Classification of Mediterranean Shrub Species from UAV Point Clouds

Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.

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

Drones ◽

10.3390/drones5040104 ◽

2021 ◽

Vol 5 (4) ◽

pp. 104

Author(s):

Zaide Duran ◽

Kubra Ozcan ◽

Muhammed Enes Atik

Keyword(s):

Machine Learning ◽

Point Cloud ◽

Point Clouds ◽

Machine Learning Algorithms ◽

Airborne Lidar ◽

Aerial Photogrammetry ◽

Feature Spaces ◽

Wide Range ◽

Extract Information

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.

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An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

Remote Sensing ◽

10.3390/rs13061121 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1121

Author(s):

Raul Sampaio de Lima ◽

Mait Lang ◽

Niall G. Burnside ◽

Miguel Villoslada Peciña ◽

Tauri Arumäe ◽

...

Keyword(s):

Data Acquisition ◽

Laser Scanning ◽

Model Fitting ◽

Ground Level ◽

Point Clouds ◽

Production Quality ◽

Unmanned Aerial Systems ◽

Aerial Photogrammetry ◽

Wide Range ◽

Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

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Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13112135 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2135

Author(s):

Jesús Balado ◽

Pedro Arias ◽

Henrique Lorenzo ◽

Adrián Meijide-Rodríguez

Keyword(s):

Laser Scanning ◽

Ambient Noise ◽

Point Clouds ◽

Density Variation ◽

Density Reduction ◽

Manual Intervention ◽

Point Density ◽

Disturbance Analysis ◽

Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

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Modes of Interaction in Naturally Occurring Medical Encounters With General Practitioners: The “One in a Million” Study

Qualitative Health Research ◽

10.1177/1049732321993790 ◽

2021 ◽

pp. 104973232199379

Author(s):

Olaug S. Lian ◽

Sarah Nettleton ◽

Åge Wifstad ◽

Christopher Dowrick

Keyword(s):

General Practitioners ◽

Narrative Analysis ◽

Mode Competition ◽

General Applicability ◽

Naturally Occurring ◽

Wide Range ◽

The One ◽

Narrative Mode ◽

Modes Of Interaction

In this article, we qualitatively explore the manner and style in which medical encounters between patients and general practitioners (GPs) are mutually conducted, as exhibited in situ in 10 consultations sourced from the One in a Million: Primary Care Consultations Archive in England. Our main objectives are to identify interactional modes, to develop a classification of these modes, and to uncover how modes emerge and shift both within and between consultations. Deploying an interactional perspective and a thematic and narrative analysis of consultation transcripts, we identified five distinctive interactional modes: question and answer (Q&A) mode, lecture mode, probabilistic mode, competition mode, and narrative mode. Most modes are GP-led. Mode shifts within consultations generally map on to the chronology of the medical encounter. Patient-led narrative modes are initiated by patients themselves, which demonstrates agency. Our classification of modes derives from complete naturally occurring consultations, covering a wide range of symptoms, and may have general applicability.

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Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽

10.3390/computers10060082 ◽

2021 ◽

Vol 10 (6) ◽

pp. 82

Author(s):

Ahmad O. Aseeri

Keyword(s):

Deep Learning ◽

Cardiac Arrhythmias ◽

Large Scale ◽

Clinical Decision Making ◽

Probabilistic Approach ◽

Classification Model ◽

Gating Mechanism ◽

Uncertainty Estimates ◽

Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

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Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽

10.3390/en14051377 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1377

Author(s):

Musaab I. Magzoub ◽

Raj Kiran ◽

Saeed Salehi ◽

Ibnelwaleed A. Hussein ◽

Mustafa S. Nasser

Keyword(s):

Machine Learning ◽

Rheological Properties ◽

Nearest Neighbor ◽

Drilling Fluid ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

Wide Range ◽

Machine Learning Approach ◽

Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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Recent Advances in Unmanned Aerial Vehicle Forest Remote Sensing—A Systematic Review. Part I: A General Framework

Forests ◽

10.3390/f12030327 ◽

2021 ◽

Vol 12 (3) ◽

pp. 327 ◽

Cited By ~ 1

Author(s):

