Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Road Extraction from Remotely Sensed Data: A Review

Proceedings of Intelligent Computing and Technologies Conference ◽

10.21467/proceedings.115.14 ◽

2021 ◽

Author(s):

Mohd Jawed Khan ◽

Pankaj Pratap Singh

Keyword(s):

Deep Learning ◽

Traffic Management ◽

Remotely Sensed ◽

Future Research ◽

Learning Approaches ◽

Road Extraction ◽

Remotely Sensed Data ◽

Remotely Sensed Imagery ◽

Urban Rural ◽

Future Research Directions

Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

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A Survey of Graphical Page Object Detection with Deep Neural Networks

Applied Sciences ◽

10.3390/app11125344 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5344

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

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Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection

Remote Sensing ◽

10.3390/rs11070794 ◽

2019 ◽

Vol 11 (7) ◽

pp. 794 ◽

Cited By ~ 27

Author(s):

Karsten Lambers ◽

Wouter Verschoof-van der Vaart ◽

Quentin Bourgeois

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Object Detection ◽

Citizen Science ◽

Remotely Sensed ◽

Training Data ◽

Remotely Sensed Data ◽

Study Region ◽

Archaeological Prospection ◽

Archaeological Object

Although the history of automated archaeological object detection in remotely sensed data is short, progress and emerging trends are evident. Among them, the shift from rule-based approaches towards machine learning methods is, at the moment, the cause for high expectations, even though basic problems, such as the lack of suitable archaeological training data are only beginning to be addressed. In a case study in the central Netherlands, we are currently developing novel methods for multi-class archaeological object detection in LiDAR data based on convolutional neural networks (CNNs). This research is embedded in a long-term investigation of the prehistoric landscape of our study region. We here present an innovative integrated workflow that combines machine learning approaches to automated object detection in remotely sensed data with a two-tier citizen science project that allows us to generate and validate detections of hitherto unknown archaeological objects, thereby contributing to the creation of reliable, labeled archaeological training datasets. We motivate our methodological choices in the light of current trends in archaeological prospection, remote sensing, machine learning, and citizen science, and present the first results of the implementation of the workflow in our research area.

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Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

Remote Sensing ◽

10.3390/rs11030284 ◽

2019 ◽

Vol 11 (3) ◽

pp. 284 ◽

Cited By ~ 1

Author(s):

Linglin Zeng ◽

Shun Hu ◽

Daxiang Xiang ◽

Xiang Zhang ◽

Deren Li ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Regional Scale ◽

Remotely Sensed ◽

Temporal Variations ◽

Training Data ◽

Estimation Accuracy ◽

Learning Approaches ◽

Remotely Sensed Data ◽

Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

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Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

Natural Language Engineering ◽

10.1017/s1351324920000352 ◽

2020 ◽

pp. 1-21 ◽

Cited By ~ 2

Author(s):

Clément Dalloux ◽

Vincent Claveau ◽

Natalia Grabar ◽

Lucas Emanuel Silva Oliveira ◽

Claudia Maria Cabral Moro ◽

...

Keyword(s):

Machine Learning ◽

Information Extraction ◽

State Of The Art ◽

Automatic Detection ◽

Brazilian Portuguese ◽

Supervised Machine Learning ◽

Biomedical Domain ◽

Learning Approaches ◽

Cross Domain ◽

Automatic Methods

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.

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Deep learning approaches for speech emotion recognition: state of the art and research challenges

Multimedia Tools and Applications ◽

10.1007/s11042-020-09874-7 ◽

2021 ◽

Author(s):

Rashid Jahangir ◽

Ying Wah Teh ◽

Faiqa Hanif ◽

Ghulam Mujtaba

Keyword(s):

Deep Learning ◽

Emotion Recognition ◽

State Of The Art ◽

Speech Emotion Recognition ◽

Learning Approaches ◽

Research Challenges

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Covid-19 detection via deep neural network and occlusion sensitivity maps

10.36227/techrxiv.14100890 ◽

2021 ◽

Author(s):

Noor Ahmad ◽

Muhammad Aminu ◽

Mohd Halim Mohd Noor

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Color Images ◽

Fine Tuning ◽

Training Dataset ◽

Learning Approaches ◽

Learning Models ◽

Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

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