Showing papers on "Photogrammetry published in 2019"

Unmanned Aerial Vehicle for Remote Sensing Applications—A Review

Abstract: The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in almost every application (e.g., agriculture, forestry, and mining) that needs observed information from the top or oblique views. While they intend to be a general remote sensing (RS) tool, the relevant RS data processing and analysis methods are still largely ad-hoc to applications. Although the obvious advantages of UAV data are their high spatial resolution and flexibility in acquisition and sensor integration, there is in general a lack of systematic analysis on how these characteristics alter solutions for typical RS tasks such as land-cover classification, change detection, and thematic mapping. For instance, the ultra-high-resolution data (less than 10 cm of Ground Sampling Distance (GSD)) bring more unwanted classes of objects (e.g., pedestrian and cars) in land-cover classification; the often available 3D data generated from photogrammetric images call for more advanced techniques for geometric and spectral analysis. In this paper, we perform a critical review on RS tasks that involve UAV data and their derived products as their main sources including raw perspective images, digital surface models, and orthophotos. In particular, we focus on solutions that address the “new” aspects of the UAV data including (1) ultra-high resolution; (2) availability of coherent geometric and spectral data; and (3) capability of simultaneously using multi-sensor data for fusion. Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical potentials, we share our views in their future research directions and draw conclusive remarks.

Structure from Motion Photogrammetry in Forestry: a Review

Abstract: The adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers. Our examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels. We highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys.

Guidelines on the use of Structure from Motion Photogrammetry in Geomorphic Research

Abstract: As a topographic modelling technique, structure-from-motion (SfM) photogrammetry combines the utility of digital photogrammetry with a flexibility and ease of use derived from multi-view computer vision methods. In conjunction with the rapidly increasing availability of imagery, particularly from unmanned aerial vehicles, SfM photogrammetry represents a powerful tool for geomorphological research. However, to fully realize this potential, its application must be carefully underpinned by photogrammetric considerations, surveys should be reported in sufficient detail to be repeatable (if practical) and results appropriately assessed to understand fully the potential errors involved. To deliver these goals, robust survey and reporting must be supported through (i) using appropriate survey design, (ii) applying suitable statistics to identify systematic error (bias) and to estimate precision within results, and (iii) propagating uncertainty estimates into the final data products.

Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data.

Abstract: Flooding is one of the leading threats of natural disasters to human life and property, especially in densely populated urban areas. Rapid and precise extraction of the flooded areas is key to supporting emergency-response planning and providing damage assessment in both spatial and temporal measurements. Unmanned Aerial Vehicles (UAV) technology has recently been recognized as an efficient photogrammetry data acquisition platform to quickly deliver high-resolution imagery because of its cost-effectiveness, ability to fly at lower altitudes, and ability to enter a hazardous area. Different image classification methods including SVM (Support Vector Machine) have been used for flood extent mapping. In recent years, there has been a significant improvement in remote sensing image classification using Convolutional Neural Networks (CNNs). CNNs have demonstrated excellent performance on various tasks including image classification, feature extraction, and segmentation. CNNs can learn features automatically from large datasets through the organization of multi-layers of neurons and have the ability to implement nonlinear decision functions. This study investigates the potential of CNN approaches to extract flooded areas from UAV imagery. A VGG-based fully convolutional network (FCN-16s) was used in this research. The model was fine-tuned and a k-fold cross-validation was applied to estimate the performance of the model on the new UAV imagery dataset. This approach allowed FCN-16s to be trained on the datasets that contained only one hundred training samples, and resulted in a highly accurate classification. Confusion matrix was calculated to estimate the accuracy of the proposed method. The image segmentation results obtained from FCN-16s were compared from the results obtained from FCN-8s, FCN-32s and SVMs. Experimental results showed that the FCNs could extract flooded areas precisely from UAV images compared to the traditional classifiers such as SVMs. The classification accuracy achieved by FCN-16s, FCN-8s, FCN-32s, and SVM for the water class was 97.52%, 97.8%, 94.20% and 89%, respectively.

