Drones

Research

14 pages, 6845 KiB

Open AccessArticle

3D AQI Mapping Data Assessment of Low-Altitude Drone Real-Time Air Pollution Monitoring

by Sarun Duangsuwan, Phoowadon Prapruetdee, Mallika Subongkod and Katanyoo Klubsuwan

Drones 2022, 6(8), 191; https://doi.org/10.3390/drones6080191 - 29 Jul 2022

Cited by 13 | Viewed by 4638

Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO₂) released from factories. However, a major air pollution problem is particulate matter [...] Read more.

Air pollution primarily originates from substances that are directly emitted from natural or anthropogenic processes, such as carbon monoxide (CO) gas emitted in vehicle exhaust or sulfur dioxide (SO₂) released from factories. However, a major air pollution problem is particulate matter (PM), which is an adverse effect of wildfires and open burning. Application tools for air pollution monitoring in risk areas using real-time monitoring with drones have emerged. A new air quality index (AQI) for monitoring and display, such as three-dimensional (3D) mapping based on data assessment, is essential for timely environmental surveying. The objective of this paper is to present a 3D AQI mapping data assessment using a hybrid model based on a machine-learning method for drone real-time air pollution monitoring (Dr-TAPM). Dr-TAPM was designed by equipping drones with multi-environmental sensors for carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), particulate matter (PM_2.5,10), and sulfur dioxide (SO₂), with data pre- and post-processing with the hybrid model. The hybrid model for data assessment was proposed using backpropagation neural network (BPNN) and convolutional neural network (CNN) algorithms. Experimentally, we considered a case study detecting smoke emissions from an open burning scenario. As a result, PM_2.5,10 and CO were detected as air pollutants from open burning. 3D AQI map locations were shown and the validation learning rates were apparent, as the accuracy of predicted AQI data assessment was 98%. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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24 pages, 82754 KiB

Open AccessArticle

Oblique View Selection for Efficient and Accurate Building Reconstruction in Rural Areas Using Large-Scale UAV Images

by Yubin Liang, Xiaochang Fan, Yang Yang, Deqian Li and Tiejun Cui

Drones 2022, 6(7), 175; https://doi.org/10.3390/drones6070175 - 16 Jul 2022

Cited by 7 | Viewed by 2792

Abstract

3D building models are widely used in many applications. The traditional image-based 3D reconstruction pipeline without using semantic information is inefficient for building reconstruction in rural areas. An oblique view selection methodology for efficient and accurate building reconstruction in rural areas is proposed [...] Read more.

3D building models are widely used in many applications. The traditional image-based 3D reconstruction pipeline without using semantic information is inefficient for building reconstruction in rural areas. An oblique view selection methodology for efficient and accurate building reconstruction in rural areas is proposed in this paper. A Mask R-CNN model is trained using satellite datasets and used to detect building instances in nadir UAV images. Then, the detected building instances and UAV images are directly georeferenced. The georeferenced building instances are used to select oblique images that cover buildings by using nearest neighbours search. Finally, precise match pairs are generated from the selected oblique images and nadir images using their georeferenced principal points. The proposed methodology is tested on a dataset containing 9775 UAV images. A total of 4441 oblique images covering 99.4% of all the buildings in the survey area are automatically selected. Experimental results show that the average precision and recall of the oblique view selection are 0.90 and 0.88, respectively. The percentage of robustly matched oblique-oblique and oblique-nadir image pairs are above 94% and 84.0%, respectively. The proposed methodology is evaluated for sparse and dense reconstruction. Experimental results show that the sparse reconstruction based on the proposed methodology reduces 68.9% of the data processing time, and it is comparably accurate and complete. Experimental results also show high consistency between the dense point clouds of buildings reconstructed by the traditional pipeline and the pipeline based on the proposed methodology. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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17 pages, 6985 KiB

Open AccessArticle

Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring

by Laura Inzerillo, Francesco Acuto, Gaetano Di Mino and Mohammed Zeeshan Uddin

Drones 2022, 6(7), 171; https://doi.org/10.3390/drones6070171 - 12 Jul 2022

Cited by 21 | Viewed by 3964

Abstract

The increasingly widespread use of smartphones as real cameras on drones has allowed an ever-greater development of several algorithms to improve the image’s refinement. Although the latest generations of drone cameras let the user achieve high resolution images, the large number of pixels [...] Read more.

