Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data.

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Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

Remote Sensing ◽

10.3390/rs10121907 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1907 ◽

Cited By ~ 18

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Jonáš Hruška ◽

Telmo Adão ◽

Emanuel Peres ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Ground Truth ◽

Volume Estimation ◽

Height Range ◽

Ground Truth Data ◽

Operational Technique ◽

Digital Elevation ◽

Multi Temporal ◽

Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

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LOW COST MULTI-SENSOR ROBOT LASER SCANNING SYSTEM AND ITS ACCURACY INVESTIGATIONS FOR INDOOR MAPPING APPLICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w8-83-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 83-85 ◽

Cited By ~ 3

Author(s):

C. Chen ◽

X. Zou ◽

M. Tian ◽

J. Li ◽

W. Wu ◽

...

Keyword(s):

Inertial Measurement Unit ◽

Laser Scanning ◽

Low Cost ◽

Laser Scanner ◽

Ground Truth ◽

Measurement Unit ◽

Scanning System ◽

Ground Truth Data ◽

Multiple Sensor ◽

Laser Scanning System

In order to solve the automation of 3D indoor mapping task, a low cost multi-sensor robot laser scanning system is proposed in this paper. The multiple-sensor robot laser scanning system includes a panorama camera, a laser scanner, and an inertial measurement unit and etc., which are calibrated and synchronized together to achieve simultaneously collection of 3D indoor data. Experiments are undertaken in a typical indoor scene and the data generated by the proposed system are compared with ground truth data collected by a TLS scanner showing an accuracy of 99.2% below 0.25 meter, which explains the applicability and precision of the system in indoor mapping applications.

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Potentiality and Limitations of Open Satellite Data for Flood Mapping

10.20944/preprints201807.0624.v1 ◽

2018 ◽

Author(s):

Davide Notti ◽

Daniele Giordan ◽

Fabiana Calò ◽

Antonio Pepe ◽

Francesco Zucca ◽

...

Keyword(s):

Satellite Data ◽

Low Cost ◽

Ground Truth ◽

Flood Mapping ◽

Ancillary Data ◽

Ground Truth Data ◽

Flood Peak ◽

Sar Data ◽

Flooded Areas ◽

Flood Maps

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

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

Heritage ◽

10.3390/heritage2030112 ◽

2019 ◽

Vol 2 (3) ◽

pp. 1835-1851 ◽

Cited By ~ 14

Author(s):

Hafizur Rahaman ◽

Erik Champion

Keyword(s):

Low Cost ◽

Laser Scanner ◽

Ground Truth ◽

Point Clouds ◽

3D Models ◽

3D Modelling ◽

Quality Data ◽

Average Deviation ◽

Ground Truth Data ◽

Software Application

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.

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Low-Cost Three-Dimensional Modeling of Crop Plants

Sensors ◽

10.3390/s19132883 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2883 ◽

Cited By ~ 8

Author(s):

Jorge Martinez-Guanter ◽

Ángela Ribeiro ◽

Gerassimos G. Peteinatos ◽

Manuel Pérez-Ruiz ◽

Roland Gerhards ◽

...

Keyword(s):

Low Cost ◽

Three Dimensional ◽

Ground Truth ◽

Reconstruction Algorithm ◽

3D Models ◽

Crop Plants ◽

Plant Phenotyping ◽

Area Index ◽

Ground Truth Data ◽

The Creation

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.

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Merging Unmanned Aerial Systems (UAS) Imagery and Echo Soundings with an Adaptive Sampling Technique for Bathymetric Surveys

Remote Sensing ◽

10.3390/rs10091362 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1362 ◽

Cited By ~ 11

Author(s):

Laura Alvarez ◽

Hernan Moreno ◽

Antonio Segales ◽

Tri Pham ◽

Elizabeth Pillar-Little ◽

...

Keyword(s):