Three-Dimensional Digital Documentation of Cultural Heritage Site Based on the Convergence of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry

Abstract: Three-dimensional digital technology is important in the maintenance and monitoring of cultural heritage sites. This study focuses on using a combination of terrestrial laser scanning and unmanned aerial vehicle (UAV) photogrammetry to establish a three-dimensional model and the associated digital documentation of the Magoksa Temple, Republic of Korea. Herein, terrestrial laser scanning and UAV photogrammetry was used to acquire the perpendicular geometry of the buildings and sites, where UAV photogrammetry yielded higher planar data acquisition rate in upper zones, such as the roof of a building, than terrestrial laser scanning. On comparing the two technologies’ accuracy based on their ground control points, laser scanning was observed to provide higher positional accuracy than photogrammetry. The overall discrepancy between the two technologies was found to be sufficient for the generation of convergent data. Thus, the terrestrial laser scanning and UAV photogrammetry data were aligned and merged post conversion into compatible extensions. A three-dimensional (3D) model, with planar and perpendicular geometries, based on the hybrid data-point cloud was developed. This study demonstrates the potential for using the integration of terrestrial laser scanning and UAV photogrammetry in 3D digital documentation and spatial analysis of cultural heritage sites.

UAV Bridge Inspection through Evaluated 3D Reconstructions

Abstract: 10 Imagery-based, three-dimensional (3D) reconstruction from Unmanned Aerial Vehicles (UAVs) 11 holds the potential to provide safer, more economical, and less disruptive bridge inspection. In 12 support of those efforts, this paper proposes a process using an imagery-based point cloud. First, 13 a bridge inspection procedure is introduced, including data acquisition, 3D reconstruction, data 14 quality evaluation, and subsequent damage detection. Next, evaluation mechanisms are proposed 15 including checking data coverage, analysing points distribution, assessing outlier noise, and 16 measuring geometric accuracy. In this final aspect, the “Guide to the Expression of Uncertainty 17 in Measurement” was used. The overall approach is illustrated in the form of a case study with a 18 low-cost UAV. Areas of particular coverage difficulty involved slim features such as railings, 19 where obtaining sufficient features for image matching proved challenging. Shadowing and large 20 tilt angles hid or weakened texturing surfaces, which also interfered with the matching process. 21 22 23

Evaluating the potential of post-processing kinematic (PPK) georeferencing for UAV-based structure- from-motion (SfM) photogrammetry and surface change detection

Abstract: . Images captured by unmanned aerial vehicles (UAVs) and processed by structure-from-motion (SfM) photogrammetry are increasingly used in geomorphology to obtain high-resolution topography data. Conventional georeferencing using ground control points (GCPs) provides reliable positioning, but the geometrical accuracy critically depends on the number and spatial layout of the GCPs. This limits the time and cost effectiveness. Direct georeferencing of the UAV images with differential GNSS, such as PPK (post-processing kinematic), may overcome these limitations by providing accurate and directly georeferenced surveys. To investigate the positional accuracy, repeatability and reproducibility of digital surface models (DSMs) generated by a UAV–PPK–SfM workflow, we carried out multiple flight missions with two different camera–UAV systems: a small-form low-cost micro-UAV equipped with a high field of view (FOV) action camera and a professional UAV equipped with a digital single lens reflex (DSLR) camera. Our analysis showed that the PPK solution provides the same accuracy (MAE: ca. 0.02 m, RMSE: ca. 0.03 m) as the GCP method for both UAV systems. Our study demonstrated that a UAV–PPK–SfM workflow can provide consistent, repeatable 4-D data with an accuracy of a few centimeters. However, a few flights showed vertical bias and this could be corrected using one single GCP. We further evaluated different methods to estimate DSM uncertainty and show that this has a large impact on centimeter-level topographical change detection. The DSM reconstruction and surface change detection based on a DSLR and action camera were reproducible: the main difference lies in the level of detail of the surface representations. The PPK–SfM workflow in the context of 4-D Earth surface monitoring should be considered an efficient tool to monitor geomorphic processes accurately and quickly at a very high spatial and temporal resolution.

