scholarly journals Method to Generate Building Damage Maps by Combining Aerial Image Processing and Crowdsourcing

2021 ◽  
Vol 16 (5) ◽  
pp. 827-839
Author(s):  
Hidehiko Shishido ◽  
◽  
Koyo Kobayashi ◽  
Yoshinari Kameda ◽  
Itaru Kitahara
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Information Source ◽  
Building Damage ◽  
Aerial Image ◽  
Information Collection ◽  
Essential Information ◽  
Disaster Information ◽  
Damage Maps ◽  
Damage Map

Building damage maps that show the damage status of buildings are an essential information source for various disaster countermeasures, such as evacuation, rescue, and reconstruction. Therefore, they must be generated as quickly as possible. However, to generate a building damage map, it is necessary to collect disaster information and estimate the damage situation over a wide area, which is time consuming. (In this paper, we consider disaster information collection as capturing aerial images.) In recent years, crowdsourcing has been widely used to understand the damage situation. Crowdsourcing achieves large-scale work by dividing it into microtasks that can be solved by anyone and by distributing the microtasks among an unspecified number of workers. We believe that crowdsourcing is suitable for gathering information and assessing damage situations as it can adjust the type and number of workers in a scalable manner and allocate resources according to the size of the disaster. Therefore, crowdsourcing has been used for gathering information and assessing the situation during disaster management. However, usually, the two types of crowdsourcing tasks (i.e., gathering information and assessing the damage) are performed independently; consequently, the collected information is often not utilized effectively. More efficient work can be expected by linking the two crowdsourcing tasks. This paper proposes a framework for efficiently generating a building damage map by combining the two methods of information collection on disaster areas and assessment of disaster situations using aerial image processing. The results of an experiment using a prototype of our proposed framework clarify the range of applications in the collection and assessment crowdsourcing tasks. The experimental results indicate the feasibility of understanding disaster situations using our method. In addition, it is possible to install artificial intelligence workers that can support human workers to estimate the damage situation more quickly.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Mapping global water projects: improving access to donor investment information on the web

Water Policy ◽  
2003 ◽  
Vol 5 (3) ◽  
pp. 203-212
Author(s):  
J. Lisa Jorgensona
Keyword(s):  
Water Resources ◽  
Web Sites ◽  
Large Scale ◽  
Technical Assistance ◽  
Water Withdrawal ◽  
Small Scale ◽  
Water Projects ◽  
Essential Information ◽  
Water Project ◽  
The Web

This paper discusses a series of discusses how web sites now report international water project information, and maps the combined donor investment in more than 6000 water projects, active since 1995. The maps show donor investment:  • has addressed water scarcity,  • has improved access to improvised water resources,  • correlates with growth in GDP,  • appears to show a correlation with growth in net private capital flow,  • does NOT appear to correlate with growth in GNI. Evaluation indicates problems in the combined water project portfolios for major donor organizations: •difficulties in grouping projects over differing Sector classifications, food security, or agriculture/irrigation is the most difficult.  • inability to map donor projects at the country or river basin level because 60% of the donor projects include no location data (town, province, watershed) in the title or abstracts available on the web sites.  • no means to identify donor projects with utilization of water resources from training or technical assistance.  • no information of the source of water (river, aquifer, rainwater catchment).  • an identifiable quantity of water (withdrawal amounts, or increased water efficiency) is not provided.  • differentiation between large scale verses small scale projects. Recommendation: Major donors need to look at how the web harvests and combines their information, and look at ways to agree on a standard template for project titles to include more essential information. The Japanese (JICA) and the Asian Development Bank provide good models.

Hybrid Combination of GIS,GPS,WSN and GPRS Technology in Modern Digital Agriculture Application

2010 ◽  
Vol 108-111 ◽  
pp. 1158-1163 ◽  
Author(s):  
Peng Cheng Nie ◽  
Di Wu ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Yong He
Keyword(s):  
Large Scale ◽  
Field Application ◽  
Wireless Sensor ◽  
Information Collection ◽  
Scale Field ◽  
Large Scale Field ◽  
Field Information ◽  
Intelligent Management ◽  
Digital Agriculture ◽  
And Control

In order to improve the information management of the modern digital agriculture, combined several modern digital agriculture technologies, namely wireless sensor network (WSN), global positioning system (GPS), geographic information system (GIS) and general packet radio service (GPRS), and applied them to the information collection and intelligent control process of the modern digital agriculture. Combining the advantage of the local multi-channel information collection and the low-power wireless transmission of WSN, the stable and low cost long-distance communication and data transmission ability of GPRS, the high-precision positioning technology of the DGPS positioning and the large-scale field information layer-management technology of GIS, such a hybrid technology combination is applied to the large-scale field information and intelligent management. In this study, wireless sensor network routing nodes are disposed in the sub-area of field. These nodes have GPS receiver modules and the electric control mechanism, and are relative positioned by GPS. They can real-time monitor the field information and control the equipment for the field application. When the GPS position information and other collected field information are measured, the information can be remotely transmitted to PC by GPRS. Then PC can upload the information to the GIS management software. All the field information can be classified into different layers in GIS and shown on the GIS map based on their GPS position. Moreover, we have developed remote control software based on GIS. It can send the control commands through GPRS to the nodes which have control modules; and then we can real-time manage and control the field application. In conclusion, the unattended automatic wireless intelligent technology for the field information collection and control can effectively utilize hardware resources, improve the field information intelligent management and reduce the information and intelligent cost.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

scholarly journals Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means

2012 ◽  
Vol 57 (9) ◽  
pp. 2667-2688 ◽  
Author(s):  
Yang Chen ◽  
Zhou Yang ◽  
Yining Hu ◽  
Guanyu Yang ◽  
Yongcheng Zhu ◽  
...  
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Low Dose ◽  
Ct Image ◽  
Nonlocal Means ◽  
Low Dose Ct

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

A Vision-Based Gap Detection Method during the Placement of Large-Scale Composite Structures

2011 ◽  
Vol 186 ◽  
pp. 11-15
Author(s):  
Li Cao ◽  
Wen Chen ◽  
Jun Xiao
Keyword(s):  
Image Processing ◽  
Video Processing ◽  
Composite Structures ◽  
Large Scale ◽  
Low Cost ◽  
High Quality ◽  
Detection Technology ◽  
Gap Measurement ◽  
Automatic Placement ◽  
On Line

Video processing technology is regarded as a low-cost detection technology in complex environment. Because the placement layer is thin and the surface is complex that causes high detection error and high cost in laser measurement. Two problems must be solved before using it in large-scale composite structures automatic placement. One is to obtain the high-quality and stable image, and the other is to improve efficiency of image processing. In this paper, a method obtaining the high quality placement gap images was studied. It made use of the optical characteristics of composite material’s surface texture. And some parameters were determined by experiments. To reduce the calculation cost of image processing, a placement gap measurement method based on line scanning was also proposed here. The method was effective in our detection experiments on an actual workpiece.

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