diff --git a/_sources/content/config.md b/_sources/content/config.md index 808b6d6..7835fbc 100644 --- a/_sources/content/config.md +++ b/_sources/content/config.md @@ -49,10 +49,13 @@ This work is an extension of the [FDL Europe 2019](https://fdleurope.org/) *"Dis > G. Mateo-Garcia, J. Veitch-Michaelis, L. Smith, S. Oprea, G. Schumann, Y. Gal, Baydin G.A., Backes D. [Towards global flood mapping onboard low cost satellites with machine learning](https://www.nature.com/articles/s41598-021-86650-z). _Scientific Reports 11, 7249_ (2021). DOI: 10.1038/s41598-021-86650-z -This work has been further extended in: +FDL work has been further extended in the following paper where better models are proposed and trained on a curated version of the *WorldFloods* dataset. > E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7). _Scientific Reports 13, 20316_ (2023). DOI: 10.1038/s41598-023-47595-7. +Additionally, ML4Floods models have been [deployed onboard a D-Orbit satellite](https://philab.esa.int/esa-explores-cognitive-computing-in-space-with-fdl-breakthrough-experiments/) where we conducted several experiments published in: + +> Mateo-Garcia, G., Veitch-Michaelis, J., Purcell, C., Longepe, N., Reid, S., Anlind, A., Bruhn, F., Parr, J., & Mathieu, P. P. , [In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery](https://www.nature.com/articles/s41598-023-34436-w), _Scientific Reports 13, 10391_ (2023). DOI: 10.1038/s41598-023-34436-w. ML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by [Trillium Technologies](http://trillium.tech/). It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033). @@ -74,6 +77,21 @@ ML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by [ year = {2023}, pages = {20316}, } +@article{mateo-garcia_inorbit_2023, + title = {In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery}, + volume = {13}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-023-34436-w}, + doi = {10.1038/s41598-023-34436-w}, + number = {1}, + urldate = {2023-06-27}, + journal = {Scientific Reports}, + author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Josh and Purcell, Cormac and Longepe, Nicolas and Reid, Simon and Anlind, Alice and Bruhn, Fredrik and Parr, James and Mathieu, Pierre Philippe}, + month = jun, + year = {2023}, + pages = {10391}, +} + @article{mateo-garcia_towards_2021, title = {Towards global flood mapping onboard low cost satellites with machine learning}, volume = {11}, diff --git a/content/config.html b/content/config.html index a137656..ac9b25e 100644 --- a/content/config.html +++ b/content/config.html @@ -426,10 +426,14 @@

About#

G. Mateo-Garcia, J. Veitch-Michaelis, L. Smith, S. Oprea, G. Schumann, Y. Gal, Baydin G.A., Backes D. Towards global flood mapping onboard low cost satellites with machine learning. Scientific Reports 11, 7249 (2021). DOI: 10.1038/s41598-021-86650-z

-

This work has been further extended in:

+

FDL work has been further extended in the following paper where better models are proposed and trained on a curated version of the WorldFloods dataset.

E. Portalés-Julià, G. Mateo-García, C. Purcell, and L. Gómez-Chova Global flood extent segmentation in optical satellite images. Scientific Reports 13, 20316 (2023). DOI: 10.1038/s41598-023-47595-7.

+

Additionally, ML4Floods models have been deployed onboard a D-Orbit satellite where we conducted several experiments published in:

+
+

Mateo-Garcia, G., Veitch-Michaelis, J., Purcell, C., Longepe, N., Reid, S., Anlind, A., Bruhn, F., Parr, J., & Mathieu, P. P. , In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery, Scientific Reports 13, 10391 (2023). DOI: 10.1038/s41598-023-34436-w.

+

ML4Floods has been funded by the United Kingdom Space Agency (UKSA) and led by Trillium Technologies. It has also been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033).

@@ -448,6 +452,21 @@

Citationyear = {2023}, pages = {20316}, } +@article{mateo-garcia_inorbit_2023, + title = {In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery}, + volume = {13}, + issn = {2045-2322}, + url = {https://www.nature.com/articles/s41598-023-34436-w}, + doi = {10.1038/s41598-023-34436-w}, + number = {1}, + urldate = {2023-06-27}, + journal = {Scientific Reports}, + author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Josh and Purcell, Cormac and Longepe, Nicolas and Reid, Simon and Anlind, Alice and Bruhn, Fredrik and Parr, James and Mathieu, Pierre Philippe}, + month = jun, + year = {2023}, + pages = {10391}, +} + @article{mateo-garcia_towards_2021, title = {Towards global flood mapping onboard low cost satellites with machine learning}, volume = {11}, diff --git a/searchindex.js b/searchindex.js index a1f99d4..69129b5 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["README", "content/config", 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