5 research outputs found
Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
Mapping floods using satellite data is crucial for managing and mitigating
flood risks. Satellite imagery enables rapid and accurate analysis of large
areas, providing critical information for emergency response and disaster
management. Historical flood data derived from satellite imagery can inform
long-term planning, risk management strategies, and insurance-related
decisions. The Sentinel-1 satellite is effective for flood detection, but for
longer time series, other satellites such as MODIS can be used in combination
with deep learning models to accurately identify and map past flood events. We
here develop a combined CNN--LSTM deep learning framework to fuse Sentinel-1
derived fractional flooded area with MODIS data in order to infer historical
floods over Bangladesh. The results show how our framework outperforms a
CNN-only approach and takes advantage of not only space, but also time in order
to predict the fractional inundated area. The model is applied to historical
MODIS data to infer the past 20 years of inundation extents over Bangladesh and
compared to a thresholding algorithm and a physical model. Our fusion model
outperforms both models in consistency and capacity to predict peak inundation
extents.Comment: CVPR 2023: Earthvision Worksho