Height estimation from single aerial images using a deep convolutional encoder-decoder network

Abstract: Extracting 3D information from aerial images is an important and still challenging topic in photogrammetry and remote sensing. Height estimation from only a single aerial image is an ambiguous and ill-posed problem. To address this challenging problem, in this paper, an architecture based on a deep convolutional neural network (CNN) is proposed in order to estimate the height values from a single aerial image. Methodologies for data preprocessing, selection of training data as well as data augmentation are presented. Subsequently, a deep CNN architecture is proposed consisting of encoding and decoding steps. In the encoding part, a deep residual learning is employed for extracting the local and global features. An up-sampling approach is proposed in the decoding part for increasing the output resolution and skip connections are employed in each scale to modify the estimated height values at the object boundaries. Finally, a post-processing approach is proposed to merge the predicted height image patches and generate a seamless continuous height map. The quantitative evaluation of the proposed approaches on the ISPRS datasets indicates relative and root mean square errors of approximately 0.9 m and 3.2 m, respectively.

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Abstract: The past few decades have witnessed great progress for unmanned aerial vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with manned aircraft and satellites, UAVs have many promising characteristicsmflexibility, efficiency, high spatial/temporal resolution, low cost, easy operation, and so forthmthat make them an effective complement to the other two platforms and a cost-effective means for remote sensing.

Terrestrial structure from motion photogrammetry for deriving forest inventory data

Abstract: The measurements of tree attributes required for forest monitoring and management planning, e.g., National Forest Inventories, are derived by rather time-consuming field measurements on sample plots, using calipers and measurement tapes. Therefore, forest managers and researchers are looking for alternative methods. Currently, terrestrial laser scanning (TLS) is the remote sensing method that provides the most accurate point clouds at the plot-level to derive these attributes from. However, the demand for even more efficient and effective solutions triggers further developments to lower the acquisition time, costs, and the expertise needed to acquire and process 3D point clouds, while maintaining the quality of extracted tree parameters. In this context, photogrammetry is considered a potential solution. Despite a variety of studies, much uncertainty still exists about the quality of photogrammetry-based methods for deriving plot-level forest attributes in natural forests. Therefore, the overall goal of this study is to evaluate the competitiveness of terrestrial photogrammetry based on structure from motion (SfM) and dense image matching for deriving tree positions, diameters at breast height (DBHs), and stem curves of forest plots by means of a consumer grade camera. We define an image capture method and we assess the accuracy of the photogrammetric results on four forest plots located in Austria and Slovakia, two in each country, selected to cover a wide range of conditions such as terrain slope, undergrowth vegetation, and tree density, age, and species. For each forest plot, the reference data of the forest parameters were obtained by conducting field surveys and TLS measurements almost simultaneously with the photogrammetric acquisitions. The TLS data were also used to estimate the accuracy of the photogrammetric ground height, which is a necessary product to derive DBHs and tree heights. For each plot, we automatically derived tree counts, tree positions, DBHs, and part of the stem curve from both TLS and SfM using a software developed at TU Wien (Forest Analysis and Inventory Tool, FAIT), and the results were compared. The images were oriented with errors of a few millimetres only, according to checkpoint residuals. The automatic tree detection rate for the SfM reconstruction ranges between 65% and 98%, where the missing trees have average DBHs of less than 12 cm. For each plot, the mean error of SfM and TLS DBH estimates is −1.13 cm and −0.77 cm with respect to the caliper measurements. The resulting stem curves show that the mean differences between SfM and TLS stem diameters is at maximum −2.45 cm up to 3 m above ground, which increases to almost +4 cm for higher elevations. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry, is an accurate solution to support forest inventory for estimating the number of trees and their location, the DBHs and stem curve up to 3 m above ground.

High-accuracy UAV photogrammetry of ice sheet dynamics with no ground control

Abstract: . Unmanned aerial vehicles (UAVs) and structure from motion with multi-view stereo (SfM–MVS) photogrammetry are increasingly common tools for geoscience applications, but final product accuracy can be significantly diminished in the absence of a dense and well-distributed network of ground control points (GCPs). This is problematic in inaccessible or hazardous field environments, including highly crevassed glaciers, where implementing suitable GCP networks would be logistically difficult if not impossible. To overcome this challenge, we present an alternative geolocation approach known as GNSS-supported aerial triangulation (GNSS-AT). Here, an on-board carrier-phase GNSS receiver is used to determine the location of photo acquisitions using kinematic differential carrier-phase positioning. The camera positions can be used as the geospatial input to the photogrammetry process. We describe the implementation of this method in a low-cost, custom-built UAV and apply the method in a glaciological setting at Store Glacier in western Greenland. We validate the technique at the calving front, achieving topographic uncertainties of ±0.12 m horizontally ( ∼ 1.1 × the ground sampling distance) and ±0.14 m vertically ( ∼ 1.3 × the ground sampling distance), when flying at an altitude of ∼ 450 m above ground level. This compares favourably with previous GCP-derived uncertainties in glacial environments and allows us to apply the SfM–MVS photogrammetry at an inland study site where ice flows at 2 m day −1 and stable ground control is not available. Here, we were able to produce, without the use of GCPs, the first UAV-derived velocity fields of an ice sheet interior. Given the growing use of UAVs and SfM–MVS in glaciology and the geosciences, GNSS-AT will be of interest to those wishing to use UAV photogrammetry to obtain high-precision measurements of topographic change in contexts where GCP collection is logistically constrained.

Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Abstract: We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

Abstract: Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

Development and Optimization of an Automated Fixed-Location Time Lapse Photogrammetric Rock Slope Monitoring System

Abstract: An automated, fixed-location, time lapse camera system was developed as an alternative to monitoring geological processes with lidar or ground-based interferometric synthetic-aperture radar (GB-InSAR). The camera system was designed to detect fragmental rockfalls and pre-failure deformation at rock slopes. It was implemented at a site along interstate I70 near Idaho Springs, Colorado. The camera system consists of five digital single-lens reflex (DSLR) cameras which collect photographs of the rock slope daily and automatically upload them to a server for processing. Structure from motion (SfM) photogrammetry workflows were optimized to be used without ground control. An automated change detection pipeline registers the point clouds with scale adjustment and filters vegetation. The results show that if a fixed pre-calibration of internal camera parameters is used, an accuracy close to that obtained using ground control points can be achieved. Over the study period between March 19, 2018 and June 24, 2019, a level of detection between 0.02 to 0.03 m was consistently achieved, and over 50 rockfalls between 0.003 to 0.1 m3 were detected at the study site. The design of the system is fit for purpose in terms of its ground resolution size and accuracy and can be adapted to monitor a wide range of geological and geomorphic processes at a variety of time scales.

UAV Photogrammetry-Based 3D Road Distress Detection

Abstract: The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.

To 3D or Not 3D: Choosing a Photogrammetry Workflow for Cultural Heritage Groups

Abstract: The 3D reconstruction of real-world heritage objects using either a laser scanner or 3D modelling software is typically expensive and requires a high level of expertise. Image-based 3D modelling software, on the other hand, offers a cheaper alternative, which can handle this task with relative ease. There also exists free and open source (FOSS) software, with the potential to deliver quality data for heritage documentation purposes. However, contemporary academic discourse seldom presents survey-based feature lists or a critical inspection of potential production pipelines, nor typically provides direction and guidance for non-experts who are interested in learning, developing and sharing 3D content on a restricted budget. To address the above issues, a set of FOSS were studied based on their offered features, workflow, 3D processing time and accuracy. Two datasets have been used to compare and evaluate the FOSS applications based on the point clouds they produced. The average deviation to ground truth data produced by a commercial software application (Metashape, formerly called PhotoScan) was used and measured with CloudCompare software. 3D reconstructions generated from FOSS produce promising results, with significant accuracy, and are easy to use. We believe this investigation will help non-expert users to understand the photogrammetry and select the most suitable software for producing image-based 3D models at low cost for visualisation and presentation purposes.

End-to-End DSM Fusion Networks for Semantic Segmentation in High-Resolution Aerial Images

Abstract: Semantic segmentation in high-resolution aerial images is a fundamental research problem in remote sensing field for its wide range of applications. However, it is difficult to distinguish regions with similar spectral features using only multispectral data. Recent research studies have indicated that the introduction of multisource information can effectively improve the robustness of segmentation method. In this letter, we use digital surface models (DSMs) information as a complementary feature to further improve the semantic segmentation results. To this end, we propose a lightweight and simple DSM fusion (DSMF) branch structure module. Compared with the existing feature extraction structures, proposed DSMF module is simple and can be easily applied to other networks. In addition, we investigate four fusion strategies based on DSMF module to explore the optimal feature fusion strategy and four end-to-end DSMFNets are designed according to the corresponding strategies. We evaluate our models on International Society for Photogrammetry and Remote Sensing Vaihingen data set and all DSMFNets achieve promising results. In particular, DSMFNet-1 achieves an overall accuracy of 91.5% on the test data set.

