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Found 5 papers, 1 papers with code
Data-Centric Machine Learning for Geospatial Remote Sensing Data
These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
Leveraging Citizen Science for Flood Extent Detection using Machine Learning Benchmark Dataset
Specifically, Sentinel-1 C-Band Synthetic Aperture Radar (SAR) imagery has proven to be useful in detecting water bodies due to low backscatter of water features in both co-polarized and cross-polarized SAR imagery.
EOD: The IEEE GRSS Earth Observation Database
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community.
Random Ferns for Semantic Segmentation of PolSAR Images
Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection.
There is no data like more data -- current status of machine learning datasets in remote sensing
Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation of remote sensing data.
