Artificial intelligence for discrimination of sediment facies based on high-resolution elemental and colour data from coastal sediments of the East Frisian Wadden Sea, Germany

2020 ◽  
Author(s):  
An-Sheng Lee ◽  
Dirk Enters ◽  
Sofia Ya Hsuan Liou ◽  
Bernd Zolitschka
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Wadden Sea ◽  
Environmental Variability ◽  
Conventional Approach ◽  
Coastal Sediments ◽  
Supervised Machine Learning ◽  
Chemical Information ◽  
Xrf Core Scanner ◽  
Physical And Chemical

<p>Sediment facies provide vital information for the reconstruction of past environmental variability. Due to rising interest for paleoclimate data, sediment surveys are continually growing in importance as well as the amount of sediments to be discriminated into different facies. The conventional approach is to macroscopically determine sediment structure and colour and combine them with physical and chemical information - a time-consuming task heavily relying on the experience of the scientist in charge. Today, rapidly generated and high-resolution multiproxy sediment parameters are readily available from down-core scanning techniques and provide qualitative or even quantitative physical and chemical sediment properties. In 2016, an interdisciplinary research project WASA (Wadden Sea Archive) was launched to investigate palaeo-landscapes and environments of the Wadden Sea. The project has recovered 92 up to 5 m long sediment cores from the tidal flats, channels and off-shore around the island of Norderney (East Frisian Wadden Sea, Germany). Their facies were described by the conventional approach into glacioflucial sands, moraine, peat, tidal deposits, shoreface sediments, etc. In this study, those sediments were scanned by a micro X-ray fluorescence (µ-XRF) core scanner to obtain high-resolution records of multi-elemental data (2000 µm) and optical images (47 µm). Here we propose a supervised machine-learning application for the discrimination of sediment facies using these scanning data. Thus, the invested time and the potential bias common for the conventional approach can be reduced considerably. We expect that our approach will contribute to developing a more comprehensive and time-efficient automatic sediment facies discrimination.</p><p>Keywords: the Wadden Sea, µ-XRF core scanning, machine-learning, sediment facies discrimination</p>

scholarly journals Augmenting biologging with supervised machine learning to studyin situbehavior of the medusaChrysaora fuscescens

2019 ◽  
Author(s):  
Clara Fannjiang ◽  
T. Aran Mooney ◽  
Seth Cones ◽  
David Mann ◽  
K. Alex Shorter ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Laboratory Data ◽  
Supervised Machine Learning ◽  
Motion Sensors ◽  
Fine Scale ◽  
Video Footage ◽  
Research Findings ◽  
Summary Statement

AbstractZooplankton occupy critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood due to the difficulty of studying individualsin situ. Here we combine biologging with supervised machine learning (ML) to demonstrate a pipeline for studyingin situbehavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on 8Chrysaora fuscescensin Monterey Bay, using the tether method for retrieval. Using simultaneous video footage of the tagged jellyfish, we develop ML methods to 1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and 2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and provide evidence that developing behavioral classifiers onin siturather than laboratory data is essential.Summary StatementHigh-resolution motion sensors paired with supervised machine learning can be used to infer fine-scalein situbehavior of zooplankton for long durations.

scholarly journals Interpreting High-resolution Spectroscopy of Exoplanets using Cross-correlations and Supervised Machine Learning

2020 ◽  
Vol 159 (5) ◽  
pp. 192 ◽  
Author(s):  
Chloe Fisher ◽  
H. Jens Hoeijmakers ◽  
Daniel Kitzmann ◽  
Pablo Márquez-Neila ◽  
Simon L. Grimm ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Supervised Machine Learning ◽  
High Resolution Spectroscopy ◽  
Cross Correlations

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898 ◽  
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals High-resolution genomic comparisons within Salmonella enterica serotypes derived from beef feedlot cattle: parsing the roles of cattle source, pen, animal, sample type and production period

2021 ◽  
Author(s):  
Gizem Levent ◽  
Ashlynn Schlochtermeier ◽  
Samuel E. Ives ◽  
Keri N. Norman ◽  
Sara D. Lawhon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Population Dynamics ◽  
Antibiotic Resistance ◽  
High Resolution ◽  
Salmonella Enterica ◽  
Core Genome ◽  
Clonal Expansion ◽  
Feedlot Cattle ◽  
Supervised Machine Learning ◽  
Sample Type

