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Found 8 papers, 3 papers with code
Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping
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.
Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks
By extension, effective representation of material and texture can describe other semantic classes strongly associated with said material and texture.
Shape From Sky: Polarimetric Normal Recovery Under the Sky
That is, we show that the unique polarization pattern encoded in the polarimetric appearance of an object captured under the sky can be decoded to reveal the surface normal at each pixel.
Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
To address the mounting destruction caused by floods in climate-vulnerable regions, we propose Street to Cloud, a machine learning pipeline for incorporating crowdsourced ground truth data into the segmentation of satellite imagery of floods.
H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement
We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation.
Angular Luminance for Material Segmentation
We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing.
Material Segmentation of Multi-View Satellite Imagery
The residuals are computed by differencing the sparse-sampled reflectance function with a dictionary of pre-defined dense-sampled reflectance functions.
