Object-Based Simulators for Large Scale Distributed Virtual Environment

Peer-to-peer (P2P) architectures have been proposed as an efficient and truly scalable solution for distributed virtual environments (DVEs). However, heavy and unbalanced network load has restricted the development of large scale DVEs. To solve this problem, this paper attempts to apply the mobile agent technology in DVEs. First, the virtual environment space was divided into a number of adjacent sub-spaces. Then, using the agent mobility, entities models moved themselves to the adjacent sub-space, and completed interactions with other entities in the sub-space. As a result, a significant part network load is transformed into local calculation load. The theoretical analysis results show that it is feasible and effective to ease the network communications bottleneck in the expansion of the DVEs.

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A Large Scale Distributed Virtual Environment Architecture

Studies in Informatics and Control ◽

10.24846/v24i2y201504 ◽

2015 ◽

Vol 24 (2) ◽

Author(s):

Mohd Sapiyan BABA ◽

Tutut HERAWAN

Keyword(s):

Virtual Environment ◽

Large Scale ◽

Distributed Virtual Environment

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1P1O: A Large Scale Distributed Virtual Environment

Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-13-1799-6_3 ◽

2019 ◽

pp. 21-29 ◽

Cited By ~ 1

Author(s):

Elfizar ◽

Mohd Sapiyan Baba ◽

Tutut Herawan

Keyword(s):

Virtual Environment ◽

Large Scale ◽

Distributed Virtual Environment

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Distributed virtual environment realizing collaborative environmental education

ACM SIGCUE Outlook ◽

10.1145/504546.504548 ◽

2001 ◽

Vol 27 (3) ◽

pp. 16-26 ◽

Cited By ~ 1

Author(s):

Masaya Okada ◽

Hiroyuki Tarumi ◽

Tetsuhiko Yoshimura ◽

Kazuyuki Moriya

Keyword(s):

Environmental Education ◽

Virtual Environment ◽

Distributed Virtual Environment

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Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

Remote Sensing ◽

10.3390/rs13030364 ◽

2021 ◽

Vol 13 (3) ◽

pp. 364

Author(s):

Han Gao ◽

Jinhui Guo ◽

Peng Guo ◽

Xiuwan Chen

Keyword(s):

Deep Learning ◽

Land Cover ◽

Spatial Resolution ◽

Large Scale ◽

High Spatial Resolution ◽

Training Data ◽

Aerial Images ◽

Rural Landscapes ◽

Feature Representations ◽

Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

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Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Remote Sensing ◽

10.3390/rs12010096 ◽

2019 ◽

Vol 12 (1) ◽

pp. 96 ◽

Cited By ~ 6

Author(s):

James Brinkhoff ◽

Justin Vardanega ◽

Andrew J. Robson

Keyword(s):

Land Cover ◽

Large Scale ◽

Satellite Image ◽

Resource Planning ◽

Support Vector ◽

Perennial Crop ◽

Perennial Crops ◽

Object Based ◽

Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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ALVIC versus the Internet: Redesigning a Networked Virtual Environment Architecture

International Journal of Computer Games Technology ◽

10.1155/2008/594313 ◽

2008 ◽

Vol 2008 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Peter Quax ◽

Jeroen Dierckx ◽

Bart Cornelissen ◽

Wim Lamotte

Keyword(s):

Virtual Environment ◽

Large Scale ◽

Virtual Communities ◽

Real Life ◽

Point Of View ◽

The Internet ◽

Generic Framework ◽

Short Period ◽

Networked Virtual Environment ◽

Near Future

The explosive growth of the number of applications based on networked virtual environment technology, both games and virtual communities, shows that these types of applications have become commonplace in a short period of time. However, from a research point of view, the inherent weaknesses in their architectures are quickly exposed. The Architecture for Large-Scale Virtual Interactive Communities (ALVICs) was originally developed to serve as a generic framework to deploy networked virtual environment applications on the Internet. While it has been shown to effectively scale to the numbers originally put forward, our findings have shown that, on a real-life network, such as the Internet, several drawbacks will not be overcome in the near future. It is, therefore, that we have recently started with the development of ALVIC-NG, which, while incorporating the findings from our previous research, makes several improvements on the original version, making it suitable for deployment on the Internet as it exists today.

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