Automatic detection and mapping of European ground squirrel burrows on UAV-based multi- and hyperspectral imagery with classification methods

2020 ◽  
Author(s):  
Mátyás Árvai ◽  
János Mészáros ◽  
Zsófia Kovács ◽  
Eric C. Brevik ◽  
László Pásztor ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Ground Squirrels ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Visible Range ◽  
Vegetation Height ◽  
Local Populations ◽  
Area Of Occupancy ◽  
Automated Method ◽  
Colony Area

<p>European ground squirrels (EGS) are members of the soil megafauna and part of the ecosystem engineers that shape physical, chemical, and biological characteristics of soil ecosystems in European grasslands. Thanks to their strict protection their abundance and distribution have been surveyed systematically and annually in Hungary. The results of their 20 year monitoring indicate that their population is declining, there are sudden extinctions of local populations, and a desynchronized variation of the abundance of local populations occur either spatially or temporally.</p><p>The monitoring protocol involves the estimation of their abundance in each colony by a strip-transect method and the habitat-colony area by visual observations or digital maps. Both approaches use animal burrows as proxies for either their presence (colony area) or density (colony size). These estimation methods, however, consist of systematical errors: first, they consider the animals’ density to be even over the entire habitat-area, second, they conjecture that animals occupy the habitable area completely, and third, evenly. If we were able to survey distribution and abundance of EGS more accurately, frequently, and efficiently, we could better intervene in time when local populations begin to decline or before they disappear. In addition, we could better estimate the effects (+ or -) of management strategies in real time.</p><p>The primary aims of our study were to develop a non-invasive, semi-automated method for (1) estimating abundance of EGS in the area of occupation of a colony, and (2) delineating their occupancy within the habitable area. We have defined burrow openings and mounds as quantitative proxies for the presence of animals. We have started to develop a monitoring technique to identify, locate, and count objects of interest in images automatically and to delineate the area of occupancy by identifying those objects of interest from the surroundings. To survey EGS colonies and habitats we have used a multirotor platform UAV equipped with either an RGB visible-range or a hyperspectral sensor.</p><p>To test our method several pilot areas with different vegetation and relief were surveyed. Acquired aerial images have been processed by photogrammetric software and resulting high spatial resolution orthomosaics are classified by machine-learning algorithms (randomforest, CART, C5.0) implemented in a custom R script. As detection of mounds and openings are visually restricted by vegetation height (e.g. grass, shrubs, weeds, herbs), we have studied the effect of grass height on detection success. Preliminary results suggest that successful classification can be performed either on RGB visible-range and hyperspectral images. However, the appropriate spatial resolution (below cm range) and the presence of high grass are more important key factors than number of spectral bands.</p><p>Detecting EGS burrow openings and mounds is based on surface characteristics of EGS burrow openings and mounds consequently the method is being developed for EGS specifically but can be modified to the characteristics of other burrowing mammals of this size (e.g. mole-rats, moles).</p>

scholarly journals Potential of spectroscopic analyses for non-destructive estimation of tea quality-related metabolites in fresh new leaves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroto Yamashita ◽  
Rei Sonobe ◽  
Yuhei Hirono ◽  
Akio Morita ◽  
Takashi Ikka
Keyword(s):  
Short Wave ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Chemical Information ◽  
Tea Quality ◽  
The Mean ◽  
Processing Techniques ◽  
Spectral Patterns ◽  
Physical And Chemical ◽  
Non Destructive

AbstractSpectroscopic sensing provides physical and chemical information in a non-destructive and rapid manner. To develop non-destructive estimation methods of tea quality-related metabolites in fresh leaves, we estimated the contents of free amino acids, catechins, and caffeine in fresh tea leaves using visible to short-wave infrared hyperspectral reflectance data and machine learning algorithms. We acquired these data from approximately 200 new leaves with various status and then constructed the regression model in the combination of six spectral patterns with pre-processing and five algorithms. In most phenotypes, the combination of de-trending pre-processing and Cubist algorithms was robustly selected as the best combination in each round over 100 repetitions that were evaluated based on the ratio of performance to deviation (RPD) values. The mean RPD values were ranged from 1.1 to 2.7 and most of them were above the acceptable or accurate threshold (RPD = 1.4 or 2.0, respectively). Data-based sensitivity analysis identified the important hyperspectral regions around 1500 and 2000 nm. Present spectroscopic approaches indicate that most tea quality-related metabolites can be estimated non-destructively, and pre-processing techniques help to improve its accuracy.