Reliability of a Virtual Prosthodontic Project Realized through a 2D and 3D Photographic Acquisition: An Experimental Study on the Accuracy of Different Digital Systems

Abstract: Aims: The study aims to assess the accuracy of digital planning in dentistry, evaluating the characteristics of different intraoral 3D scanners and comparing it with traditional imaging 2D recording methods. Specifically, using computer aided design (CAD) software and measuring inside CAD software, authors want to verify the reliability of different models obtained with different techniques and machines. Methods: 12 patients that needed aesthetic restorative treatment were enrolled in the study. All the patients underwent recording data of the height and width dental elements 1.1, 1.2, and 1.3 size using different technologies and comparing 2D with 3D methods. A T test was then applied in order to verify whether there was a statistically significant difference between the measurements obtained, comparing the different tools data (Emerald, TRIOS, Photogrammetry and DSS (Digital Smile System)) with the reference values. Results: No significant differences emerged in the measurements made with the different scanners (Trios 3Shape ®, Planmeca Emerald ®) and photogrammetry. Therefore, what should be underlined regarding the 2D measurements is the speed and simplicity compared to all 3D techniques, so this work can help to better define the field of application and the limits connected to 2D techniques, giving a good window of the technique. Conclusions: The low number of patients is not sufficient to provide statistically significant results, but the digital planning future prospects seem to be promising. This study results highlighted how a photogrammetric scanner for dental arches would only have a much smaller shooting field size and greater accuracy. Despite these considerations, the photogrammetric facial scanner provided excellent results for the measurement of individual teeth, showing a great versatility of use.

Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments

Abstract: Rapid mapping of near-shore and coastal regions has become an indispensable task for the local authority to serve the purpose of coastal management and post-disaster monitoring. Aerial photogrammetry and satellite remote sensing have been utilized to fulfill such a task in the last few decades. Airborne LiDAR can further compensate the drawbacks of these image capturing approaches as a result of the direct geo-referenced 3D point cloud. The recent introduction of multispectral airborne LiDAR, such as the Teledyne Optech Titan, can potentially enhance the capability of water mapping, minimize the involvement of manual intervention and reduce the use of supplementary information or ancillary data. This study demonstrates the use of multispectral airborne LiDAR data for automatic land-water classification under different coastal and inland river environments. Two automatic training data selection methods are proposed. The first method utilizes Gaussian mixture model (GMM) to split preliminarily the land and water region based on the elevation/intensity histogram, and the second method is developed based on the use of scan line intensity-elevation ratio (SLIER). Subsequently, various LiDAR-derived feature sets, particularly based on the multispectral LiDAR intensity, are constructed in order to serve as an input for the log-likelihood classification model. Two optional post-classification enhancements can be implemented to further adjust the misclassified data points. The proposed workflow was evaluated with four Optech Titan datasets collected for different near-shore and river environments that are located nearby Lake Ontario, Ontario, Canada. Our experimental work demonstrated that the multispectral LiDAR intensity data was capable of enhancing the classification capability, where an overall accuracy better than 96% was achieved in most of the cases.

Low-budget topographic surveying comes of age: Structure from motion photogrammetry in geography and the geosciences:

Abstract: This is the author accepted manuscript. The final version is available from SAGE Publicarions via the DOI in this record

Monitoring and Computation of the Volumes of Stockpiles of Bulk Material by Means of UAV Photogrammetric Surveying

Abstract: The monitoring and metric assessment of piles of natural or man-made materials plays a fundamental role in the production and management processes of multiple activities. Over time, the monitoring techniques have undergone an evolution linked to the progress of measure and data processing techniques; starting from classic topography to global navigation satellite system (GNSS) technologies up to the current survey systems like laser scanner and close-range photogrammetry. Last-generation 3D data management software allow for the processing of increasingly truer high-resolution 3D models. This study shows the results of a test for the monitoring and computing of stockpile volumes of material coming from the differentiated waste collection inserted in the recycling chain, performed by means of an unmanned aerial vehicle (UAV) photogrammetric survey and the generation of 3D models starting from point clouds. The test was carried out with two UAV flight sessions, with vertical and oblique camera configurations, and using a terrestrial laser scanner for measuring the ground control points and as ground truth for testing the two survey configurations. The computations of the volumes were carried out using two software and comparisons were made both with reference to the different survey configurations and to the computation software.