Salmonella enterica is a major foodborne pathogen, and contaminated beef products have been identified as one of the primary sources of Salmonella-related outbreaks. Pathogenicity and antibiotic resistance of Salmonella are highly serotype- and subpopulation-specific, which makes it essential to understand high-resolution Salmonella population dynamics in cattle. Time of year, source of cattle, pen, and sample type(i.e., feces, hide or lymph nodes) have previously been identified as important factors influencing the serotype distribution of Salmonella (e.g., Anatum, Lubbock, Cerro, Montevideo, Kentucky, Newport, and Norwich) that were isolated from a longitudinal sampling design in a research feedlot. In this study, we performed high-resolution genomic comparisons of Salmonella isolates within each serotype using both single-nucleotide polymorphism (SNP)-based maximum likelihood phylogeny and hierarchical clustering of core-genome multi-locus sequence typing. The importance of the aforementioned features on clonal Salmonella expansion was further explored using a supervised machine learning algorithm. In addition, we identified and compared the resistance genes, plasmids, and pathogenicity island profiles of the isolates within each sub-population. Our findings indicate that clonal expansion of Salmonella strains in cattle was mainly influenced by the randomization of block and pen, as well as the origin/source of the cattle; that is, regardless of sampling time and sample type (i.e., feces, lymph node or hide). Further research is needed concerning the role of the feedlot pen environment prior to cattle placement to better understand carry-over contributions of existing strains of Salmonella and their bacteriophages. IMPORTANCE Salmonella serotypes isolated from outbreaks in humans can also be found in beef cattle and feedlots. Virulence factors and antibiotic resistance are among the primary defense mechanisms of Salmonella, and are often associated with clonal expansion. This makes understanding the subpopulation dynamics of Salmonella in cattle critical for effective mitigation. There remains a gap in the literature concerning subpopulation dynamics within Salmonella serotypes in feedlot cattle from the beginning of feeding up until slaughter. Here, we explore Salmonella population dynamics within each serotype using core genome phylogeny and hierarchical classifications. We used machine-learning to quantitatively parse the relative importance of both hierarchical and longitudinal clustering among cattle host samples. Our results reveal that Salmonella populations in cattle are highly clonal over a 6-month study period, and that clonal dissemination of Salmonella in cattle is mainly influenced spatially by experimental block and pen, as well by the geographical origin of the cattle.

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals High-resolution genomic comparisons within Salmonella enterica serotypes derived from beef feedlot cattle: parsing the roles of cattle source, pen, animal, sample type and production period

2020 ◽  
Author(s):  
Gizem Levent ◽  
Ashlynn Schlochtermeier ◽  
Samuel E. Ives ◽  
Keri N. Norman ◽  
Sara D. Lawhon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Population Dynamics ◽  
Antibiotic Resistance ◽  
High Resolution ◽  
Salmonella Enterica ◽  
Core Genome ◽  
Clonal Expansion ◽  
Feedlot Cattle ◽  
Supervised Machine Learning ◽  
Sample Type

AbstractSalmonella enterica is a major foodborne pathogen, and contaminated beef products have been identified as the primary source of Salmonella-related outbreaks. Pathogenicity and antibiotic resistance of Salmonella are highly serotype- and subpopulation-specific, which makes it essential to understand high-resolution Salmonella population dynamics in cattle. Time of year, source of cattle, pen, and sample type(i.e., feces, hide or lymph nodes) have previously been identified as important factors influencing the serotype distribution of Salmonella (e.g., Anatum, Lubbock, Cerro, Montevideo, Kentucky, Newport, and Norwich) that were isolated from a longitudinal sampling design in a research feedlot. In this study, we performed high-resolution genomic comparisons of Salmonella isolates within each serotype using both single-nucleotide polymorphism (SNP)-based maximum likelihood phylogeny and hierarchical clustering of core-genome multi-locus sequence typing. The importance of the aforementioned features on clonal Salmonella expansion was further explored using a supervised machine learning algorithm. In addition, we identified and compared the resistance genes, plasmids, and pathogenicity island profiles of the isolates within each sub-population. Our findings indicate that clonal expansion of Salmonella strains in cattle was mainly influenced by the randomization of block and pen, as well as the origin/source of the cattle; that is, regardless of sampling time and sample type (i.e., feces, lymph node or hide). Further research is needed concerning the role of the feedlot pen environment prior to cattle placement to better understand carry-over contributions of existing strains of Salmonella and their bacteriophages.ImportanceSalmonella serotypes isolated from outbreaks in humans can also be found in beef cattle and feedlots. Virulence factors and antibiotic resistance are among the primary defense mechanisms of Salmonella, and are often associated with clonal expansion. This makes understanding the subpopulation dynamics of Salmonella in cattle critical for effective mitigation. There remains a gap in the literature concerning subpopulation dynamics within Salmonella serotypes in feedlot cattle from the beginning of feeding up until slaughter. Here, we explore Salmonella population dynamics within each serotype using core genome phylogeny and hierarchical classifications. We used machine-learning to quantitatively parse the relative importance of both hierarchical and longitudinal clustering among cattle host samples. Our results reveal that Salmonella populations in cattle are highly clonal over a 6-month study period, and that clonal dissemination of Salmonella in cattle is mainly influenced spatially by experimental block and pen, as well by the geographical origin of the cattle.

scholarly journals Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model

2021 ◽  
Vol 11 (13) ◽  
pp. 6072
Author(s):  
Nicla Maria Notarangelo ◽  
Arianna Mazzariello ◽  
Raffaele Albano ◽  
Aurelia Sole
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Urban Morphology ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Surface Model ◽  
Feature Descriptor ◽  
Digital Surface Model ◽  
Building Extraction ◽  
Area Of Interest

Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86).

Sign in / Sign up

Export Citation Format

Share Document