutions that aim at promoting the use of satellite data in different application domains. In the framework of the Support by Pre-classification to specific Applications Project, started in 2008, the European Space Agency has requested the development of a specific platform, named Multi-sensor Evolution Analysis (MEA), with the scope of demonstrating that long term satellite datasets coming from different sensors can be accessed and exploited in almost real time (few seconds) from a web application as user interface. The MEA system has been implemented based on 15 years of global (A)ATSR data (1 km resolution), together with 5 years of regional AVNIR-2 data (10 m resolution), with the final aim of permitting on-the-fly Land Use / Land Cover Change analysis. Moreover, a modified version of MEA has been set-up to permit the multi-temporal analysis of pollution maps coming from satellite observations and ground measurements, demonstrating the generality of the pursued approach. The present work aims at introducing the basis of the MEA system, describing the two implementations for land cover and pollution multi-temporal analysis, including external validation activities being performed for the first application by third parties. © 2011 IEEE.

Thanh Nguyen T.N.,University of Ferrara | Thanh Nguyen T.N.,MEEO S.r.l. | Mantovani S.,MEEO S.r.l. | Mantovani S.,SISTEMA GmbH | Bottoni M.,SISTEMA GmbH
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

In the frame of the Remote Sensing applications applied to MODIS data collected by the polar orbiting satellites Terra and Aqua, operated by the NASA, we present PM MAPPER, a novel data processing system developed for air pollution monitoring. Our system has derived from the MODIS data an updated set of information consisting of AOT, PM2.5/10, AQI, and surface information with increased spatial resolutions up to 3×3 km2. With the fine spatial resolution and augmented background information, the software is effective in monitoring air pollution at local scale, especially over small urban areas with complicated topography. We carried out a validation on the data set covering Italy in a period of six months to evaluate the system's performance. The validation outcomes show that our results have good quality in comparison with MODIS standard products and a higher capacity in retrieving AOT information over land areas, especially coastlines where are nearly empty in the MODIS products. Besides, integrated surface information could be useful for further improvements of aerosol derivation.

Nguyen T.N.T.,University of Ferrara | Nguyen T.N.T.,MEEO S.r.l | Mantovani S.,MEEO S.r.l | Mantovani S.,SISTEMA GmbH | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Processing of data recorded by the MODIS sensors on board the Terra and Aqua satellites has provided AOT maps that in some cases show low correlations with ground-based data recorded by the AERONET. Application of SVR techniques to MODIS data is a promising, though yet poorly explored, method of enhancing the correlations between satellite data and ground measurements. The article explains how satellite data recorded over three years on central Europe are correlated in space and time with ground based data and then shows results of the application of the SVR technique which somewhat improves previously computed correlations. Hints about future work in testing different SVR variants and methodologies are inferred from the analysis of the results thus far obtained. © 2010 Springer-Verlag.

Thanh Nguyen T.N.,University of Ferrara | Mantovani S.,MEEO S.r.l. | Mantovani S.,SISTEMA GmbH | Campalani P.,University of Ferrara | Limone G.P.,University of Ferrara
ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods | Year: 2012

Processing of data recorded by MODIS sensors on board the polar orbiting satellite Terra and Aqua usually provides Aerosol Optical Thickness maps at a coarse spatial resolution. It is appropriate for applications of air pollution monitoring at the global scale but not adequate enough for monitoring at local scales. Different from the traditional approach based on physical algorithms to downscale the spatial resolution, in this article, we propose a methodology to derive AOT maps over land at 1 km 2 of spatial resolution from MODIS data using support vector regression relied on domain knowledge. Experiments carried out on data recorded in three years over Europe areas show promising results on limited areas located around ground measurement sites where data are collected to make empirical data models as well as on large areas over satellite maps.

Beccati A.,MEEO srl | Beccati A.,University of Ferrara | Folegani M.,MEEO srl | D'Elia S.,Earth Observation Directorate | And 3 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

Providing land use/land cover change maps through the use of satellite imagery is very challenging and demanding in terms of human interaction, mainly because of limited process automation. One main cause is that most of land use/land cover change applications require multi-temporal acquisitions over the same area, that introduces the need for accurate pre-processing of the dataset, in both geo-referencing and radiometry. Moreover, single multi-spectral images can be hundred of megabytes in size and therefore image time series are even more difficult to be handled and processed in real time. The approach here proposed foresees the use of a robust land cover classification system named SOIL MAPPER® to reduce input data size by assigning a semantic meaning (in the land cover domain) to each pixel of a single image. Land cover transitions and land use maps can be expressed as evolutions of land cover classes (features) on temporal domain. This permits to define 'trajectories' in the features - Time space, that define specific transition or periodic behaviour. The target system, named Land Classification System, provides fully automatic and real time land use/land cover change analysis and includes fundamental sub-systems for accurate radiometric calibration, accurate geo-referencing (with geolocation within the pixel size) and accurate remapping onto an Earth fixed grid. The characteristics of the selected pre-classification system and Earth fixed grid allow general application across different sensors. Land Classification System has been prototyped over 15 years of global (A)ATSR data and foresees integration of over 3 years of regional ALOS-AVNIR-2 data.

Loading MEEO Srl collaborators
Loading MEEO Srl collaborators