Riccardo Dainelli ◽

Piero Toscano ◽

Salvatore Filippo Di Gennaro ◽

Alessandro Matese

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Relevant Information ◽

Forest Monitoring ◽

Machine Learning Techniques ◽

Planted Forests ◽

Systematic Analysis ◽

Aerial Photogrammetry ◽

Wide Range ◽

Aerial Vehicle

Natural, semi-natural, and planted forests are a key asset worldwide, providing a broad range of positive externalities. For sustainable forest planning and management, remote sensing (RS) platforms are rapidly going mainstream. In a framework where scientific production is growing exponentially, a systematic analysis of unmanned aerial vehicle (UAV)-based forestry research papers is of paramount importance to understand trends, overlaps and gaps. The present review is organized into two parts (Part I and Part II). Part II inspects specific technical issues regarding the application of UAV-RS in forestry, together with the pros and cons of different UAV solutions and activities where additional effort is needed, such as the technology transfer. Part I systematically analyzes and discusses general aspects of applying UAV in natural, semi-natural and artificial forestry ecosystems in the recent peer-reviewed literature (2018–mid-2020). The specific goals are threefold: (i) create a carefully selected bibliographic dataset that other researchers can draw on for their scientific works; (ii) analyze general and recent trends in RS forest monitoring (iii) reveal gaps in the general research framework where an additional activity is needed. Through double-step filtering of research items found in the Web of Science search engine, the study gathers and analyzes a comprehensive dataset (226 articles). Papers have been categorized into six main topics, and the relevant information has been subsequently extracted. The strong points emerging from this study concern the wide range of topics in the forestry sector and in particular the retrieval of tree inventory parameters often through Digital Aerial Photogrammetry (DAP), RGB sensors, and machine learning techniques. Nevertheless, challenges still exist regarding the promotion of UAV-RS in specific parts of the world, mostly in the tropical and equatorial forests. Much additional research is required for the full exploitation of hyperspectral sensors and for planning long-term monitoring.

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Classification of unlabeled online media

Scientific Reports ◽

10.1038/s41598-021-85608-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sakthi Kumar Arul Prakash ◽

Conrad Tucker

Keyword(s):

Social Media ◽

Real World ◽

Graphical Model ◽

Ground Truth ◽

Classification Problem ◽

Machine Learning Algorithms ◽

Social Media Networks ◽

Online Social Media ◽

Wide Range

AbstractThis work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need for ground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, this work leverages user–user and user–media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) being spread, without needing to know the actual details of the information itself. To study the inception and evolution of user–user and user–media interactions over time, we create an experimental platform that mimics the functionality of real-world social media networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty (entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world social media network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, and with media content. The discovery that the entropy of user–user and user–media interactions approximate fake and authentic media likes, enables us to classify fake media in an unsupervised learning manner.

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Sequence data from isolated lichen-associated melanized fungi enhance delimitation of two new lineages within Chaetothyriomycetidae

Mycological Progress ◽

10.1007/s11557-021-01706-8 ◽

2021 ◽

Vol 20 (7) ◽

pp. 911-927

Author(s):

Lucia Muggia ◽

Yu Quan ◽

Cécile Gueidan ◽

Abdullah M. S. Al-Hatmi ◽

Martin Grube ◽

...

Keyword(s):

Sequence Data ◽

Single Species ◽

Sister Group ◽

Asexual Propagation ◽

Dna Sequence Data ◽

Wide Range ◽

The Family ◽

Rock Inhabiting Fungi ◽

Stable Habitat

AbstractLichen thalli provide a long-lived and stable habitat for colonization by a wide range of microorganisms. Increased interest in these lichen-associated microbial communities has revealed an impressive diversity of fungi, including several novel lineages which still await formal taxonomic recognition. Among these, members of the Eurotiomycetes and Dothideomycetes usually occur asymptomatically in the lichen thalli, even if they share ancestry with fungi that may be parasitic on their host. Mycelia of the isolates are characterized by melanized cell walls and the fungi display exclusively asexual propagation. Their taxonomic placement requires, therefore, the use of DNA sequence data. Here, we consider recently published sequence data from lichen-associated fungi and characterize and formally describe two new, individually monophyletic lineages at family, genus, and species levels. The Pleostigmataceae fam. nov. and Melanina gen. nov. both comprise rock-inhabiting fungi that associate with epilithic, crust-forming lichens in subalpine habitats. The phylogenetic placement and the monophyly of Pleostigmataceae lack statistical support, but the family was resolved as sister to the order Verrucariales. This family comprises the species Pleostigma alpinum sp. nov., P. frigidum sp. nov., P. jungermannicola, and P. lichenophilum sp. nov. The placement of the genus Melanina is supported as a lineage within the Chaetothyriales. To date, this genus comprises the single species M. gunde-cimermaniae sp. nov. and forms a sister group to a large lineage including Herpotrichiellaceae, Chaetothyriaceae, Cyphellophoraceae, and Trichomeriaceae. The new phylogenetic analysis of the subclass Chaetothyiomycetidae provides new insight into genus and family level delimitation and classification of this ecologically diverse group of fungi.

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BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽

10.3390/s21062144 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2144

Author(s):

Stefan Reitmann ◽

Lorenzo Neumann ◽

Bernhard Jung

Keyword(s):

Point Clouds ◽

Training Data ◽

Sensor Data ◽

Generation Process ◽

Data Generation ◽

Depth Sensor ◽

Depth Sensing ◽

Depth Sensors ◽

3D Point Clouds ◽

Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

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