This consolidated repository outlines our research work on lake ice monitoring using machine (deep) learning approaches. These works are part of the two projects (LIP1, LIP2) at ETH Zurich (funded by MeteoSwiss under the GCOS Switzerland framework).
PhD thesis of Manu Tom can be accessed here.
1. Lake Ice Detection from Sentinel-1 SAR with Deep Learning
2. Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning
3. Photi-LakeIce Webcam Dataset
4. Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery
5. Learning a Joint Embedding of Multiple Satellite Sensorsa: A Case Study for Lake Ice Monitoring
6. Lake Ice Detection in Crowd-sourced images using Deep-U-Lab
7. Lake Ice Monitoring with Webcams using Tiramisu Network
8. Automatic Lake Detection using Deep-U-Lab
This work was presented at the ISPRS Congress 2020. Access the paper here.
We use the Sentinel-1 Synthetic Aperture Radar data downloaded from the Google Earth Engine platform to detect lake ice with the state-of-the-art semantic segmentation network Deeplab v3+. Here, we model lake ice detection as a 2-class (frozen, non-frozen) classification problem. Access the tensorflow-based repository here to browse the CNN code, pre-trained model etc.
Please cite the following paper if you use this project in your research:
@article{tom_aguilar_2020:isprs,
author = {Manu Tom and Roberto Aguilar and Pascal Imhof and Silvan Leinss and Emmanuel Baltsavias and Konrad Schindler},
title = {Lake Ice Detection from Sentinel-1 SAR with Deep Learning},
journal = {ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.},
year = {2020},
volume = {V-3-2020},
pages = {409--416},
}
This work was published in the MDPI Remote Sensing journal. Access the paper here.
We detect lake ice in MODIS and VIIRS optical satellite images using support vector machines aided by an automatic feature selection by XGBoost. Here, we cast lake ice detection as a 2-class (frozen, non-frozen) classification problem. Additionally, we use the freely available (online) webcam data to monitor lake ice using the Deep-U-Lab network. Access the tensorflow-based repository here to browse the Deep-U-Lab code, pre-trained model etc.
Kindly cite the following paper, if you use this project in your research:
@article{tom_prabha_2020:isprs,
author = {Manu Tom and Rajanie Prabha and Tianyu Wu and Emmanuel Baltsavias and Laura Leal-Taixe and Konrad Schindler},
title = {Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning},
journal = {Remote Sens.},
year = {2020},
volume = {12},
issue = {21},
pages = {3555},
}
The dataset and pre-trained model (Deep-U-Lab) can be found here.
Kindly cite the following paper, if you use this dataset in your research:
@article{tom_prabha_2020:isprs,
author = {Manu Tom and Rajanie Prabha and Tianyu Wu and Emmanuel Baltsavias and Laura Leal-Taixe and Konrad Schindler},
title = {Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning},
journal = {Remote Sens.},
year = {2020},
volume = {12},
issue = {21},
pages = {3555},
}
This work was published in the Springer PFG journal. Access the paper here.
We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000–2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. From the ice maps, we derive long-term LIP trends. We find a change in complete freeze duration of −0.76 and −0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations.
Code coming soon!
Please cite the following paper, if you use this project in your research:
@article{tom_wu_2022:pfg,
author = {Manu Tom and Tianyu Wu and Emmanuel Baltsavias and Konrad Schindler},
title = {Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery},
journal = {PFG},
year = {2022},
volume = {90},
pages = {413–431},
}
This work was published in the IEEE Transactions on Geoscience and Remote Sensing. Access the paper here.
In this work, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. To reach the temporal resolution requirement of the Swiss GCOS office, we combine three image sources: Sentinel-1 SAR, Terra MODIS, VIIRS. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of <1.5 days.
Code will be published soon!
Please cite the following paper, if you use this project in your research:
@article{tom_jiang_2022:tgrs,
author = {Manu Tom and Yuchang Jiang and Emmanuel Baltsavias and Konrad Schindler},
title = {Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
year = {2022},
volume = {60, Art no. 4306315},
pages = {1-15},
}
More details here.
Kindly cite the following paper, if you use this research in your work:
@article{prabha_tom_2020:isprs,
author = {Rajanie Prabha and Manu Tom and Mathias Rothermel and Emmanuel Baltsavias and Laura Leal-Taixe and Konrad Schindler},
title = {Lake Ice Monitoring with Webcams and Crowd-Sourced Images},
journal = {ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.},
year = {2020},
volume = {V-2-2020},
pages = {549--556},
}
More details here.
Please cite the following paper, if you use this project in your research:
@article{xiao_rothermel_2018:isprs,
author = {Muyan Xiao and Mathias Rothermel and Manu Tom and Silvano Galliani and Emmanuel Baltsavias and Konrad Schindler},
title = {Lake Ice Monitoring with Webcams},
journal = {ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.},
year = {2018},
volume = {IV-2},
pages = {311--317},
}
Details coming soon ...
- Tom, M.: Lake Ice Monitoring from Space and Earth with Machine Learning. Doctoral Thesis, ETH Zurich, 2021.
- Tom, M., Suetterlin, M., Bouffard, D., Rothermel, M., Wunderle, S., and Baltsavias, E.: Integrated monitoring of ice in selected Swiss lakes. Final Project Report, 2020.
- Tom, M., Baltsavias, E., and Schindler, K.: Integrated lake ice monitoring and generation of sustainable, reliable, long time series. Final Project Report, 2020.
- Tom, M., Kälin, U., Sütterlin, M., Baltsavias, E., and Schindler, K.: Lake ice detection in low-resolution optical satellite images, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 279–286, https://doi.org/10.5194/isprs-annals-IV-2-279-2018, 2018.
- Rothermel, M., Xiao, M., Tom, M., Baltsavias, E., and Schindler, K. : Monitoring der vereisung von Schweizer seen mit webcams, Geomatik Schweiz, 9/2018, 268--271, 2018.
- Tom, M., Sütterlin, M., Bouffard, D., Rothermel, M., Hamann, U., Duguay-Tetzlaff, A., Wunderle, S., Baltsavias, E.: Integrated lake ice monitoring in Swiss lakes, EUMETSAT - Meteorological Satellite Conference, Tallinn, Estonia, 17-21 September 2018.
- Tom M., Lanaras, C., Baltsavias, E., and Schindler, K.: Ice detection in Swiss lakes using MODIS data, In Proceedings of the Asian Conference on Remote Sensing, New Delhi, India, 23–27 October 2017.
MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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Author: Manu Tom