
# DTACS main trained models

This repository contains the trained models of the publication:


> Mateo-García, G., Aybar, C., Acciarini, G., Růžička, V., Meoni, G., Longépé, N., and Gómez-Chova, L. (2023). Onboard Cloud Detection and Atmospheric Correction with Deep Learning Emulators. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 1875–1878. DOI: [10.1109/IGARSS52108.2023.10282605](https://doi.org/10.1109/IGARSS52108.2023.10282605)


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<img src="./figure02.png" width="40%">
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We include the trained models:
* **DTACS S2 bands** SR model with all the S2 bands
* **DTACS phi-sat II bands**  SR model with RGBN S2 bands


Examples of use here: https://github.com/spaceml-org/DTACSNet

{"license": "cc-by-nc-4.0", "tags": ["remote sensing", "sentinel2", "onboard"], "pipeline_tag": "image-segmentation"}
remote sensing sentinel2 onboard image-segmentation license:cc-by-nc-4.0 region:us