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Mapping Urban floods using multi-sensor satellite imagery and deep learning
Rohit Mukherjee
Post Doctoral Researcher
rohitmukherjee.space
rohitmukherjee@arizona.edu
@RohitMukherjee9 (Twitter)
+ social [pixel] lab with Dr. Beth Tellman
Dhaka, Bangladesh 25th August 2020
Hannah Friedrich, Zhijie Zhang, Jonathan Giezendanner
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Urban Floods: More challenging
Complex Infrastructure, Drainage Systems, Impervious Surfaces
*Existing flood models are not as accurate in urban regions
PlanetScope 3 meters
2020-09-26, pre-flood
PlanetScope 3 meters
2020-11-22, post flood
Sentinel-2 10 meters
2020-11-27, post flood
MODIS 250 meters
2020-11-24, flood
San Pedro Sula, Honduras
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Satellite Data: No single product is sufficient
Trade-offs in spatial, spectral, and temporal resolutions, optical/radar
No clouds, but distortions and speckle noise
Sentinel-1, VV-VH, 2020-11-25
Multispectral, but occluded by clouds
Sentinel-2, FCC, 2020-11-27
High resolution, but commercial
PlanetScope, FCC, 2020-11-25
Khartoum, Sudan
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We covered 14 urban flood events
250+ 1024 by 1024 images labeled using PlanetScope imagery
Over 250 sq km of flooded regions labeled
Urban Flood Dataset
~1 hour total per chip (50 min labeling, 10 min reviewing)
Total of ~350 hours spent
Acknowledgements: Patrick Hellmann, Simone Holladay, Aaron Krupp, Lin Ji,
and Natasha Rapp
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Hand-Labels are not ground truth
Labelers do not always agree with each other
It’s difficult to define high vs low confidence classes
Hand-labeled datasets are valuable (also expensive) especially for independent evaluations
Higher quality labels are more important than larger deep learning architectures architectures
PlanetScope NIR, Red, Green at 3 meters
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PlanetScope NIR, Red, Green at 3 meters
Hand Labels
Inference on PlanetScope at
3 meters
Inference on
Sentinel-1 at 10 meters
Floods in Khartoum (Sudan) on the 25th of August, 2020
Trained U-Nets on Urban Flood Dataset
Sentinel-1 missing finer features
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Evaluated our PlanetScope model for spatial generalizability
PlanetScope NIR, Red,
Green at 3 meters
Urban Flood Dataset
Predictions on unseen
Dhaka Floods in 2020
Mean F1
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Evaluated across public and commercial sensors
Sentinel-2
Sentinel-1
PlanetScope
Urban Flood Dataset
Bing Imagery
Khartoum, Sudan
Z. Zhang
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Higher spatial resolution (e.g., Planet) = more accuracy, but
PlanetScope
MODIS
Bing Imagery
Can Tho, Vietnam
10th October 2020
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PlanetScope FCC
Urban Flood Dataset Label
Trained model
Trained on larger FloodPlanet
Label data quality is more important than training dataset size*
*pre-train with lower quality labels and then fine-tune with stronger labels
When trained on non-urban focused and larger FloodPlanet dataset, accuracy did not improve
FloodPlanet contains 19 non-urban focused global flood events with temporally collocated with (at least one of) Sentinel-1, Sentinel-2, and Landsat 8 images for each label.
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Differences in mean surface reflectance values per spectral band
across three PlanetScope’s SuperDove sensors
that captured images within 45 minutes of each other
Mapping Floods in the desert: A golf course in Pima County, Arizona
Frazier, A. E., & Hemingway, B. L. (2021). A technical review of planet smallsat data: Practical considerations for processing and using planetscope imagery. Remote Sensing, 13(19), 3930.
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What we learned
Reliable flood monitoring requires multi-source cloud free high spatial and temporal data
We need application specific robust validation strategies
Higher spatial resolution data = better accuracy, but short wave infrared bands are crucial
Commercial data will not solve all problems, but deep learning could mitigate some
Flooding due to dam failure
May 2020
Midlands, MI
Bing Imagery
PlanetScope
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PlanetScope Model
Thank you for your attention!
Hannah Friedrich, Jonathan Giezendanner, Zhijie Zhang, Ruixue Wang, Elise Arellano-Thompson, Alex Saunders, Lucas Belury, Prashanti Sharma, Ariful Islam, Beth Tellman
This work is funded by NASA THP (Award #80NSSC21K1341).
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