Auditing Geospatial Datasets for Biases: Using Global Building Datasets for Disaster Risk Management

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 12579 - 12590

Published: Jan. 1, 2024

The presence of biases has been demonstrated in a wide range machine learning applications; however, it is not yet widespread the case geospatial datasets. This study illustrates importance auditing datasets for biases, with particular focus on disaster risk management applications, as lack local data may direct humanitarian actors to utilize global building estimate damage and distribution aid efforts. It important ensure that there are no against representation vulnerable populations they missed aid. manuscript audits four [Google Open Buildings, Microsoft Bing Maps Building Footprints, Overture Foundation (OMF), OpenStreetMap (OSM)] regarding relative wealth index (RWI), population density, urban/rural proportions, size Tanzania Philippines. dataset accuracies these two countries lower than expected. Google Buildings (with confidence above 0.7) OSM best combinations false negative discovery, though was more consistent across tiles. equality opportunity lowest whereas OMF displayed particularly low density RWI Tanzania. These results demonstrate exist types areas, which emphasizes new applications areas.

Language: Английский

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Research gaps and challenges for impact-based forecasts and warnings: Results of international workshops for High Impact Weather in 2022

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105234 - 105234

Published: Jan. 1, 2025

Language: Английский

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Can Small Towns Survive Climate Change? Assessing Economic Resilience and Vulnerability Amid Major Storms

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145158 - 145158

Published: Feb. 1, 2025

Language: Английский

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A First Attempt at Impact-Based Typhoon Track Ensemble Forecasting in Japan: Evaluating the Role of Typhoon Tracks in Flood Damage for Hagibis (2019)

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Typhoon Hagibis (2019), one of the most powerful storms to strike Japan in recent years, caused widespread flooding and significant damage. Impact-based forecasting is crucial for planning effective mitigation measures enhancing future disaster responses. This study employs Integrated Land Simulator (ILS) coupled with Weather Research Forecasting (WRF) Model evaluate flood damage induced by Hagibis. Our control (c000) simulation successfully reproduced spatial distribution intensity accumulated rainfall peak river discharge. However, compared observations, simulated discharge exhibited a slight westward shift central eastward northeastern Japan. These discrepancies are likely due (eastward) typhoon track before (after) its landfall To systematically assess impact tracks on damage, we conducted ensemble simulations. The e008 (0.8° shift) resulted highest totaling 2478.7 billion JPY. A reduced total across but increased it southwestern regions, whereas an led overall decrease nationwide. Regarding worst each region, was primarily concentrated floodplain areas along Pacific Ocean coast central, southwestern, Japan, while southern more Sea coast. findings underscore critical influence risk. can enhance our understanding high-risk flood-prone improve preparedness strategies.

Language: Английский

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Remote Sensing for Disaster Risk Management—Advances and Limitations

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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