scholarly journals APPROACH TO CONSTRUCTING 3D VIRTUAL SCENE OF IRRIGATION AREA USING MULTI-SOURCE DATA

2015 ◽  
Vol II-2/W2 ◽  
pp. 227-233
Author(s):  
S. Cheng ◽  
M. Dou ◽  
J. Wang ◽  
S. Zhang ◽  
X. Chen
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Research Direction ◽  
Data Representation ◽  
Google Earth ◽  
Future Research ◽  
3D Gis ◽  
Irrigation Area ◽  
Virtual Scene ◽  
Source Data

For an irrigation area that is often complicated by various 3D artificial ground features and natural environment, disadvantages of traditional 2D GIS in spatial data representation, management, query, analysis and visualization is becoming more and more evident. Building a more realistic 3D virtual scene is thus especially urgent for irrigation area managers and decision makers, so that they can carry out various irrigational operations lively and intuitively. Based on previous researchers' achievements, a simple, practical and cost-effective approach was proposed in this study, by adopting3D geographic information system (3D GIS), remote sensing (RS) technology. Based on multi-source data such as Google Earth (GE) high-resolution remote sensing image, ASTER G-DEM, hydrological facility maps and so on, 3D terrain model and ground feature models were created interactively. Both of the models were then rendered with texture data and integrated under ArcGIS platform. A vivid, realistic 3D virtual scene of irrigation area that has a good visual effect and possesses primary GIS functions about data query and analysis was constructed.Yet, there is still a long way to go for establishing a true 3D GIS for the irrigation are: issues of this study were deeply discussed and future research direction was pointed out in the end of the paper.

scholarly journals A REVIEW OF 3D GIS FOR USE IN CREATING VIRTUAL HISTORIC DUBLIN

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 249-254
Author(s):  
A. Chenaux ◽  
M. Murphy ◽  
S. Pavia ◽  
S. Fai ◽  
T. Molnar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Complex Analysis ◽  
Remote Sensing Data ◽  
Public Works ◽  
3D Gis ◽  
Building Information ◽  
Urban Rural ◽  
Interactive 3D ◽  
Under Construction

<p><strong>Abstract.</strong> This paper illustrates how BIM integration with GIS is approached as part of the workflow in creating Virtual Historic Dublin. A design for a WEB based interactive 3D model of historic buildings and centres in Dublin City (Virtual Historic Dublin City) paralleling smart city initiates is now under construction and led by the National Monuments at the Office of Public Works in Ireland. The aim is to facilitate the conservation and maintenance of historic infrastructure and fabric and the dissemination of knowledge for education and cultural tourism using an extensive Historic Building Information Model. Remote sensing data is now processed with greater ease to create 3D intelligent models in Historic BIM. While the use of remote sensing, HBIM and game engine platforms are the main applications used at present, 3D GIS has potential to form part of the workflow for developing the Virtual Historic City. 2D GIS is now being replaced by 3D spatial data allowing more complex analysis to be carried out, 3D GIS can define and depict buildings, urban rural centres in relation to their geometry topological, semantic and visualisation properties. The addition of semantic attributes allows complex analysis and 3D spatial queries for modelling city and urban elements. This analysis includes fabric and structural elements of buildings, relief, vegetation, transportation, water bodies, city furniture and land use.</p>

Geographic Space Ontology, Locus-Object, and Spatial Data Representation Semantic Theory

2014 ◽  
pp. 28-52
Author(s):  
Sébastien Gadal
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
General Framework ◽  
Data Representation ◽  
Semantic Theory ◽  
Semantic Concept ◽  
Geographic Space ◽  
The Earth ◽  
Geographic Object

Within the general framework of the analysis of the geographic space and its ontological components by remote sensing, the author explores the ability of the morphogenetic modeling in the recognition of one major ontological and semantic concept of geography: the “locus-object.” The “locus-object” couple concept results from the interrelation formalization between the geographic locus, the geographic object, and the geo-localization notions. Geographic loci and objects are linked and both geo-localized. The links and relations between locus and object are mathematically formalized by geospatiology, the study of the logical role of space in the study of entities on the surface of the Earth. Morphogenetic modeling recognizes the loci of the geographic space by spatial discontinuities detection. The spatial discontinuities allow the identification of the types of spatial differentiations (boundaries, limits) between two geographic entities. The concept of “locus-object” is one of the key conceptual ontological elements of the geographic space.

Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

scholarly journals CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 553-557
Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Data Representation ◽  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

Independent Component Analysis and its Applications to Manufacturing Problems

2011 ◽  
pp. 316-344
Author(s):  
Xian-Chuan Yu ◽  
Ting Zhang ◽  
Li-Bo Zhang ◽  
Hui He ◽  
Wei Zou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Independent Component Analysis ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Mineral Resources ◽  
Component Analysis ◽  
Independent Component ◽  
Background Information ◽  
Future Research ◽  
Remote Sensing Imagery

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are as independent as possible. In this chapter, we introduce the background information, the theory of ICA, and present several common algorithms such as fast ICA, kernel ICA, and constrained ICA. It is first applied to mineral resources prediction and remote sensing imagery, while traditional methods cannot satisfy the complexity of the spatial data (prospecting geochemistry data, remote sensing data, etc.). In application cases, ICA is applied to analyze the spatial data in some districts of China. The result shows that some independent elements accord with the practical distribution better than conventional methods. Moreover, ICA can get rid of the various kinds of correlations in remote sensing imagery effectively and improve the classification accuracy. However, this method also has some limitations. At last, we list the future research directions of our work.

scholarly journals Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review

2020 ◽  
Vol 12 (9) ◽  
pp. 1444 ◽  
Author(s):  
Abolfazl Abdollahi ◽  
Biswajeet Pradhan ◽  
Nagesh Shukla ◽  
Subrata Chakraborty ◽  
Abdullah Alamri
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Traffic Management ◽  
Google Earth ◽  
Future Research ◽  
Learning Approaches ◽  
Road Extraction ◽  
Research Subjects ◽  
Remote Sensing Images ◽  
Generative Adversarial Network

One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.

ECONOMIC GROWTH ANALYSIS: A STUDY ON MALAYSIAN MARKET

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Suhaily Maizan Abdul Manaf ◽  
Shuhada Mohamed Hamidi ◽  
Nur Shafini Mohd Said ◽  
Siti Rapidah Omar Ali ◽  
Nur Dalila Adenan
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Direct Investment ◽  
Inflation Rate ◽  
Government Expenditure ◽  
Correlation Method ◽  
Research Direction ◽  
Least Square ◽  
Future Research ◽  
Internal And External Factors

Economic performance of a country is mostly determined by the growth and any other internal and external factors. In this study, researchers purposely focused on Malaysian market by examining the relationship between export, inflation rate, government expenditure and foreign direct investment towards economic growth in Malaysia by applying the yearly data of 47 years from 1970 to 2016 using descriptive statistics, regression model and correlation method analysis. By applying Ordinary Least Square (OLS) method, the result suggests that export, government expenditure and foreign direct investment are positively and significantly correlated with the economic growth. However, inflation rate has negative and insignificant relationship with the economic growth. The outcome of the study is suggested to be useful in providing the future research direction towards the economic growth in Malaysia. Keywords: economic growth; export; inflation rate; government expenditure

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