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UID:pretalx-foss4g-2023-academic-track-C9QYMM@talks.osgeo.org
DTSTART;TZID=CET:20230628T150000
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DESCRIPTION:[Background and Purpose]\nThere are currently more than 7.5 mil
 lion workers worldwide working in the field of fire\, medical\, and variou
 s emergency services with a total budget exceeding 400 billion euros. Addi
 tionally\, approximately 15 billion euros are spent on equipment and other
  needs.\nWater pollution caused by a downpour and climate change have a fa
 tal impact on our health and the number of waterborne diseases continues t
 o increase domestically and internationally. Therefore\, the significance 
 of technology which properly responds to various disasters caused by the c
 limate crisis is increasing. Technologies regarding natural disasters have
  been widely developed\; however\, disaster response systems related to me
 dical and biological emergencies are lacking. Many technologies for natura
 l disasters have been developed\, but there is no technology development a
 nd response system platform related to biological and medical risks\, whic
 h are considered social disasters. \n \nFurthermore\, we aim to develop ra
 pid and accurate pathogen detection technology which contains situational 
 awareness\, control/response methods\, risk assessment\, and epidemiologic
 al investigation methods. Eventually\, by combining all these methods\, we
  want to establish a user-centered GIS platform. \n\n[Methods]\nA decision
  support system that manages pathogen contamination was developed by using
  information obtained from sensors and fields. Moreover\, risk assessment 
 and epidemiological investigation technology which was developed through a
 rtificial intelligence and big data were included. The following three tec
 hnologies were applied to analyze contaminated areas. \n\nFirst\, it provi
 des a preview of data taken by satellites and collects images of aquatic r
 egions to analyze and inform the pollution degree. Moreover\, the turbidit
 y of the water is provided from the data of aquatic regions which are cons
 tantly being filmed. Lastly\, it also builds a water quality monitoring sy
 stem based on data analyzed from water samples that are acquired from dron
 es. \n\nThese images were taken from regions that humans cannot easily acc
 ess. The technology provides both the spatial analysis result and images t
 o users. Data and photos on social media are also analyzed to provide the 
 severity of water pollution along with the specific spatial locations. To 
 effectively provide and manage information on the platform\, the system co
 nsists of seven layers: source management\, data collection\, interoperabi
 lity\, data harmonization\, data application\, data process\, and security
 . All components in the data collection\, interoperability\, data harmoniz
 ation\, and security layers provide geographic information and statistics 
 for users. \n\n\n\n[Results]\nConsidering the functions of the system\, th
 e following platform can be applied in three fields: "Detection of pathoge
 ns and water pollution/situational response/post-investigation"\, "Infecti
 on management and decision support system " and "Protection and management
  of the first responder".\n\nTwo test locations were selected and the pilo
 t case study was conducted in each location.\n\nLimassol Pilot Case Study\
 n- An earthquake near Limassol caused flash floods and landslides\, pollut
 ing the Kouris Dam which is a primary reservoir in Limassol.\n \n- The wat
 er pollution over time can be checked by satellite images analyzed through
  PathoSAT. The turbidity and temperature of water detected by PathoSENSE a
 nd the results of satellite image analysis can be checked on the PathoGIS 
 platform.\n\n- The user can check the areas heavily affected by flood and 
 the magnitude of the tide is visualized on the PathoGIS data panel through
  graphs.\n\n- A warning alert appears when the pollution level exceeds the
  threshold and the user can check. If victims report the location of pollu
 ted areas on Twitter\, it can be checked through PathoTweet.\n\nKorean Pil
 ot Youth Case Demonstration\nA person in close contact with ASF wild pigs 
 visited a farm near Soyang Dam and all pigs on the farm had to be dislocat
 ed due to mass infection. Many positive ASF cases were reported near the S
 oyang River inevitably.\n\n- Due to the unusually high precipitation in su
 mmer\, the Soyang River Dam overflowed and caused leaked leachate to flow 
 into the Soyang River Dam.\n\n- PathoSAT satellite images can be used to i
 dentify the boundaries of areas that can be potentially damaged by floodin
 g. The time series visualization shows that water pollution is more severe
  near the location of ASF-positive cases.\n\n-Since the government needs t
 o respond to the rapidly increasing number of ASF cases\, the results of A
 SF case analysis can be checked using the analysis application. Using such
  data to prevent African swine fever from spreading south\, analysts can d
 etermine the optimal distance from SLL (Southern Limit Line)\, CLL (Civil 
 Defense Limit Line)\, primary fence\, or the need for additional fence ins
 tallation.\n\n- As the number of ASF-positive cases increases\, pollution 
 in tap water can be easily found in Seoul since a large portion of water o
 riginates from Soyanggang Dam.\n\n- The PathoSENSE turbidity sensor notifi
 es the current situation regarding water pollution. If the turbidity of ta
 p water increases\, Twitter reports on health problems also increase in Se
 oul simultaneously.\n\n\n[Conclusion]\nA platform that contains a database
  related to the spread of pathogens and provides AI-based information rega
 rding the dangers of the situation will certainly help in responding to in
 fectious diseases.  \n\nIt will be able to strengthen its ability to respo
 nd to infectious diseases and disasters by using it as a tool to improve t
 he capability of the first responder and reduce the time required to detec
 t and respond to the situation.\n\nIn particular\, there will be an effect
  of reducing industrial accidents by improving the ability to respond to u
 nidentified risk situations that are likely to be encountered by first-tim
 e field responders.\n\nBy improving the ability to respond to unidentified
  situations\, the number of industrial accidents will likely decrease. Sho
 rtly\, when database expansion and the cost of maintenance becomes stable\
 , in-depth data analysis of epidemiologic big data will be possible using 
 pattern recognition and deep learning models.
DTSTAMP:20260316T151245Z
LOCATION:UBT E / N209 - Floor 3
SUMMARY:GIS-based intelligent decision making support system for the disast
 er response of infectious disease - MIN YOUNG LEE
URL:https://talks.osgeo.org/foss4g-2023-academic-track/talk/C9QYMM/
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