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

2021 ◽  
pp. 036119812110061
Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Condition Assessment ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geographical Information ◽  
Related Field ◽  
Pavement Surface ◽  
Automated Method ◽  
Smartphone Technology

The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone technology for the spatial condition-assessment mapping of roadway pavement surface anomalies. The study explores the use of data, collected by sensors from a smartphone and a vehicle’s onboard diagnostic device while the vehicle is in movement, for the detection of roadway anomalies. The research proposes a low-cost and automated method to obtain up-to-date information on roadway pavement surface anomalies with the use of smartphone technology, artificial neural networks, robust regression analysis, and supervised machine learning algorithms for multiclass problems. The technology for the suggested system is readily available and accurate and can be utilized in pavement monitoring systems and geographical information system applications. Further, the proposed methodology has been field-tested, exhibiting accuracy levels higher than 90%, and it is currently expanded to include larger datasets and a bigger number of common roadway pavement surface defect types. The proposed system is of practical importance since it provides continuous information on roadway pavement surface conditions, which can be valuable for pavement engineers and public safety.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals FaceSync: Open source framework for recording facial expressions with head-mounted cameras

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 702 ◽  
Author(s):  
Jin Hyun Cheong ◽  
Sawyer Brooks ◽  
Luke J. Chang
Keyword(s):  
Facial Expression ◽  
Open Source ◽  
Facial Expressions ◽  
Video Recording ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Video Recordings ◽  
Automated Method ◽  
Open Source Framework ◽  
The Cost

Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings.  Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups, including group interaction experiments.

scholarly journals Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

2020 ◽  
Author(s):  
Carolyn Lou ◽  
Pascal Sati ◽  
Martina Absinta ◽  
Kelly Clark ◽  
Jordan D. Dworkin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
List Type ◽  
Training Set ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification

AbstractBackground and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

scholarly journals Comparing three approaches of spatial disaggregation of legacy soil maps based on DSMART algorithm

2019 ◽  
Author(s):  
Yosra Ellili ◽  
Brendan Philip Malone ◽  
Didier Michot ◽  
Budiman Minasny ◽  
Sébastien Vincent ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Type ◽  
Expert Knowledge ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Soil Profiles ◽  
Soil Maps ◽  
Soil Map ◽  
Soil Landscape ◽  
Spatial Disaggregation

Abstract. Enhancing the spatial resolution of pedological information is a great challenge in the field of Digital Soil Mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially available at coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining Soil Map Units (SMU), where each SMU can include one or several Soil Type Units (STU) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at finer spatial resolution by machine learning algorithms using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithm (DSMART with extra soil profiles, DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at 50 m resolution was assessed over a large study area (6775 km2) using an external validation based on independent 135 soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1 : 25 000 soil maps. Pairwise comparisons were also performed, using Shannon entropy measure, to spatially locate differences between disaggregated maps. The main results show that adding soil landscape relationships in the disaggregation process enhances the performance of prediction of soil type distribution. Considering the three most probable STU and using 135 independent soil profiles, the overall accuracy measures are: 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost twofold higher when validated using 3 × 3 windows. They achieved 28.5 % for DSMART with soil landscape relationships, 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships as well as extra soil observations constraints the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial to obtain consistent soil maps with clear internal disaggregation of SMU across the landscape.

scholarly journals Evaluation of Six High-Spatial Resolution Clear-Sky Surface Upward Longwave Radiation Estimation Methods with MODIS

2020 ◽  
Vol 12 (11) ◽  
pp. 1834
Author(s):  
Boxiong Qin ◽  
Biao Cao ◽  
Hua Li ◽  
Zunjian Bian ◽  
Tian Hu ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hybrid Methods ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Estimation Methods ◽  
Mean Bias Error ◽  
Clear Sky ◽  
Surface Radiation Budget