The increasingly widespread use of smartphones as real cameras on drones has allowed an ever-greater development of several algorithms to improve the image’s refinement. Although the latest generations of drone cameras let the user achieve high resolution images, the large number of pixels to be processed and the acquisitions from multiple lengths for stereo-view often fail to guarantee satisfactory results. In particular, high flight altitudes strongly impact the accuracy, and result in images which are undefined or blurry. This is not acceptable in the field of road pavement monitoring. In that case, the conventional algorithms used for the image resolution conversion, such as the bilinear interpolation algorithm, do not allow high frequency information to be retrieved from an undefined capture. This aspect is felt more strongly when using the recorded images to build a 3D scenario, since its geometric accuracy is greater when the resolution of the photos is higher. Super-Resolution algorithms (SRa) are utilized when registering multiple low-resolution images to interpolate sub-pixel information The aim of this work is to assess, at high flight altitudes, the geometric precision of a 3D model by using the the Morpho Super-Resolution™ algorithm for a road pavement distress monitoring case study. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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11 pages, 5652 KiB

Open AccessFeature PaperArticle

UAV Mapping and 3D Modeling as a Tool for Promotion and Management of the Urban Space

by Alexandros Skondras, Eleni Karachaliou, Ioannis Tavantzis, Nikolaos Tokas, Elena Valari, Ifigeneia Skalidi, Giovanni Augusto Bouvet and Efstratios Stylianidis

Drones 2022, 6(5), 115; https://doi.org/10.3390/drones6050115 - 3 May 2022

Cited by 24 | Viewed by 6559

Abstract

In the past few decades, the management of urban spaces with appropriate tools has been in constant discussion due to the plethora of new technologies that have emerged for participatory planning, drone mapping, photogrammetry and 3D modeling. In a multitude of situations, considerable [...] Read more.

In the past few decades, the management of urban spaces with appropriate tools has been in constant discussion due to the plethora of new technologies that have emerged for participatory planning, drone mapping, photogrammetry and 3D modeling. In a multitude of situations, considerable progress has been made regarding the strategic impact of the successful use of technology for the development of urban spaces. The current era provides us with important digital tools and the opportunity to test new perspectives in the sustainable development of cities. This paper aims to explore the contribution of UAVs to the spatial mapping process of urban space, with the goal of collecting quantifiable and qualitative information to use for 3D modeling that can enable a more comprehensive understanding of the urban environment, thus facilitating urban regeneration processes. Three-dimensional models of high accuracy are not mandatory for this research. The location of the selected research area is particularly interesting due to its boundaries, urban voids and public space that can evolve through public participation. The results can be used for crowdsourcing in participatory decision-making processes and for exploring the consequences that these have on the built environment, and they can be used as a new means of involvement of citizens in local decision-making processes. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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22 pages, 11826 KiB

Open AccessArticle

New Supplementary Photography Methods after the Anomalous of Ground Control Points in UAV Structure-from-Motion Photogrammetry

by Jia Yang, Xiaopeng Li, Lei Luo, Lewen Zhao, Juan Wei and Teng Ma

Drones 2022, 6(5), 105; https://doi.org/10.3390/drones6050105 - 24 Apr 2022

Cited by 8 | Viewed by 4067

Abstract

Recently, multirotor UAVs have been widely used in high-precision terrain mapping, cadastral surveys and other fields due to their low cost, flexibility, and high efficiency. Indirect georeferencing of ground control points (GCPs) is often required to obtain highly accurate topographic products such as [...] Read more.

Recently, multirotor UAVs have been widely used in high-precision terrain mapping, cadastral surveys and other fields due to their low cost, flexibility, and high efficiency. Indirect georeferencing of ground control points (GCPs) is often required to obtain highly accurate topographic products such as orthoimages and digital surface models. However, in practical projects, GCPs are susceptible to anomalies caused by external factors (GCPs covered by foreign objects such as crops and cars, vandalism, etc.), resulting in a reduced availability of UAV images. The errors associated with the loss of GCPs are apparent. The widely used solution of using natural feature points as ground control points often fails to meet the high accuracy requirements. For the problem of control point anomalies, this paper innovatively presents two new methods of completing data fusion by supplementing photos via UAV at a later stage. In this study, 72 sets of experiments were set up, including three control experiments for analysis. Two parameters were used for accuracy assessment: Root Mean Square Error (RMSE) and Multiscale Model to Model Cloud Comparison (M3C2). The study shows that the two new methods can meet the reference accuracy requirements in horizontal direction and elevation direction (RMSE_X = 70.40 mm, RMSE_Y = 53.90 mm, RMSE_Z = 87.70 mm). In contrast, the natural feature points as ground control points showed poor accuracy, with RMSE_X = 94.80 mm, RMSE_Y = 68.80 mm, and RMSE_Z = 104.40 mm for the checkpoints. This research considers and solves the problems of anomalous GCPs in the photogrammetry project from a unique perspective of supplementary photography, and proposes two new methods that greatly expand the means of solving the problem. In UAV high-precision projects, they can be used as an effective means to ensure accuracy when the GCP is anomalous, which has significant potential for application promotion. Compared with previous methods, they can be applied in more scenarios and have higher compatibility and operability. These two methods can be widely applied in cadastral surveys, geomorphological surveys, heritage conservation, and other fields. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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15 pages, 20945 KiB

