scholarly journals Application of GIS, GPS, Remote Sensing Technologies and Virtual Reality in Environmental Planning

2021 ◽  
Vol 7 (4) ◽  
pp. 96
Author(s):  
Bing Zhao
Keyword(s):  
Remote Sensing ◽  
Virtual Reality ◽  
Virtual Environment ◽  
Large Scale ◽  
Environmental Planning ◽  
Ideal Space ◽  
Surface Processing ◽  
Social And Economic Development ◽  
3S Technologies ◽  
Processing Line

<span lang="EN-US">Environmental planning is a systematic work with large-scale issues and big difficulties. This article briefly discusses GIS, GPS, remote sensing technologies (3S technologies) and virtual reality (VR), and verifies that these technologies can provide assistance for environmental planning. On this basis, this article focuses on the application of 3S technologies and VR in environmental planning, such as point processing, line processing and surface processing, constructing geometric virtual environment and image virtual environment. In order to maximize roles of these technologies, advantages, scientificity and rationality of environmental planning should be enhanced to create ideal space for social and economic development.</span>

Visiting Tourist Landmarks in Virtual Reality Systems by Real-Walking

2010 ◽  
pp. 180-193 ◽  
Author(s):  
F. Steinicke ◽  
G. Bruder ◽  
J. Jerald ◽  
H. Frenz
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Virtual Environment ◽  
User Interfaces ◽  
Large Scale ◽  
Three Dimensional ◽  
Interaction Space ◽  
Immersive Virtual Environments ◽  
Redirected Walking ◽  
Virtual City

In recent years virtual environments (VEs) have become more and more popular and widespread due to the requirements of numerous application areas in particular in the 3D city visualization domain. Virtual reality (VR) systems, which make use of tracking technologies and stereoscopic projections of three-dimensional synthetic worlds, support better exploration of complex datasets. However, due to the limited interaction space usually provided by the range of the tracking sensors, users can explore only a portion of the virtual environment (VE). Redirected walking allows users to walk through large-scale immersive virtual environments (IVEs) such as virtual city models, while physically remaining in a reasonably small workspace by intentionally injecting scene motion into the IVE. With redirected walking users are guided on physical paths that may differ from the paths they perceive in the virtual world. The authors have conducted experiments in order to quantify how much humans can unknowingly be redirected. In this chapter they present the results of this study and the implications for virtual locomotion user interfaces that allow users to view arbitrary real world locations, before the users actually travel there in a natural environment.

scholarly journals Development of 3D workspace and methodology for assessing operator’s functional state during the work in virtual environment

2021 ◽  
Vol 330 ◽  
pp. 03006
Author(s):  
Mikhail Nikitenko ◽  
Sergey Kizilov ◽  
Irina Tarasova ◽  
Alla Ignatova ◽  
Elena Natura
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Functional State ◽  
Large Scale ◽  
Data Sources ◽  
Information Space ◽  
Research Project ◽  
Psychophysiological State ◽  
Software And Hardware ◽  
Immersive Technologies

The article presents the current results of a research project aimed at developing software and hardware solutions that optimize an operation of dispatch services based on immersive technologies. The results of the virtual operator’s office creation for the subsequent program-protocol communication of the displayed information with the data sources of the dispatching object are presented. The virtual workspace is implemented in the form of large-scale objects, textures, lighting, etc. “animated” in virtual reality glasses. The results of the initial approbation of the methodology for assessing the psychophysiological state of operators during virtualization of the working information space are also presented in framework of multistage studies of effects of virtual reality on the user.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

scholarly journals Rear-Seat Productivity in Virtual Reality: Investigating VR Interaction in the Confined Space of a Car

2021 ◽  
Vol 5 (4) ◽  
pp. 15
Author(s):  
Jingyi Li ◽  
Ceenu George ◽  
Andrea Ngao ◽  
Kai Holländer ◽  
Stefan Mayer ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Physical Space ◽  
Rear Seat ◽  
Virtual Space ◽  
Future Research ◽  
Confined Space ◽  
Physical Restraints ◽  
Working Environments ◽  
Research And Design

Ubiquitous technology lets us work in flexible and decentralised ways. Passengers can already use travel time to be productive, and we envision even better performance and experience in vehicles with emerging technologies, such as virtual reality (VR) headsets. However, the confined physical space constrains interactions while the virtual space may be conceptually borderless. We therefore conducted a VR study (N = 33) to examine the influence of physical restraints and virtual working environments on performance, presence, and the feeling of safety. Our findings show that virtual borders make passengers touch the car interior less, while performance and presence are comparable across conditions. Although passengers prefer a secluded and unlimited virtual environment (nature), they are more productive in a shared and limited one (office). We further discuss choices for virtual borders and environments, social experience, and safety responsiveness. Our work highlights opportunities and challenges for future research and design of rear-seat VR interaction.

scholarly journals A novel remote sensing ecological vulnerability index on large scale: A case study of the China-Pakistan Economic Corridor region

2021 ◽  
Vol 129 ◽  
pp. 107955
Author(s):  
Hongwei Wu ◽  
Bing Guo ◽  
Junfu Fan ◽  
Fei Yang ◽  
Baomin Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vulnerability Index ◽  
Ecological Vulnerability

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Low-Asymmetry Interface for Multiuser VR Experiences with Both HMD and Non-HMD Users

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 397
Author(s):  
Qimeng Zhang ◽  
Ji-Su Ban ◽  
Mingyu Kim ◽  
Hae Won Byun ◽  
Chang-Hun Kim
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Mobile Device ◽  
User Satisfaction ◽  
Virtual World ◽  
User Study ◽  
Visual Display ◽  
Point Of View ◽  
Head Mounted Display ◽  
And Behavior

We propose a low-asymmetry interface to improve the presence of non-head-mounted-display (non-HMD) users in shared virtual reality (VR) experiences with HMD users. The low-asymmetry interface ensures that the HMD and non-HMD users’ perception of the VR environment is almost similar. That is, the point-of-view asymmetry and behavior asymmetry between HMD and non-HMD users are reduced. Our system comprises a portable mobile device as a visual display to provide a changing PoV for the non-HMD user and a walking simulator as an in-place walking detection sensor to enable the same level of realistic and unrestricted physical-walking-based locomotion for all users. Because this allows non-HMD users to experience the same level of visualization and free movement as HMD users, both of them can engage as the main actors in movement scenarios. Our user study revealed that the low-asymmetry interface enables non-HMD users to feel a presence similar to that of the HMD users when performing equivalent locomotion tasks in a virtual environment. Furthermore, our system can enable one HMD user and multiple non-HMD users to participate together in a virtual world; moreover, our experiments show that the non-HMD user satisfaction increases with the number of non-HMD participants owing to increased presence and enjoyment.

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