The impact of innovative and emerging technologies on the surveying activities

Mancini, Francesco; Piras, Marco; Ruotsalainen, Laura; Vacca, Giuseppina; Lingua, Andrea Maria

doi:10.1007/s12518-020-00302-x

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…In relation to the above, it may seem strange not to mention photogrammetric methods, which have revolutionized 3D documentation in cultural heritage [7,[18][19][20]. It is true that new developments in photogrammetry (SFM-structure from motion-, computer vision .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments

Maté-González

Pietra

Piras

2022

Sensors

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In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments

Maté-González

Pietra

Piras

2022

Sensors

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“…Besides being timeconsuming and often limited in the spatial scale, traditional data collection is strongly limited by currents and water depth (such as shallow mountain streams and deep waters) (Dietrich, 2017;Burgazzi et al, 2021). In such a scenario, RS has progressively complemented traditional methods, providing high-resolution information from laser scanning and optical sensors, which have been recently mounted on unmanned aerial vehicles (UAV) systems (Mancini et al, 2020;Vélez-Nicolás et al, 2021). UAVs, also known as unmanned aerial systems (UAS), remotely piloted aircraft systems (RPAS) or drones, have emerged as new means for environmental monitoring and management.…”

Section: Introductionmentioning

confidence: 99%

Laying the Foundation for an Artificial Neural Network for Photogrammetric Riverine Bathymetry

Belcore

Pietra

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths.

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Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

Davidović¹,

Kuzmić²,

Vasić³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

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The impact of innovative and emerging technologies on the surveying activities

Cited by 3 publications

References 7 publications

Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments

Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments

Laying the Foundation for an Artificial Neural Network for Photogrammetric Riverine Bathymetry

Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

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