Open AccessFeature PaperCommunication

CNN-Based Dense Monocular Visual SLAM for Real-Time UAV Exploration in Emergency Conditions

by Anne Steenbeek and Francesco Nex

Drones 2022, 6(3), 79; https://doi.org/10.3390/drones6030079 - 18 Mar 2022

Cited by 42 | Viewed by 9438

Abstract

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small [...] Read more.

Unmanned Aerial Vehicles (UAVs) for 3D indoor mapping applications are often equipped with bulky and expensive sensors, such as LIDAR (Light Detection and Ranging) or depth cameras. The same task could be also performed by inexpensive RGB cameras installed on light and small platforms that are more agile to move in confined spaces, such as during emergencies. However, this task is still challenging because of the absence of a GNSS (Global Navigation Satellite System) signal that limits the localization (and scaling) of the UAV. The reduced density of points in feature-based monocular SLAM (Simultaneous Localization and Mapping) then limits the completeness of the delivered maps. In this paper, the real-time capabilities of a commercial, inexpensive UAV (DJI Tello) for indoor mapping are investigated. The work aims to assess its suitability for quick mapping in emergency conditions to support First Responders (FR) during rescue operations in collapsed buildings. The proposed solution only uses images in input and integrates SLAM and CNN-based (Convolutional Neural Networks) Single Image Depth Estimation (SIDE) algorithms to densify and scale the data and to deliver a map of the environment suitable for real-time exploration. The implemented algorithms, the training strategy of the network, and the first tests on the main elements of the proposed methodology are reported in detail. The results achieved in real indoor environments are also presented, demonstrating performances that are compatible with FRs’ requirements to explore indoor volumes before entering the building. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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16 pages, 3009 KiB

Open AccessArticle

Classification of Photogrammetric and Airborne LiDAR Point Clouds Using Machine Learning Algorithms

by Zaide Duran, Kubra Ozcan and Muhammed Enes Atik

Drones 2021, 5(4), 104; https://doi.org/10.3390/drones5040104 - 24 Sep 2021

Cited by 21 | Viewed by 4556

Abstract

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information [...] Read more.

With the development of photogrammetry technologies, point clouds have found a wide range of use in academic and commercial areas. This situation has made it essential to extract information from point clouds. In particular, artificial intelligence applications have been used to extract information from point clouds to complex structures. Point cloud classification is also one of the leading areas where these applications are used. In this study, the classification of point clouds obtained by aerial photogrammetry and Light Detection and Ranging (LiDAR) technology belonging to the same region is performed by using machine learning. For this purpose, nine popular machine learning methods have been used. Geometric features obtained from point clouds were used for the feature spaces created for classification. Color information is also added to these in the photogrammetric point cloud. According to the LiDAR point cloud results, the highest overall accuracies were obtained as 0.96 with the Multilayer Perceptron (MLP) method. The lowest overall accuracies were obtained as 0.50 with the AdaBoost method. The method with the highest overall accuracy was achieved with the MLP (0.90) method. The lowest overall accuracy method is the GNB method with 0.25 overall accuracy. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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18 pages, 34779 KiB

Open AccessArticle

Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping

by Conor McMahon, Omar E. Mora and Michael J. Starek

Drones 2021, 5(2), 50; https://doi.org/10.3390/drones5020050 - 1 Jun 2021

Cited by 10 | Viewed by 5845

Abstract

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low [...] Read more.

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs. Full article

(This article belongs to the Special Issue UAV Photogrammetry for 3D Modeling)

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