Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.

Validation of a UAV-derived point cloud by semantic classification and comparison with TLS data / M. Franzini, V. Casella, P. Marchese, M. Marini, G. Della Porta, F. Felletti. - In: INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES. - ISSN 1682-1750. - 43:B2(2021), pp. 83-90. ((Intervento presentato al 24. convegno ISPRS Congress Commission II: Imaging Today, Foreseeing Tomorrow tenutosi a on line nel 2021 [10.5194/isprs-archives-XLIII-B2-2021-83-2021].

Validation of a UAV-derived point cloud by semantic classification and comparison with TLS data

M. Marini
Writing – Original Draft Preparation
;
G. Della Porta
Penultimo
Writing – Review & Editing
;
F. Felletti
Ultimo
Writing – Original Draft Preparation
2021

Abstract

Recent years showed a gradual transition from terrestrial to aerial survey thanks to the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient and can support outcrops surveying in many difficult situations such as inaccessible steep and high rock faces. The experiences acquired in terrestrial survey, with total stations, GNSS or terrestrial laser scanner (TLS), are not yet completely transferred to UAV acquisition. Hence, quality comparisons are still needed. The present paper is framed in this perspective aiming to evaluate the quality of the point clouds generated by an UAV in a geological context; data analysis was conducted comparing the UAV product with the homologous acquired with a TLS system. Exploiting modern semantic classification, based on eigenfeatures and support vector machine (SVM), the two point clouds were compared in terms of density and mutual distance. The UAV survey proves its usefulness in this situation with a uniform density distribution in the whole area and producing a point cloud with a quality comparable with the more traditional TLS systems.
TLS; UAV; Eigenfeatures; SVM; Classification; Validation; Accuracy; Precision
Settore GEO/02 - Geologia Stratigrafica e Sedimentologica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/905492
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