Surface upward longwave radiation (SULR) is a critical component in the calculation of the Earth’s surface radiation budget. Multiple clear-sky SULR estimation methods have been developed for high-spatial resolution satellite observations. Here, we comprehensively evaluated six SULR estimation methods, including the temperature-emissivity physical methods with the input of the MYD11/MYD21 (TE-MYD11/TE-MYD21), the hybrid methods with top-of-atmosphere (TOA) linear/nonlinear/artificial neural network regressions (TOA-LIN/TOA-NLIN/TOA-ANN), and the hybrid method with bottom-of-atmosphere (BOA) linear regression (BOA-LIN). The recently released MYD21 product and the BOA-LIN—a newly developed method that considers the spatial heterogeneity of the atmosphere—is used initially to estimate SULR. In addition, the four hybrid methods were compared with simulated datasets. All the six methods were evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Surface Radiation Budget Network (SURFRAD) in situ measurements. Simulation analysis shows that the BOA-LIN is the best one among four hybrid methods with accurate atmospheric profiles as input. Comparison of all the six methods shows that the TE-MYD21 performed the best, with a root mean square error (RMSE) and mean bias error (MBE) of 14.0 and −0.2 W/m2, respectively. The RMSE of BOA-LIN, TOA-NLIN, TOA-LIN, TOA-ANN, and TE-MYD11 are equal to 15.2, 16.1, 17.2, 21.2, and 18.5 W/m2, respectively. TE-MYD21 has a much better accuracy than the TE-MYD11 over barren surfaces (NDVI < 0.3) and a similar accuracy over non-barren surfaces (NDVI ≥ 0.3). BOA-LIN is more stable over varying water vapor conditions, compared to other hybrid methods. We conclude that this study provides a valuable reference for choosing the suitable estimation method in the SULR product generation.

scholarly journals Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
Debasis Maji ◽  
Arif Ahmed Sekh
Keyword(s):  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Retinal Images ◽  
Fundus Images ◽  
Hard Exudates ◽  
Automated Method ◽  
Eye Problems ◽  
Automated Grading ◽  
Automatic Grading ◽  
Automated Screening

Abstract Automatic grading of retinal blood vessels from fundus image can be a useful tool for diagnosis, planning and treatment of eye. Automatic diagnosis of retinal images for early detection of glaucoma, stroke, and blindness is emerging in intelligent health care system. The method primarily depends on various abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture, and entropies. The development of an automated screening system based on vessel width, tortuosity, and vessel branching are also used for grading. However, the automated method that directly can come to a decision by taking the fundus images got less attention. Detecting eye problems based on the tortuosity of the vessel from fundus images is a complicated task for opthalmologists. So automated grading algorithm using deep learning can be most valuable for grading retinal health. The aim of this work is to develop an automatic computer aided diagnosis system to solve the problem. This work approaches to achieve an automatic grading method that is opted using Convolutional Neural Network (CNN) model. In this work we have studied the state-of-the-art machine learning algorithms and proposed an attention network which can grade retinal images. The proposed method is validated on a public dataset EIARG1, which is only publicly available dataset for such task as per our knowledge.

scholarly journals DEVIATION BETWEEN HILL SLOPE ESTIMATION METHODS AND THE ROLE OF DEM SPATIAL RESOLUTION. COMPARATIVE STUDY IN A GIS ENVIRONMENT

2018 ◽  
Vol 36 (2) ◽  
pp. 894
Author(s):  
Μ. Φουμέλης ◽  
Εμμ. Βασιλάκης
Keyword(s):  
Spatial Resolution ◽  
Correlation Factor ◽  
Estimation Methods ◽  
General Tendency ◽  
New Approach ◽  
Hill Slope ◽  
Digital Elevation ◽  
Slope Estimation ◽  
Gis Environment

Three methods are descibed between the most frequently used for the estimation of hill slope from digital elevation models (DEM), the 'd8' method (Travis et al. 1975), Horn's method (1981) and Zevenbergen and Thorne method (1987), are described. Also a new approach was examined introduced by Tarboton (1997) called 'd-infinite' method. In order to investigate the spatial distribution of deviations between the examined methods, a statistical analysis was performed in a GIS environment. The magnitude of the differences between the methods was determined by calculating the RMSEr (Root Mean Square Error), while the degree of their equivalence was expressed by the linear correlation factor (r). Most deviations were located in areas of high relief, whereas the most interesting fact was that each method overestimates or underestimates the slope in very specific morphological structures, e.g. along watersheds, streams and generally in some types of morphological discontinuities. Finally, the DEM spatial resolution effect was explored on the estimated slope values using the selected methods. Regardless the method used, a general tendency of underestimating slope values by decreasing the DEM resolution was observed. The degree of divergences between these was also highly depended on the change of spatial resolution. Thus, the importance of defining a suitable resolution for each application should be addressed.

scholarly journals FaceSync: Open source framework for recording facial expressions with head-mounted cameras

2017 ◽  
Author(s):  
Jin Hyun Cheong ◽  
Sawyer Brooks ◽  
Luke J. Chang
Keyword(s):  
Facial Expression ◽  
Open Source ◽  
Facial Expressions ◽  
Video Recording ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Video Recordings ◽  
Automated Method ◽  
Open Source Framework ◽  
The Cost

Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings. Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups including group interaction experiments.

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