Road Infrastructure Heritage: From Scan to InfraBIM

Barrile, Vincenzo; Candela, Gabriele; Bilotta, Giuliana; Modafferi, A.; Fotia, Antonino

doi:10.37394/232015.2020.16.65

Cited by 16 publications

(2 citation statements)

References 20 publications

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“…Furthermore, surface models can be used not only for design/rehabilitation but also for vehicle simulations. There are multiple available methods to describe road surfaces (Barrile et al, 2020). CRG (Curved Regular Grid) models are considered the most used ones that support transportation applications (Poto et al, 2018).…”

Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Comparative analysis of Road Scanning Techniques

et al. 2023

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A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.

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Section: Introduction Introduction Introduction Introductionmentioning

confidence: 99%

Comparative analysis of Road Scanning Techniques

et al. 2023

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“…The MultiSpectral Instrument (MSI) of Sentinel 2 covers large areas, and many satellite programs (i.e., Landsat, Sentinel-1 and Sentinel-2) now freely supply datasets, which promotes the exploitation of satellite imagery for many applications, including agricultural applications, as multi-sensor and multiresolution data fusion [ 22 , 23 , 24 , 25 , 26 ]. The Sentinel-2 satellite, which was developed by the European Space Agency (ESA), has a resolution of one decametre, a revisitation time of six days, and an efficient resolution for analysing crop variability and conditions.…”

Section: Introductionmentioning

confidence: 99%

Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

Barrile

Simonetti

Citroni

et al. 2022

Sensors

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Geomatics is important for agriculture 4.0; in fact, it uses different types of data (remote sensing from satellites, Unmanned Aerial Vehicles-UAVs, GNSS, photogrammetry, laser scanners and other types of data) and therefore it uses data fusion techniques depending on the different applications to be carried out. This work aims to present on a study area concerning the integration of data acquired (using data fusion techniques) from remote sensing techniques, UAVs, autonomous driving machines and data fusion, all reprocessed and visualised in terms of results obtained through GIS (Geographic Information System). In this work we emphasize the importance of the integration of different methodologies and data fusion techniques, managing data of a different nature acquired with different methodologies to optimise vineyard cultivation and production. In particular, in this note we applied (focusing on a vineyard) geomatics-type methodologies developed in other works and integrated here to be used and optimised in order to make a contribution to agriculture 4.0. More specifically, we used the NDVI (Normalized Difference Vegetation Index) applied to multispectral satellite images and drone images (suitably combined) to identify the vigour of the plants. We then used an autonomous guided vehicle (equipped with sensors and monitoring systems) which, by estimating the optimal path, allows us to optimise fertilisation, irrigation, etc., by data fusion techniques using various types of sensors. Everything is visualised on a GIS to improve the management of the field according to its potential, also using historical data on the environmental, climatic and socioeconomic characteristics of the area. For this purpose, experiments of different types of Geomatics carried out individually on other application cases have been integrated into this work and are coordinated and integrated here in order to provide research/application cues for Agriculture 4.0.

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UAV for Precision Agriculture in Vineyards: A Case Study in Calabria

Bilotta

Bernardo

2022

Communications in Computer and Information Science

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Road Infrastructure Heritage: From Scan to InfraBIM

Cited by 16 publications

References 20 publications

Comparative analysis of Road Scanning Techniques

Comparative analysis of Road Scanning Techniques

Experimenting Agriculture 4.0 with Sensors: A Data Fusion Approach between Remote Sensing, UAVs and Self-Driving Tractors

UAV for Precision Agriculture in Vineyards: A Case Study in Calabria

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