Dr. Eufemia Tarantino Profile

Dr. Eufemia Tarantino

Assistant Professor in Geomatics Engineering at Politecnico di Bari

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Publications (12)

Proceedings Article | 28 October 2022 Presentation + Paper

Combining OBIA approach and machine learning algorithm to extract photovoltaic panels from Sentinel-2 images automatically

Claudio Ladisa, Alessandra Capolupo, Maria Nicolina Ripa, Eufemia Tarantino

Proceedings Volume 12262, 122620K (2022) https://doi.org/10.1117/12.2636451

KEYWORDS: Image segmentation, Photovoltaics, Solar energy, Satellites, Machine learning, Short wave infrared radiation, Satellite imaging

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Proceedings Article | 12 September 2021 Paper

Google Earth Engine for land surface albedo estimation: comparison among different algorithms

A. Capolupo, C. Monterisi, C. Barletta, E. Tarantino

Proceedings Volume 11856, 118560F (2021) https://doi.org/10.1117/12.2597666

KEYWORDS: Earth observing sensors, Landsat, Satellites, Algorithm development, Sensors, Satellite imaging, Error analysis, Data acquisition, Vegetation

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Proceedings Article | 26 August 2020 Paper

Cloud-to-cloud assessment of UAV and TLS 3D reconstructions of cultural heritage monuments: the case of Torre Zozzoli

Mirko Saponaro, Alessandra Capolupo, Adriano Turso, Eufemia Tarantino

Proceedings Volume 11524, 1152408 (2020) https://doi.org/10.1117/12.2570771

KEYWORDS: Clouds, Unmanned aerial vehicles, 3D modeling, Georeferencing, Satellite navigation systems, Buildings, 3D acquisition, Cameras, Error analysis, Cultural heritage

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Proceedings Article | 26 August 2020 Paper

Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform

Alessandra Capolupo, Cristina Monterisi, Mirko Saponaro, Eufemia Tarantino

Proceedings Volume 11524, 1152419 (2020) https://doi.org/10.1117/12.2571228

KEYWORDS: Earth observing sensors, Landsat, Vegetation, Mining, Image classification, Satellites, Image processing, Satellite imaging, Clouds, Data processing

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Proceedings Article | 6 August 2018 Paper

Crop water requirements estimation at irrigation district scale from remote sensing: a comparison between MODIS ET product and the analytical approach

Giuseppe Peschechera, Eufemia Tarantino, Umberto Fratino

Proceedings Volume 10773, 1077318 (2018) https://doi.org/10.1117/12.2326147

KEYWORDS: Water, MODIS, Remote sensing, Vegetation, Data modeling, Climatology, Satellites, Spatial resolution, Statistical analysis, Sensors

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Remote sensing provides reliable information for the quantification of evapotranspiration (ET) over large areas, essential for water management and irrigation scheduling. The ET represents the Crop Water Requirements (CWR) that must be provided by rainfall and/or irrigation to ensure the crop yield. During last decades different ET estimation methods were developed according to problem-specific requirements, characteristics of data input (e.g. data accuracy, availability and resolution) and temporal and spatial scale of interest. The selection of the best methodology has a great influence on the results of ET estimation. Generally, the comparison of ET estimated trough different methods is affected by many parameters: data input (different sources, typology, temporal and spatial resolutions), different scales of analysis (from field to global scale), contests, crop type and climate condition. For this reason, defining whether algorithm can capture spatial and temporal pattern of ET at the required accuracy is a significant challenge. In this study two different methods, both based on the logic of the Penman-Monteith equation, were tested for ET trends estimation at irrigation district scale: the improved algorithm of the MODIS ET product (MYD16A2 V006) and the “Analytical Approach”. While the MODIS product follows the energy balance method, the Analytical Approach exploits the single crop coefficient (Kc) approach proposed by the FAO in Irrigation and Drainage Paper No. 56. It combines agrometeorological data measured in situ and surface reflectance satellite derived data: the albedo (α) of the crop-soil surface and the Leaf Area Index (LAI). In order to compare the two ET trends, the satellite data input used in the present work were chosen from the MODIS products: MODIS LAI (MCD15A2H V006) and MODIS Albedo (MCD43A3 V006). The comparison was assessed in the study area of “Sinistra Ofanto” Irrigation district located in the Apulia Region (Italy) and characterized by an extremely heterogeneous and fragmented landscape.

