scholarly journals Automatic Extraction of Logical Web Lists

2014 ◽  
pp. 365-374 ◽  
Author(s):  
Pasqua Fabiana Lanotte ◽  
Fabio Fumarola ◽  
Michelangelo Ceci ◽  
Andrea Scarpino ◽  
Michele Damiano Torelli ◽  
...  
Keyword(s):  
Automatic Extraction

Automatic Extraction of Acronyms from Text

2020 ◽  
Author(s):  
Stuart Yeates
Keyword(s):  
Digital Library ◽  
False Positives ◽  
Automatic Extraction ◽  
False Negatives ◽  
Library Research ◽  
Communications Theory ◽  
Textual Content

A brief introduction to acronyms is given and motivation for extracting them in a digital library environment is discussed. A technique for extracting acronyms is given with an analysis of the results. The technique is found to have a low number of false negatives and a high number of false positives. Introduction Digital library research seeks to build tools to enable access of content, while making as few as possible assumptions about the content, since assumptions limit the range of applicability of the tools. Generally, the broader the assumptions the more widely applicable the tools. For example, keyword based indexing [5] is based on communications theory and applies to all natural human textual languages (allowances for differences in character sets and similar localisation issues not withstanding) . The algorithm described in this paper makes much stronger assumptions about the content. It assumes textual content that contains acronyms, an assumption which is known to hold for...

An Automatic Extraction Approach to Fractional Vegetation Cover of Saline Land with Digital Images

2013 ◽  
Vol 15 (4) ◽  
pp. 618 ◽  
Author(s):  
Yanling DING ◽  
Kai ZHAO ◽  
Xiaofeng LI ◽  
Xingming ZHENG
Keyword(s):  
Vegetation Cover ◽  
Digital Images ◽  
Automatic Extraction ◽  
Fractional Vegetation Cover ◽  
Saline Land

Towards Automatic Extraction of Prosodic Patterns for Speech Synthesis

2014 ◽  
Author(s):  
Mónica Domínguez ◽  
Mireia Farrús ◽  
Alicia Burga ◽  
Leo Wanner
Keyword(s):  
Speech Synthesis ◽  
Automatic Extraction

RESEARCH ON AUTOMATIC EXTRACTION OF DEFOCUS IMAGES IN UAV AERIAL IMAGES

2019 ◽  
Vol 75 (2) ◽  
pp. I_106-I_113
Author(s):  
Etsuji KITAGAWA ◽  
Ryo KATO ◽  
Satoshi ABIKO ◽  
Takumi TSUMURA ◽  
Yusuke NAKATANI
Keyword(s):  
Aerial Images ◽  
Automatic Extraction

scholarly journals MAT: GIS-Based Morphometry Assessment Tools for Concave Landforms

2021 ◽  
Vol 13 (14) ◽  
pp. 2810
Author(s):  
Joanna Gudowicz ◽  
Renata Paluszkiewicz
Keyword(s):  
Rapid Development ◽  
Assessment Tools ◽  
Lidar Data ◽  
Automatic Extraction ◽  
Sensing Technology ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Morphometric Assessment ◽  
Morphometric Evaluation

The rapid development of remote sensing technology for obtaining high-resolution digital elevation models (DEMs) in recent years has made them more and more widely available and has allowed them to be used for morphometric assessment of concave landforms, such as valleys, gullies, glacial cirques, sinkholes, craters, and others. The aim of this study was to develop a geographic information systems (GIS) toolbox for the automatic extraction of 26 morphometric characteristics, which include the geometry, hypsometry, and volume of concave landforms. The Morphometry Assessment Tools (MAT) toolbox in the ArcGIS software was developed. The required input data are a digital elevation model and the form boundary as a vector layer. The method was successfully tested on an example of 21 erosion-denudation valleys located in the young glacial area of northwest Poland. Calculations were based on elevation data collected in the field and LiDAR data. The results obtained with the tool showed differences in the assessment of the volume parameter at the average level of 12%, when comparing the field data and LiDAR data. The algorithm can also be applied to other types of concave forms, as well as being based on other DEM data sources, which makes it a universal tool for morphometric evaluation.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

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