Adaptive Sampling ◽

Low Cost ◽

Sampling Technique ◽

Ground Truth ◽

Accurate Method ◽

Unmanned Aerial Systems ◽

Southern Plains ◽

Ground Truth Data ◽

Light Passing ◽

Aerial Systems

Bathymetric surveying to gather information about depths and underwater terrain is increasingly important to the sciences of hydrology and geomorphology. Submerged terrain change detection, water level, and reservoir storage monitoring demand extensive bathymetric data. Despite often being scarce or unavailable, this information is fundamental to hydrodynamic modeling for imposing boundary conditions and building computational domains. In this manuscript, a novel, low-cost, rapid, and accurate method is developed to measure submerged topography, as an alternative to conventional approaches that require significant economic investments and human power. The method integrates two types of Unmanned Aerial Systems (UAS) sampling techniques. The first couples a small UAS (sUAS) to an echosounder attached to a miniaturized boat for surveying submerged topography in deeper water within the range of accuracy. The second uses Structure from Motion (SfM) photogrammetry to cover shallower water areas no detected by the echosounder where the bed is visible from the sUAS. The refraction of light passing through air–water interface is considered for improving the bathymetric results. A zonal adaptive sampling algorithm is developed and applied to the echosounder data to densify measurements where the standard deviation of clustered points is high. This method is tested at a small reservoir in the U.S. southern plains. Ground Control Points (GCPs) and checkpoints surveyed with a total station are used for properly georeferencing of the SfM photogrammetry and assessment of the UAS imagery accuracy. An independent validation procedure providing a number of skill and error metrics is conducted using ground-truth data collected with a leveling rod at co-located reservoir points. Assessment of the results shows a strong correlation between the echosounder, SfM measurements and the field observations. The final product is a hybrid bathymetric survey resulting from the merging of SfM photogrammetry and echosoundings within an adaptive sampling framework.

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Perception in the Dark; Development of a ToF Visual Inertial Odometry System

Sensors ◽

10.3390/s20051263 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1263 ◽

Cited By ~ 2

Author(s):

Shengyang Chen ◽

Ching-Wei Chang ◽

Chih-Yung Wen

Keyword(s):

Weighting Function ◽

Current System ◽

Low Cost ◽

Ground Truth ◽

Measurement Unit ◽

Point Selection ◽

Variable Intensity ◽

Ground Truth Data ◽

Depth Measurement ◽

Tof Camera

Visual inertial odometry (VIO) is the front-end of visual simultaneous localization and mapping (vSLAM) methods and has been actively studied in recent years. In this context, a time-of-flight (ToF) camera, with its high accuracy of depth measurement and strong resilience to ambient light of variable intensity, draws our interest. Thus, in this paper, we present a realtime visual inertial system based on a low cost ToF camera. The iterative closest point (ICP) methodology is adopted, incorporating salient point-selection criteria and a robustness-weighting function. In addition, an error-state Kalman filter is used and fused with inertial measurement unit (IMU) data. To test its capability, the ToF–VIO system is mounted on an unmanned aerial vehicle (UAV) platform and operated in a variable light environment. The estimated flight trajectory is compared with the ground truth data captured by a motion capture system. Real flight experiments are also conducted in a dark indoor environment, demonstrating good agreement with estimated performance. The current system is thus shown to be accurate and efficient for use in UAV applications in dark and Global Navigation Satellite System (GNSS)-denied environments.

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The Influence of Camera Calibration on Nearshore Bathymetry Estimation from UAV Videos

Remote Sensing ◽

10.3390/rs13010150 ◽

2021 ◽

Vol 13 (1) ◽

pp. 150

Author(s):

Gonzalo Simarro ◽

Daniel Calvete ◽

Theocharis A. Plomaritis ◽

Francesc Moreno-Noguer ◽

Ifigeneia Giannoukakou-Leontsini ◽

...

Keyword(s):

Camera Calibration ◽

Low Cost ◽

Ground Truth ◽

Critical Issue ◽

Cost Evaluation ◽

Lens Distortion ◽

Critical Aspect ◽

Ground Truth Data ◽

Video Systems ◽

Mean Square Errors

Measuring the nearshore bathymetry is critical in coastal management and morphodynamic studies. The recent advent of Unmanned Aerial Vehicles (UAVs), in combination with coastal video monitoring techniques, allows for an alternative and low cost evaluation of the nearshore bathymetry. Camera calibration and stabilization is a critical issue in bathymetry estimation from video systems. This work introduces a new methodology in order to obtain such bathymetries, and it compares the results to echo-sounder ground truth data. The goal is to gain a better understanding on the influence of the camera calibration and stabilization on the inferred bathymetry. The results show how the proposed methodology allows for accurate evaluations of the bathymetry, with overall root mean square errors in the order of 40 cm. It is shown that the intrinsic calibration of the camera, related to the lens distortion, is the most critical aspect. Here, the intrinsic calibration that was obtained directly during the flight yields the best results.

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EARBOX: An open tool for high-throughput measurements of the spatial organization of maize ears and inference of novel traits

10.1101/2021.12.20.473433 ◽

2021 ◽

Author(s):

Vincent Oury ◽

Timothe Leroux ◽

Olivier Turc ◽

Romain Chapuis ◽

Carine Palaffre ◽

...

Keyword(s):

Open Source ◽

High Throughput ◽

Spatial Organization ◽

Low Cost ◽

Plant Genetic Resources ◽

Ground Truth ◽

Ground Truth Data ◽

Spatial Features ◽

Novel Traits ◽

Maize Ear

Background: Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results: We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organization, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions: The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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