scholarly journals Combining Deep Learning and Location-Based Ranking for Large-Scale Archaeological Prospection of LiDAR Data from The Netherlands

2020 ◽  
Vol 9 (5) ◽  
pp. 293 ◽  
Author(s):  
Wouter B. Verschoof-van der Vaart ◽  
Karsten Lambers ◽  
Wojtek Kowalczyk ◽  
Quentin P.J. Bourgeois
Keyword(s):  
Deep Learning ◽  
The Netherlands ◽  
Large Scale ◽  
Human Performance ◽  
Research Area ◽  
Lidar Data ◽  
Random Testing ◽  
Archaeological Prospection ◽  
Testing Dataset ◽  
Novel Approach

This paper presents WODAN2.0, a workflow using Deep Learning for the automated detection of multiple archaeological object classes in LiDAR data from the Netherlands. WODAN2.0 is developed to rapidly and systematically map archaeology in large and complex datasets. To investigate its practical value, a large, random test dataset—next to a small, non-random dataset—was developed, which better represents the real-world situation of scarce archaeological objects in different types of complex terrain. To reduce the number of false positives caused by specific regions in the research area, a novel approach has been developed and implemented called Location-Based Ranking. Experiments show that WODAN2.0 has a performance of circa 70% for barrows and Celtic fields on the small, non-random testing dataset, while the performance on the large, random testing dataset is lower: circa 50% for barrows, circa 46% for Celtic fields, and circa 18% for charcoal kilns. The results show that the introduction of Location-Based Ranking and bagging leads to an improvement in performance varying between 17% and 35%. However, WODAN2.0 does not reach or exceed general human performance, when compared to the results of a citizen science project conducted in the same research area.

scholarly journals Optimizing clinical trials recruitment via deep learning

2019 ◽  
Vol 26 (11) ◽  
pp. 1195-1202 ◽  
Author(s):  
Jelena Gligorijevic ◽  
Djordje Gligorijevic ◽  
Martin Pavlovski ◽  
Elizabeth Milkovits ◽  
Lucas Glass ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Substantial Improvement ◽  
Novel Approach ◽  
Industry Standards ◽  
Human Participants ◽  
New Treatments ◽  
The Cost

Abstract Objective Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive process where investigators aim to enroll volunteers with predetermined characteristics, administer treatment(s), and collect safety and efficacy data. Therefore, choosing top-enrolling investigators is essential for efficient clinical trial execution and is 1 of the primary drivers of drug development cost. Materials and Methods To facilitate clinical trials optimization, we propose DeepMatch (DM), a novel approach that builds on top of advances in deep learning. DM is designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials. Results Large-scale evaluation conducted on 2618 studies provides evidence that the proposed ranking-based framework improves the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. Discussion The extensive experimental section suggests that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators. Conclusion Due to the great significance of the problem at hand, related research efforts are set to shift the paradigm of how investigators are chosen for clinical trials, thereby optimizing and automating them and reducing the cost of new therapies.

scholarly journals Automation of Leaf Counting in Maize and Sorghum Using Deep Learning

2020 ◽  
Author(s):  
Chenyong Miao ◽  
Alice Guo ◽  
Addie M. Thompson ◽  
Jinliang Yang ◽  
Yufeng Ge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Human Performance ◽  
Performance Metrics ◽  
Model Performance ◽  
Image Data ◽  
Individual Plant ◽  
Multiple Time ◽  
Leaf Emergence ◽  
Lower Accuracy

ABSTRACTLeaf number and leaf emergence rate are phenotypes of interest to plant breeders, plant geneticists, and crop modelers. Counting the extant leaves of an individual plant is straightforward even for an untrained individual, but manually tracking changes in leaf numbers for hundreds of individuals across multiple time points is logistically challenging. This study generated a dataset including over 150,000 maize and sorghum images for leaf counting projects. A subset of 17,783 images also includes annotations of the positions of individual leaf tips. With these annotated images, we evaluate two deep learning-based approaches for automated leaf counting: the first based on counting-by-regression from whole image analysis and a second based on counting-by-detection. Both approaches can achieve RMSE (root of mean square error) smaller than one leaf, only moderately inferior to the RMSE between human annotators of between 0.57 and 0.73 leaves. The counting-by-regression approach based on CNNs (convolutional neural networks) exhibited lower accuracy and increased bias for plants with extreme leaf numbers which are underrepresented in this dataset. The counting-by-detection approach based on Faster R-CNN object detection models achieve near human performance for plants where all leaf tips are visible. The annotated image data and model performance metrics generated as part of this study provide large scale resources for the comparison and improvement of algorithms for leaf counting from image data in grain crops.

