THE DEEP LEARNING AND GEOSPATIAL BIG DATA REVOLUTION IN PREDICTING EXTREME EVENTS: STATE OF THE ART, CHALLENGES, AND FUTURE DIRECTIONS FOR FLOODS, LANDSLIDES, AND WILDFIRES
DOI:
https://doi.org/10.56238/ERR01v10n4-036Keywords:
Systematic Review, Disaster Management, Explainable AIAbstract
The increasing frequency of climate disasters demands more advanced forecasting tools. In this context, Deep Learning (DL) applied to geospatial data offers transformative potential. This paper provides a systematic review of the state-of-the-art use of DL for mapping and predicting floods, landslides, and wildfires. The analysis of 112 recent articles shows a rapid specialization of the field, with the consolidation of architectures such as U-Net for floods and CNNs for landslides. However, the results also reveal a strong geographical concentration of research, with gaps in vulnerable regions. The transition from research to practice is hindered by critical challenges, including the scarcity of quality training data, the low interpretability of models, and their limited generalization. We conclude that future progress depends on creating hybrid, explainable models and on a research agenda that promotes geographic equity in disaster management.
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