S. M. Dassanayake, I. Mahakalanda, D. M. R. Sanjula, B. Dissanayake, R. M. Pasan, I. Gunathunga, et al. (2023). Geospatial Impact Analytics of Hydrometeorological Hazards: A Study on Urban and Suburban Floods in Sri Lanka using Online Textual Data. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 156–163). Palmerston North, New Zealand: Massey Unversity.
Abstract: Urban and suburban communities in tropical countries like Sri Lanka typically experience hydrometeorological hazards that substantially damage property and lives. Although accurate forecasts of weather events are available, the decision-makers often fail to mitigate the actual impact of these forecasts alone. The adverse impacts experienced by the community and reported by news and online media complement this fact. The forecast-impact disparity underpins the scope for holistically linking the forecast data with actual impact. This paper presents a work-in-progress study that develops a geospatial analytics framework using online textual data for assessing the spatiotemporal impact of the hydrometeorological hazards in disaster hot spots. The preliminary findings show prospects for extending the study to impact-focused visualization and forecasting that capture the community's and decision makers' attention for better interventions. For example, these include the degree of disaster response, planning and scheduling critical infrastructure and estimating damages, compensations and insurance claims.
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James Hilton, & Nikhil Garg. (2023). Rapid Geospatial Processing for Hazard and Risk Management using the Geostack Framework. In V. L. Thomas J. Huggins (Ed.), Proceedings of the ISCRAM Asia Pacific Conference 2022 (pp. 2–7). Palmerston North, New Zealand: Massey Unversity.
Abstract: Operational predictive and risk modelling of landscape-scale hazards such as floods and fires requires rapid processing of geospatial data, fast model execution and efficient data delivery. However, geospatial data sets required for hazard prediction are usually large, in a variety of different formats and usually require a complex pre-processing toolchain. In this paper we present an overview of the Geostack framework, which has been specifically designed for this task using a newly developed software library. The platform aims to provide a unified interface for spatial and temporal data sets, deliver rapid processing through OpenCL and integrate with web APIs or external graphical user interface systems to display and deliver results. We provide examples of hazard and risk use cases, particularly Spark, a Geostack based system for predicting the spread of wildfires. The framework is open-source and freely available to end users and practitioners in the hazard and geospatial space.
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Michael Erskine, Scott Seipel, & Cayson Seipel. (2022). Development of a Geospatial Agent-Based Simulation of Disaster Evacuations for Battery Electric Vehicle (BEV) Policy. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 532–540). Tarbes, France.
Abstract: Several nations have signaled their intent to phase out petroleum-based engines for passenger vehicles and promote a transition to battery electric vehicles (BEVs). While researchers have established the long-term environmental benefits of BEVs, there are critical considerations for policymakers in areas prone to natural disasters. This research intends to develop a geospatial-based model to explore and simulate the evacuation of BEVs during a disaster. This work-in-progress (WiPe) paper examines the variables essential to creating an effective hurricane simulation. The final simulation model is intended to allow for the evaluation of BEV policy options under a variety of scenarios. We describe the considerations made during the development of this geospatial agent-based simulation under various hurricane parameters. Finally, we mention the expected benefits of our work and hint at possible policy directions.
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Tim-Jonathan Huyeng, Timo Bittner, & Uwe Rüppel. (2022). Examining the Feasibility of LoRa-based Monitoring in Large-scale Disaster Response Scenarios. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 541–550). Tarbes, France.
Abstract: Following a natural disaster or other large-scale events which require emergency response assessing and monitoring the situation at hand is of critical importance. However, some infrastructure that is often relied upon such as cellular service or the power grid might be temporarily disrupted or entirely unavailable. In order to be able to still transmit relevant monitoring data gathered from sensors, the use of a low-cost LPWAN with LoRa modulation technique is suggested in the approach presented here. Combined with an analysis of disaster response in Germany the relevant aspects are consolidated in a concept utilizing LoRaWAN with a ChirpStack backend that is easy to set up and entirely independent of external infrastructure. The proposed addition which aims to support disaster control management in Germany is then tested in conjunction with a fictional flooding scenario where an area is monitored with autarkic sensors using LoRaWAN technology.
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Zainab Akhtar, Ferda Ofli, & Muhammad Imran. (2021). Towards Using Remote Sensing and Social Media Data for Flood Mapping. In Anouck Adrot, Rob Grace, Kathleen Moore, & Christopher W. Zobel (Eds.), ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management (pp. 536–551). Blacksburg, VA (USA): Virginia Tech.
Abstract: Ghana's capital, the Greater Accra Metropolitan Area (GAMA) is most vulnerable to flooding due to its high population density. This paper proposes the fusion of satellite imagery, social media, and geospatial data to derive near real-time (NRT) flood maps to understand human activity during a disaster and the extent of infrastructure damage. To that end, the paper presents an automatic thresholding technique for NRT flood mapping using Sentinel-1 images where four different speckle filters are compared using the VV, VH and VV/VH polarization to determine the best polarization(s) for delineating flood extents. The VV and VH bands together on Perona-Malik filtered images achieved the highest accuracy with an F1-score of 81.6%. Moreover, all tweet text and images were found to be located in flooded regions or in very close proximity to a flooded region, thus allowing crisis responders to better understand vulnerable communities and what humanitarian action is required.
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