scholarly journals Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway

2019 ◽  
Vol 11 (23) ◽  
pp. 2863 ◽  
Author(s):  
Markus Eckerstorfer ◽  
Hannah Vickers ◽  
Eirik Malnes ◽  
Jakob Grahn
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Processing System ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Large Area ◽  
Avalanche Forecasting ◽  
Sar Data ◽  
Spatio Temporal ◽  
Avalanche Warning

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel-1 SAR data are download. Our avalanche detection algorithm has an average probability of detection (POD) of 67.2% with a false alarm rate (FAR) averaging 45.9, with a maximum POD of over 85% and a minimum FAR of 24.9% compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 × 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3% were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79% with high POD in cases of medium to large wet snow avalanches. For the first time, we present a dataset of spatio-temporal avalanche activity over several winters from a large region. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

scholarly journals Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway

2019 ◽  
Author(s):  
Markus Eckerstorfer ◽  
Hannah Vickers ◽  
Eirik Malnes ◽  
Jakob Grahn
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Processing System ◽  
Detection Algorithm ◽  
Activity Monitoring ◽  
Probability Of Detection ◽  
Hazard Mapping ◽  
Large Area ◽  
Avalanche Forecasting ◽  
Sar Data

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

scholarly journals Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1976 ◽  
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Hassan Bazzi ◽  
Mehrez Zribi
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Agricultural Sector ◽  
Surface States ◽  
Frost Damage ◽  
Detection Algorithm ◽  
Temperature Data ◽  
Economic Losses ◽  
Freeze Thaw ◽  
Sar Data

Short-term freeze/thaw cycles, which mostly occur in the northern hemisphere across the majority of land surfaces, are reported to cause severe economic losses over broad areas of Europe and North America. Therefore, in order to assess the extent of frost damage in the agricultural sector, the objective of this study is to build an operational approach capable of detecting frozen plots at the plot scale in a near real-time scenario using Sentinel-1 (S1) data. C-band synthetic aperture radar (SAR) data show high potential for the detection of freeze/thaw surface states due to the significant alterations to the dielectric properties of the soil, which are distinctly observable in the backscattered signal. In this study, we propose an approach that relies on change detection in the high-resolution Sentinel-1 C-band SAR backscattered coefficients, to determine surface states at the plot scale as either frozen or unfrozen. A threshold analysis is first performed in order to determine the best thresholds for three distinct land cover classes, and for each polarization mode (VH, and VV). S-1 SAR data are then used to detect a plot’s surface state as either unfrozen, mild-to-moderately frozen or severely frozen. A temperature-based filter has also been applied at the end of the detection chain to eliminate false detections in the freezing detection algorithm due mainly to rainfall, irrigation, tillage, or signal noise. Our approach has been tested over two study sites in France, and the output results, using either VH or VV, compared qualitatively well with both in situ air temperature data and soil temperature data provided by ERA5-Land. Overall, our algorithm was able to detect all freezing episodes over the analyzed S-1 SAR time series, and with no false detections. Moreover, given the high-resolution aspect of S-1 SAR data, our algorithm is capable of mapping the local variation of freezing episodes at plot scale. This is in contrast with previous products that only offer coarser results across larger areas (low spatial resolution).

Machine Vision-Based Intelligent Fire Fighting Robot

2010 ◽  
Vol 450 ◽  
pp. 312-315 ◽  
Author(s):  
Chao Ching Ho ◽  
Ming Chen Chen ◽  
Chih Hao Lien
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Monitoring System ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Data ◽  
Fire Fighting ◽  
Fire Source ◽  
Fire Flame

Designing a visual monitoring system to detect fire flame is a complex task because a large amount of video data must be transmitted and processed in real time. In this work, an intelligent fire fighting and detection system is proposed which uses a machine vision to locate the fire flame positions and to control a mobile robot to approach the fire source. This real-time fire monitoring system uses the motion history detection algorithm to register the possible fire position in transmitted video data and then analyze the spectral, spatial and temporal characteristics of the fire regions in the image sequences. The fire detecting and fighting system is based on the visual servoing feedback framework with portable components, off-the-shelf commercial hardware, and embedded programming. Experimental results show that the proposed intelligent fire fighting system is successfully detecting the fire flame and extinguish the fire source reliably.

scholarly journals A Novel Information Processing in IoT Based Real Time Health Care Monitoring System

2020 ◽  
Vol 2 (3) ◽  
pp. 188-196
Author(s):  
Jennifer S. Raj
Keyword(s):  
Health Care ◽  
Information Processing ◽  
Real Time ◽  
Monitoring System ◽  
Communication Networks ◽  
Research Work ◽  
Processing System ◽  
User Preference ◽  
Superior Performance ◽  
Health Care Monitoring

The recent technology developments and innovations improves the life style of people through smart applications, sensors, wireless communication networks, etc., for all those technologies internet is the backbone and the information processing like accessing, distributing the necessary information is achieved through Internet of Things (IoT). IoT supports multi-disciplinary applications as an active entity in engineering, science and business discipline. Based on the user preference these applications and its services could be framed in IoT. On contrary to the development, IoT has flaws in information processing as huge volume of data is need to be handled in a single environment. Considering these facts, the proposed research work is aimed to develop a novel information processing system in IoT platform through a reliable health care monitoring system. The effective utilization of big data in IoT environment is analysed through the proposed architecture to attain minimum delay in a real time environment. Conventional models are used to compare the performance of proposed design and the experimentation is performed to verify the superior performance of proposed approach using accuracy, cost functions in terms of transmission and storage, f-measure, sensitivity and specificity.

scholarly journals GNSS/CORS-Based Technology for Real-Time Monitoring of Landslides on Waste Dump – A Case Study at the Deo Nai South Dump, Vietnam

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Pham Cong Khai ◽  
Dinh Trong TRAN ◽  
Van Hai NGUYEN
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Human Life ◽  
Processing System ◽  
Warning System ◽  
Open Pit ◽  
Waste Dump ◽  
Real Time Monitoring ◽  
Monitoring Stations

Nowadays, there are many different methods for monitoring waste dump landslides based on GPS, total station, remote sensing, UAV, Lidar, etc. However, these technologies can only periodically monitor but cannot continuously monitor in real time. In recent years, GNSS CORS technology has been applied to continuously monitor real time waste dump landslides in open-pit mines to provide immediate or nearly-instant warning of waste dump landslides, which can timely prevent and minimize damages to property and human life. In the present work, the real time monitoring system of waste dump landslides monitoring based on GNSS CORS technology was designed and built. This real time monitoring system includes (1) the GNSS CORS station based on Leica technology, (2) the monitoring stations system, (3) the data collection, transmission and processing system based on Trimble technology and the warning system. This system allows continuous monitoring in real time and provides an instant warning if the landslide occurred. Moreover, it also has the advantage of being cheap, flexible and easy to install for monitoring stations. A simulation experiment results showed that our monitoring system operates stably and continuously 24/7 with a horizontal accuracy of ±3 mm and vertical accuracy of ±5 mm.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

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