mlsanityData to 14 Oct 2026

Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquake

arXiv 2311.08999 ↗First posted 15 Nov 2023cs.AIcited by 1

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Abstract (arXiv)

The devastating 6.8-magnitude earthquake in Al Haouz, Morocco in 2023 prompted critical reflections on global disaster management strategies, resulting in a post-disaster hackathon, using artificial intelligence (AI) to improve disaster preparedness, response, and recovery. This paper provides (i) a comprehensive literature review, (ii) an overview of winning projects, (iii) key insights and challenges, namely real-time open-source data, data scarcity, and interdisciplinary collaboration barriers, and (iv) a community-call for further action.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 85 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

12 of 85 references.

References · 12

  1. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
  2. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  3. 2022GraphCast: Learning skillful medium-range global weather forecastingcited by 267
  4. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 139
  5. 2020MetNet: A Neural Weather Model for Precipitation Forecastingcited by 131
  6. 2023A Systematic Review of Green AIcited by 77
  7. 2023Deep Learning for Day Forecasts from Sparse Observationscited by 72
  8. 2019Flood Prediction Using Machine Learning Models: Literature Reviewcited by 36
  9. 2022RDD2022: A multi-national image dataset for automatic Road Damage Detectioncited by 32
  10. 2020MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videoscited by 25
  11. 2020Hurricane Forecasting: A Novel Multimodal Machine Learning Frameworkcited by 13
  12. 2020From predictions to prescriptions: A data-driven response to COVID-19cited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Architectural Insights for Post-Tornado Damage Recognitioncited by 0