CoastSat image classification training data CoastSat is an open-source global shoreline mapping toolbox, available at https://github.com/kvos/CoastSat, which enables users to extract time-series of shoreline change from 30+ years of publicly available satellite imagery (Landsat 5, 7, 8 and Sentinel-2). The automated shoreline extraction relies on a classifier (Multilayer Perceptron from scikit-learn) which labels each pixels on the images with one of four classes: sand, water, white-water and other land features. The data used to train the classifier is stored here, the README.md file provides information on the data organisation and content of each file
The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-...
Satellite earth observation data has become fundamental in efforts to map coastal change at large ge...
Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Eros...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various ...
Shoreline extraction provides the boundary information of land and water, which helps monitor erosio...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
Description 1018 images and 1018 associated labels for semantic segmentation of Sentinel-2 and Land...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
© 2022 National Authority of Remote Sensing & Space ScienceMachine learning (ML) classifiers provide...
Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. S...
The author has identified the following significant results. Studies of cover distribution along Del...
Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends o...
Remote sensing has been used widely to map the shoreline and offers the potential to update maps fre...
Today's coastal zones are densely inhabited as the majority of the world's population lives in these...
The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-...
Satellite earth observation data has become fundamental in efforts to map coastal change at large ge...
Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Eros...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various ...
Shoreline extraction provides the boundary information of land and water, which helps monitor erosio...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
Description 1018 images and 1018 associated labels for semantic segmentation of Sentinel-2 and Land...
CoastSat is an open-source software toolkit written in Python that enables users to obtain time-seri...
© 2022 National Authority of Remote Sensing & Space ScienceMachine learning (ML) classifiers provide...
Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. S...
The author has identified the following significant results. Studies of cover distribution along Del...
Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends o...
Remote sensing has been used widely to map the shoreline and offers the potential to update maps fre...
Today's coastal zones are densely inhabited as the majority of the world's population lives in these...
The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-...
Satellite earth observation data has become fundamental in efforts to map coastal change at large ge...
Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Eros...