New deep-learning based approaches for forest landscape modeling

2021 ◽  
Author(s):  
Werner Rammer ◽  
Rupert Seidl
Keyword(s):  
Deep Learning ◽  
Vegetation Dynamics ◽  
Large Scale ◽  
Spatial Scales ◽  
Environmental Drivers ◽  
Conceptual Approach ◽  
Vegetation Development ◽  
Novel Approach ◽  
Process Understanding ◽  
Forest Landscape Modeling

<p>In times of rapid global change, the ability to faithfully predict the development of vegetation on larger scales is of key relevance to society. However, ecosystem models that incorporate enough process understanding for being applicable under future and non-analog conditions are often restricted to finer spatial scales due to data and computational constraints. Recent breakthroughs in machine learning, particularly in the field of deep learning, allow bridging this scale mismatch by providing new means for analyzing data, e.g., in remote sensing, but also new modelling approaches. We here present a novel approach for Scaling Vegetation Dynamics (SVD) which uses a deep neural network for predicting large-scale vegetation development. In a first step, the network learns its representation of vegetation dynamics as a function of current vegetation state and environmental drivers from process-based models and empirical data. The trained model is then used within of a dynamic simulation on large spatial scales. In this contribution we introduce the conceptual approach of SVD and show results for example applications in Europe and the US. More broadly we discuss aspects of applying deep learning in the context of ecological modeling.</p>

scholarly journals Applying Deep Learning Technique for Depression Classification in Social Media Text

2020 ◽  
Vol 10 (10) ◽  
pp. 2446-2451
Author(s):  
Hussain Ahmad ◽  
Muhammad Zubair Asghar ◽  
Fahad M. Alotaibi ◽  
Ibrahim A. Hameed
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Large Scale ◽  
Research Area ◽  
Mental Illnesses ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Close Relationship ◽  
Social Media Platforms

In social media, depression identification could be regarded as a complex task because of the complicated nature associated with mental disorders. In recent times, there has been an evolution in this research area with growing popularity of social media platforms as these have become a fundamental part of people's day-to-day life. Social media platforms and their users share a close relationship due to which the users' personal life is reflected in these platforms on several levels. Apart from the associated complexity in recognising mental illnesses via social media platforms, implementing supervised machine learning approaches like deep neural networks is yet to be adopted in a large scale because of the inherent difficulties associated with procuring sufficient quantities of annotated training data. Because of such reasons, we have made effort to identify deep learning model that is most effective from amongst selected architectures with previous successful record in supervised learning methods. The selected model is employed to recognise online users that display depression; since there is limited unstructured text data that could be extracted from Twitter.

scholarly journals The interactions between Quaternary Geology and Archaeology

2010 ◽  
Vol 89 (1) ◽  
pp. 1-2
Author(s):  
Wim Z. Hoek
Keyword(s):  
Climate Change ◽  
The Netherlands ◽  
Large Scale ◽  
Environmental Changes ◽  
Late Quaternary ◽  
Quaternary Geology ◽  
Archaeological Prospection ◽  
Quaternary Climate ◽  
National Representation ◽  
Environmental Setting

This special issue focuses on the interactions between Quaternary Geology and Archaeology and results from the INQUA-NL Symposium: ‘Late Quaternary Climate Change: a Human Perspective’ held on April 14th2009, KNAW Trippenhuis, Amsterdam. The symposium was attended by over 125 scientists and students with interest in the fields of Quaternary Geology and Archaeology. The symposium was organized for the INQUA Netherlands commission (INQUA-NL) by Wim Hoek, Faculty of Geosciences, Utrecht University (UU), Henry Hooghiemstra, Faculty of Science, University of Amsterdam (UvA), and Jos Deeben, National Service for Cultural Heritage (RCE). The INQUA Netherlands commission (INQUA-NL) is the Netherlands' national representation in INQUA, the International Union for Quaternary Research (see furtherwww.geo.uu.nl/inqua-nl).The Netherlands is a country made by humans, but before the large-scale impact of humans that formed the typical Dutch landscape, people inhabited our area and needed to be able to deal with natural disasters like climate change, river floods or sea-level change. In the field of archaeology, there is increasingly more space to include the environmental changes that partly determined the behaviour of prehistoric communities. The interactions between quaternary geology and archaeology are not only restricted to provide stratigraphical information during archaeological prospection or on exposures during excavations. Quaternary geology is increasingly applied to gain insight in the landscape development and environmental setting where people have lived in the past. Above this, predictive models can be improved by the interaction of archaeological and geological/palaeogeographical research (see also Deeben et al., 2010).

scholarly journals Advances in Deep Learning-Based Medical Image Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoqing Liu ◽  
Kunlun Gao ◽  
Bo Liu ◽  
Chengwei Pan ◽  
Kongming Liang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Research Area ◽  
Clinical Applications ◽  
Learning Technologies ◽  
Small Scale ◽  
Great Success

Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions. Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors. Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.

scholarly journals Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

2021 ◽  
Vol 13 (24) ◽  
pp. 5113
Author(s):  
Elias Ayrey ◽  
Daniel J. Hayes ◽  
John B. Kilbride ◽  
Shawn Fraver ◽  
John A. Kershaw ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Regional Scale ◽  
Lidar Data ◽  
Stem Density ◽  
Calibration Data ◽  
Eastern United States ◽  
Forest Inventories ◽  
Public Data ◽  
Forest Attributes

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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