0000000000749829

AUTHOR

Alessandra Capolupo

showing 2 related works from this author

Integration of terrestrial laser scanning and UAV-SFM technique to generate a detailed 3D textured model of a heritage building

2020

The digital twin is among the Top 10 of the strategic technological trends for the period 2007-2019, and it represents a powerful tool for the conservation and enhancement of cultural heritage. It reproduces with "precision" a physical asset, thus allowing to investigate its structure and to analyze the deformations that occur over the years. Various techniques have been introduced to obtain high-resolution 3D models. Among these, the Terrestrial Laser Scanner (TLS) is widely recognized as the gold standard to generate accurate 3D metric reconstructions. TLS allows acquiring a lot of data (point cloud) in a fast way, being not in physical contact with the objects of investigation. By integr…

Laser scanningComputer sciencebusiness.industryPoint cloudData fusionSensor fusion3D modeling3D modelingdigital photogrammetryData acquisitionPhotogrammetryMetric (mathematics)Terrestrial laser scannerCultural heritageStructure from motion3D modeling Cultural heritage Data fusion Digital photogrammetry Terrestrial laser scannerComputer visionData fusion Terrestrial laser scanner digital photogrammetry 3D modeling Cultural heritageArtificial intelligencebusinessSettore ICAR/06 - Topografia E Cartografia
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Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images

2020

The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…

PixelComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONObject basedLand coverClass (biology)Random forestObject-Based image analysisRemote sensing (archaeology)Computer Science::Computer Vision and Pattern RecognitionVector based generalizationHigh spatial resolutionObject-Based image analysis; Random forest; Vector based generalizationState (computer science)Settore ICAR/06 - Topografia E CartografiaRandom forestRemote sensing
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