This paper presents the results of the first attempt to assess, identify and quantify the residual number of shell craters of World War I currently present in the Vezzena/Luserna/Lavarone Plateau, areas of Millegrobbe, Bisele and Cima Campo (Province of Trento, Italy). Historical sources report the existence of several thousand artillery explosions: therefore, a field survey or a classic photo-interpretation would be labour-intensive and highly time-consuming. For this reason, a digital terrain model (DTM) of the test-site was processed using the Sky-view Factor algorithm and was analysed with an object-based approach, which implied: (1) multiresolution segmentation; (2) classification (main features considered size, shape and colour). The automatically classified shell craters were thus verified during an in situ survey that determined the accuracy of the method in the order of 84% of the total occurrences.

Object-based Shell Craters Classification from LiDAR-derived Sky-view Factor

MAGNINI, LUIGI
Methodology
;
BETTINESCHI, CINZIA
Writing – Original Draft Preparation
;
DE GUIO, ARMANDO
Supervision
2017

Abstract

This paper presents the results of the first attempt to assess, identify and quantify the residual number of shell craters of World War I currently present in the Vezzena/Luserna/Lavarone Plateau, areas of Millegrobbe, Bisele and Cima Campo (Province of Trento, Italy). Historical sources report the existence of several thousand artillery explosions: therefore, a field survey or a classic photo-interpretation would be labour-intensive and highly time-consuming. For this reason, a digital terrain model (DTM) of the test-site was processed using the Sky-view Factor algorithm and was analysed with an object-based approach, which implied: (1) multiresolution segmentation; (2) classification (main features considered size, shape and colour). The automatically classified shell craters were thus verified during an in situ survey that determined the accuracy of the method in the order of 84% of the total occurrences.
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3219764
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