NH003
Big Earth Data for Disaster Risk Reduction

Session ID#: 161291

Session Description:
Natural hazards become disasters when the communities affected by them are unable to revert to their previous functionality. Recent two decades have seen over 35% increase in the occurrence of natural disasters with no signs of ebbing. Consequently, increasing population in these disaster-prone areas has made the goal of reducing disaster risks appear in the radar amongst researchers, policy makers and the public. As such, disaster risk reduction research has utmost importance in the current scenario of increasing disasters, as its results help in building more robust, safe and sustainable cities. In that aspect, Big Earth Data provides us with a new perspective of viewing the makings of the disaster alongside their impacts and responsiveness of the communities to it in real time, i.e., data from remote sensing, sensors, social media, cellphone devices. With the help of these dataset, the understanding of the different phases of disaster and the interactions they have with the communities, societies and research into disaster risk reduction. For this session, we are welcoming presentations of research involving analyzing Big Earth Data to monitor, measure or model disaster risks and suggest procedures to curb them through informed and intelligent policy making.
Index Terms:

4332 Disaster resilience [NATURAL HAZARDS]
4335 Disaster management [NATURAL HAZARDS]
4337 Remote sensing and disasters [NATURAL HAZARDS]
4339 Disaster mitigation [NATURAL HAZARDS]

Primary Convener:  Debayan Mandal, Texas A&M University, Department of Geography, College Station, United States
Conveners:  Lei Zou, Texas A&M University College Station, Geography, College Station, TX, United States and Heng Cai, Texas A&M University, Department of Geography, College Station, United States
Student/Early Career Convener:  Debayan Mandal, Texas A&M University, Department of Geography, College Station, United States
 
Evaluation of Spatial Variability of Loess Properties Using a Self-Developed Database in Shaanxi Province, China (1048817)
Guangpeng Zhou, Xi'an, Shaanxi, CHINA
 
Building Footprint Recognition through Satellite Imagery Identification by a Convolutional Neural Network (1060979)
Wei An Chen, Institute of Geophysics, National Central University, Taoyuan, Taiwan, TAIWAN
 
Towards a Digital Twin on Geophysical Extremes (1061677)
Ramon Carbonell, Geosciences Barcelona, GEO3BCN-CSIC, Barcelona, Spain, Arnau Folch, Spanish National Research Council, Zaragoza, Spain, Antonio Costa, Istituto Nazionale di Geofisica e Vulcanologia, Palermo, Italy, Beata Orlecka-Sikora, Institute of Geophysics Polish Academy of Sciences, Seismology, Warsaw, Poland, Piero Lanucara, CINECA, Rome, Italy, Finn Løvholt, Norwegian Geotechnical Institute, Oslo, Norway, Jorge Macias, University of Malaga, Applied Mathematics, Malaga, Spain, Dr. Sascha Brune, University of Sydney, EarthByte Group, Sydney, Australia, Alice-Agnes Gabriel, Ludwig-Maximilians-Universität, Earth and Environmental Sciences, Munich, Germany, Sara Barsotti, Icelandic Meteorological Office, Monitoring and forecasting, Reykjavik, Iceland, Joern Behrens, University of Hamburg, Hamburg, Germany, J Gomez, Laboratorio Fisica Experimental de Partículas, Lisboa, Portugal, Jean Schmittbuhl, University of Strasbourg/CNRS, Strasbourg Cedex, France, Carmela Freda, J Kocot, Akademia Gorniczo-Hunticza CYFRONET, Warsaw, Poland, Domenico Giardini, ETH Zurich, Department of Earth Sciences, Institute of Geophysics, Zurich, Switzerland, Michael Afanasiev, Mondaic AG, Zurich, Switzerland, Helen Glaves, British Geological Survey, Nottingham, United Kingdom and Rosa Badia, Barcelona Supercomputing Center, Barcelona, Spain
 
Mapping and Quantifying the Global Wildland-Urban Interface (1092472)
Franz Schug1, Avi Bar-Masada2, Amanda Carlson1, Heather Cox1, Todd J Hawbaker3, David Helmers1, Patrick Hostert4, Dominik Kaim5, Neda Kasraee1, Katarzyna E Lewinska4, Sebastian Martinuzzi1, Miranda Mockrin6, Jose Pereira7, Kira Pfoch8, Owen Francis Price9 and Volker Radeloff1, (1)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States, (2)University of Haifa, Department of Biology and Environment, Kiryat Tivon, Israel, (3)US Geological Survey, Geosciences and Environmental Change Science Center, Lakewood, United States, (4)Humboldt-Universitaet zu Berlin, Geography Department, Berlin, Germany, (5)Jagiellonian University, Institute of Geography and Spatial Management, Kraków, Poland, (6)USDA Forest Service, Baltimore, MD, United States, (7)University of Lisbon, Forest Research Center, Lisbon, Portugal, (8)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, WI, United States, (9)University of Wollongong, Center for Environmental Risk Management of Bushfires, Wollongong, Australia
 
Flood Debris Quantification and Comparison based on the Removal and Disposal Operation: A Post-Disaster Study of Beaumont, Texas Following Hurricane Harvey (1096534)
Jasmine Bekkaye and Navid Jafari, Louisiana State University, Department of Civil and Environmental Engineering, Baton Rouge, LA, United States
 
