Large Scale Masked Autoencoding for Reducing Label Requirements on SAR
Data
- URL: http://arxiv.org/abs/2310.00826v3
- Date: Sun, 3 Dec 2023 00:28:25 GMT
- Title: Large Scale Masked Autoencoding for Reducing Label Requirements on SAR
Data
- Authors: Matt Allen, Francisco Dorr, Joseph A. Gallego-Mejia, Laura
Mart\'inez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Ra\'ul Ramos-Poll\'an
- Abstract summary: We apply a self-supervised pretraining scheme, masked autoencoding, to SAR amplitude data covering 8.7% of the Earth's land surface area.
We show that the use of this pretraining scheme reduces labelling requirements for the downstream tasks by more than an order of magnitude.
Our findings significantly advance climate change mitigation by facilitating the development of task and region-specific SAR models.
- Score: 5.057850174013128
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Satellite-based remote sensing is instrumental in the monitoring and
mitigation of the effects of anthropogenic climate change. Large scale, high
resolution data derived from these sensors can be used to inform intervention
and policy decision making, but the timeliness and accuracy of these
interventions is limited by use of optical data, which cannot operate at night
and is affected by adverse weather conditions. Synthetic Aperture Radar (SAR)
offers a robust alternative to optical data, but its associated complexities
limit the scope of labelled data generation for traditional deep learning. In
this work, we apply a self-supervised pretraining scheme, masked autoencoding,
to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tune
the pretrained weights on two downstream tasks crucial to monitoring climate
change - vegetation cover prediction and land cover classification. We show
that the use of this pretraining scheme reduces labelling requirements for the
downstream tasks by more than an order of magnitude, and that this pretraining
generalises geographically, with the performance gain increasing when tuned
downstream on regions outside the pretraining set. Our findings significantly
advance climate change mitigation by facilitating the development of task and
region-specific SAR models, allowing local communities and organizations to
deploy tailored solutions for rapid, accurate monitoring of climate change
effects.
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