Proceedings Article | 12 August 2016 Paper

Evaluation of relative radiometric correction techniques on Landsat 8 OLI sensor data

Antonio Novelli, Grazia Caradonna, Eufemia Tarantino

Proceedings Volume 9688, 968808 (2016) https://doi.org/10.1117/12.2241002

KEYWORDS: Radiometric corrections, Landsat, Sensors, Earth observing sensors, Calibration, Data acquisition, Data processing, Data modeling, Reflectivity, Detection and tracking algorithms

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Proceedings Article | 19 June 2015 Paper

Testing high spatial resolution WorldView-2 imagery for retrieving the leaf area index

Eufemia Tarantino, Antonio Novelli, Maurizio Laterza, Andrea Gioia

Proceedings Volume 9535, 95351N (2015) https://doi.org/10.1117/12.2192561

KEYWORDS: Near infrared, Vegetation, Spatial resolution, Data modeling, Reflectivity, Satellites, Soil science, Agriculture, Image retrieval, Sensors

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Proceedings Article | 19 June 2015 Paper

The contribution of Landsat 8 TIRS sensor data to the identification of plastic covered vineyards

Antonio Novelli, Eufemia Tarantino

Proceedings Volume 9535, 95351E (2015) https://doi.org/10.1117/12.2192095

KEYWORDS: Landsat, Remote sensing, Agriculture, Sensors, Earth observing sensors, Neodymium, Satellites, Environmental sensing, Clouds, Terbium

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Proceedings Article | 21 October 2014 Paper

Evaluating the potential of GeoEye data in retrieving LAI at watershed scale

Mariella Aquilino, Antonio Novelli, Eufemia Tarantino, Vito Iacobellis, Francesco Gentile

Proceedings Volume 9239, 92392B (2014) https://doi.org/10.1117/12.2067185

KEYWORDS: Data modeling, Satellites, Vegetation, Sensors, Reflectivity, Data acquisition, Near infrared, Performance modeling, Remote sensing, Radiometric corrections

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Proceedings Article | 19 October 2012 Paper

An object-based method for mapping ephemeral river areas from WorldView-2 satellite data

B. Figorito, E. Tarantino, G. Balacco, U. Fratino

Proceedings Volume 8531, 85310B (2012) https://doi.org/10.1117/12.974689

KEYWORDS: Image segmentation, Satellites, Sensors, Roads, Spatial resolution, Associative arrays, Floods, Image classification, Data modeling, Performance modeling

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Proceedings Article | 7 October 2011 Paper

Plastic covered vineyard extraction from airborne sensor data with an object-oriented approach

E. Tarantino, A. Aiello

Proceedings Volume 8174, 817415 (2011) https://doi.org/10.1117/12.898175

KEYWORDS: Image segmentation, Image classification, Vegetation, Agriculture, Orthophoto maps, Sensors, Spatial resolution, Image analysis, Visualization, Geographic information systems

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Proceedings Article | 4 November 2010 Paper

Features extraction from multi-date ASTER imagery using a hybrid classification method for land cover transformations

Eufemia Tarantino

Proceedings Volume 7840, 78401T (2010) https://doi.org/10.1117/12.872959

KEYWORDS: Vegetation, Image classification, Image segmentation, Agriculture, Data acquisition, Feature extraction, Atmospheric sensing, Sensors, Satellites, Image analysis

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Showing 5 of 12 publications

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