Standardised geo-sensor webs and web-based geo-processing for near real-time situational awareness in emergency management

Author(s):  
Guenther Sagl ◽  
Bernd Resch ◽  
Manfred Mittlboeck ◽  
Barbara Hochwimmer ◽  
Michael Lippautz ◽  
...  
Author(s):  
Laura Coconea ◽  
Uberto Delprato ◽  
David Prior ◽  
Guido Albertengo ◽  
Phil Blythe ◽  
...  

2014 ◽  
Vol 2014 (1) ◽  
pp. 1583-1595 ◽  
Author(s):  
Judd Muskat

ABSTRACT Computing technology has advanced to the point where it is now standard practice to employ complex Geographic Information Systems (GIS) within the Incident Command Post (ICP). Simultaneously, field data collection has been migrating to mobile computing applications which output GIS files that are quickly displayed for real-time situational awareness. From the initial emergency response through clean-up and sign-off much data with a spatial component is generated and many disparate data sets are collected. More efficient data integration, management and visual analysis affords Incident Commanders and Section Chiefs the ability to make informed and timely planning, operational and strategic decisions. Traditionally GIS maps were created in the ICP from field sketches, field notes and verbal reports. Processing of these data by the GIS Unit is very time consuming and prone to error. Preliminary efforts to streamline and automate field data collection by the California Department of Fish and Wildlife (CDFW, formerly the California Department of Fish and Game), Office of Spill Prevention and Response (OSPR) utilized Global Positioning System (GPS) receivers to record waypoints and track lines. Since then more elegant electronic field data collection applications installed on small, handheld computers have been developed including those for “Wildlife Recovery and Transport”, “Resources at Risk” over flights, and the “Shoreline Cleanup and Assessment Technique” (SCAT). Other recent advancements allow for real-time aerial remote sensing for oil slick detection and detailed mapping of its properties, and displaying the output from coastal High Frequency (HF) radar installations for real-time visualization of local ocean surface current fields. These field data collection applications are explained in more detail in the body of this paper. Once these data are incorporated into the GIS a web-based Common Operational Picture (COP) is utilized for timely dissemination of relevant geospatial data. OSPR has worked closely with the National Oceanic and Atmospheric Agency (NOAA) to develop “Southwest ERMA” (Environmental Response Management Application) as California's COP for web-based data dissemination and incident situational awareness. At the Deepwater Horizon (MC-252) Incident Command Post (ICP) in Houma, Louisiana many responders were from outside of the region and unfamiliar with the local geography. Area base maps with a standardized coast line and place names were not readily available for several days which added unnecessary confusion to the mix. As a lesson learned and in order to avoid this situation for an oil spill response in California, OSPR and NOAA have pre-loaded Southwest ERMA with pertinent base maps, charts and spill response planning data from the three California Area Contingency Plans (ACPs). These data are deliberately made freely available to the general public via the Southwest ERMA web-viewer without any user login credentials required.


2019 ◽  
Author(s):  
Berardo Naticchia ◽  
Leonardo Messi ◽  
Massimiliano Pirani ◽  
Andrea Bonci ◽  
Alessandro Carbonari ◽  
...  

Author(s):  
Bhargav Appasani ◽  
Amitkumar Vidyakant Jha ◽  
Sunil Kumar Mishra ◽  
Abu Nasar Ghazali

AbstractReal time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.


Author(s):  
Niroj Gurung ◽  
Sri Raghavan Raghav Kothandaraman ◽  
Liuxi Calvin Zhang ◽  
Heng Kevin Chen ◽  
Farnoosh Rahmatian ◽  
...  

2021 ◽  
Vol 11 (16) ◽  
pp. 7197
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Shan Bao

Warning pedestrians of oncoming vehicles is critical to improving pedestrian safety. Due to the limitations of a pedestrian’s carrying capacity, it is crucial to find an effective solution to provide warnings to pedestrians in real-time. Limited numbers of studies focused on warning pedestrians of oncoming vehicles. Few studies focused on developing visual warning systems for pedestrians through wearable devices. In this study, various real-time projection algorithms were developed to provide accurate warning information in a timely way. A pilot study was completed to test the algorithm and the user interface design. The projection algorithms can update the warning information and correctly fit it into an easy-to-understand interface. By using this system, timely warning information can be sent to those pedestrians who have lower situational awareness or obstructed view to protect them from potential collisions. It can work well when the sightline is blocked by obstructions.


2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.


Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.


Sign in / Sign up

Export Citation Format

Share Document