remote sensing sentinel2 onboard image-segmentation license:cc-by-nc-4.0 region:us
---
license: cc-by-nc-4.0
pipeline_tag: image-segmentation
tags:
- remote sensing
- sentinel2
- onboard
---

# DTACS 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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For surface reflectance (SR) estimation we include the models:
* **DTACS S2 bands** SR model with all the S2 bands. `DTACS_SR_sentinel2.pt`
* **DTACS phi-sat II bands**  SR model with overlapping bands of Phi-Sat II and Sentinel-2. `DTACS_SR_phisat2.pt`
* **DTACS Proba-V bands**  SR model with Blue, Red, NIR and SWIR bands. `DTACS_SR_probav.pt`
* **DTACS PlanetScope bands**  SR model with Blue, Green, Red and NIR bands. `DTACS_SR_planetscope.pt`

For cloud detection (CD):
* **DTACS S2 bands**: CD model with all the S2 bands. `DTACS_CLOUD_ALL.pt`
* **DTACS RGBNIR bands**: CD model with Red, Green, Blue and NIR bands. `DTACS_CLOUD_RGBNIR.pt`

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

## Licence
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by-nc.png" alt="licence" width="60"/>

All pre-trained models in this repository are released under a [Creative Commons non-commercial licence](https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt) 