Using Big Earth Data to Evaluate Global Landslide Hazard Under a Changing Climate (1121754)
William Mitchell, James Brennan, Graham Reveley, Claire Burke and Laura Ramsamy, Climate X, London, United Kingdom
 
Simulating Coastal Land Losses and Gains by Integrating Neighborhood Effects and Deep Learning with Cellular Automata (1128354)
Mingzheng Yang1, Lei Zou1, Heng Cai2, Joynal Abedin1 and Debayan Mandal1, (1)Texas A&M University, Department of Geography, College Station, United States, (2)Louisiana State University, Baton Rouge, LA, United States
 
Quantifying Flood Risk of the Shaded X Zone in the United States (1132283)
Ayat Salah Al Assi1, Carol J Friedland2, Robert V Rohli3 and Rubayet Bin Mostafiz3, (1)Louisiana State University, Bert S. Turner Department of Construction Management, Baton Rouge, LA, United States, (2)Louisiana State University Agricultural Center, Department of Biological and Agricultural Engineering, LaHouse Resource Center, Baton Rouge, United States, (3)Louisiana State University, Oceanography and Coastal Sciences, Baton Rouge, LA, United States
 
Fairness and Explainability of Random Forest Regression in Predicting Social Media Rescue Requests with Socio-Environmental Features: Case of Hurricane Harvey (1163197)
Volodymyr Mihunov1, Kejin Wang2, Zheye Wang3, Nina Lam1 and Mingxuan Sun2, (1)Louisiana State University, Baton Rouge, LA, United States, (2)Louisiana State University, Baton Rouge, United States, (3)Rice University, Houston, United States
 
Development of multi-stakeholder platform "Early Warning Early Action and Early Finance" for Humanitarian Crisis (1165741)
Giriraj Amarnath, International Water Management Institute, Silverton, South Africa and Ana Maria Rodriguez Loboguerrero, Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT), Rome, Italy
 
Predicting the Tunnel Damages due to Strong Ground Motion: A Neural Network Approach (1166825)
Abdullah Ansari1, Anas Ansari2, KS Rao1 and AK Jain1, (1)Indian Institute of Technology Delhi, Civil Engineering, New Delhi, India, (2)Sanjivani College of Engineering, Kopargaon, Department of Computer Engineering, Kopargaon, India
 
Developing a Database of Historical Extratropical Cyclones (1170385)
Nazla Bushra1, Andreas D Muehlbauer1 and Emmanouil Flaounas2,3, (1)FM Global, Research Division, Norwood, United States, (2)National Observatory of Athens, Athens, Greece, (3)Hellenic Centre for Marine Research, Athens, Attiki, Greece
 
Rapid Disaster Damage Estimation Based On Geo-referenced Social Media Data (1175447)
Yalong Pi, Texas A&M University Institute of Data Science, College Station, United States, Xinyue Ye, Texas A&M University, Department of Landscape Architecture and Urban Planning, College Station, United States and Nick Duffield, Texas A&M Institute of Data Science, College Station, United States
 
Geospatial analysis for identifying risk factors of residential fires in Ohio: A census tracts analysis using NFIRS dataset (1180116)
Md Rifat Hossain, University of Toledo, Toledo, OH, United States
 
Spatial-Temporal Changes of Flood Exposure in the United States (1184399)
Joynal Abedin1, Lei Zou1, Heng Cai1, Mingzheng Yang1, Humaira Akter2, Debayan Mandal1, Binbin Lin1 and Bing Zhou1, (1)Texas A&M University, Department of Geography, College Station, United States, (2)University of Dhaka, Department of Geography and Environment, Dhaka, Bangladesh
 
Exploring Hurricane Preparedness Through Human Mobility Data: Insights from Hurricane Ida (1185264)
Qingchun Li, Princeton University, Princeton, NJ, United States, Anu Ramaswami, Princeton University, Department of Civil & Environmental Engineering; High Meadows Environmental Institute & M.S. Chadha Center for Global India, Princeton, United States and Ning Lin, Princeton University, Civil and Environmental Engineering, Princeton, NJ, United States
 
Evolving Geographical Disparities of Community Disaster Resilience in the United States (1195969)
Debayan Mandal, Lei Zou, Heng Cai, Joynal Abedin, Mingzheng Yang, Binbin Lin and Bing Zhou, Texas A&M University, Department of Geography, College Station, United States
 
Revealing Disaster Resilience Inequalities during Texas 2021 Winter Storm through Geospatial Data Mining (1196836)
Heng Cai1, Binbin Lin2, Dongying Li3 and Lei Zou2, (1)Texas A&M University College Station, Geography, College Station, United States, (2)Texas A&M University, Department of Geography, College Station, United States, (3)Texas A&M University College Station, College Station, United States
 
Empowering Smart Disaster Response with Social Media and GeoAI (1199737)
Lei Zou1, Texas A University2, Heng Cai3, Debayan Mandal4, Joynal Abedin4, Mingzheng Yang4 and Binbin Lin4, (1)Texas A&M University College Station, College Station, TX, United States, (2)Texas A&M University, College Station, United States, (3)Texas A&M University College Station, College Station, United States, (4)Texas A&M University, Department of Geography, College Station, United States
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