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dtacs

DTACS: Onboard atmospheric correction emulator for S2 and PhiSat

https://github.com/spaceml-org/dtacsnet

Science Score: 67.0%

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    Links to: ieee.org
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    Low similarity (12.1%) to scientific vocabulary
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Repository

DTACS: Onboard atmospheric correction emulator for S2 and PhiSat

Basic Info
Statistics
  • Stars: 15
  • Watchers: 7
  • Forks: 2
  • Open Issues: 0
  • Releases: 1
Created about 3 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation

README.md

Article DOI:10.1109/JSTARS.2024.3480520 GitHub release (latest SemVer including pre-releases) PyPI PyPI - Python Version PyPI - License docs

DTACSNet: Onboard Cloud Detection and Atmospheric Correction With Efficient Deep Learning Models

Cesar Aybar§, Gonzalo Mateo-García§, Giacomo Acciarini§, Vit Ruzicka, Gabriele Meoni, Nicolas Longepe, Luis Gómez-Chova § development contribution

10.1109/JSTARS.2024.3480520

This repo contains an open implementation to run inference with DTACSNet models for atmospheric correction. This repo and trained models are released under a Creative Commons non-commercial licence licence

Install ⚙️: bash pip install dtacs

Run:

```python from dtacs.modelwrapper import ACModel modelatmosphericcorrection = ACModel(modelname="CNNcorrectorphisat2") modelatmosphericcorrection.load_weights()

acoutput = modelatmosphericcorrection.predict(l1ctoa_s2) ```

awesome atmospheric correction The figure above shows a sample of Sentinel-2 level 1C, DTACSNet model output and Sentinel-2 level 2A in the RGB (first row) and in the SWIR, NIR, Red (last row) composites.

Tutorials: * Sentinel-2 inference tutorial. Open In Colab * PhiSat II inference tutorial. Open In Colab

Citation

If you find this work useful for your research, please consider citing our work:

bibtex @article{aybar_onboard_2024, title = {Onboard {Cloud} {Detection} and {Atmospheric} {Correction} {With} {Efficient} {Deep} {Learning} {Models}}, volume = {17}, issn = {2151-1535}, url = {https://ieeexplore.ieee.org/abstract/document/10716772}, doi = {10.1109/JSTARS.2024.3480520}, urldate = {2024-11-12}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, author = {Aybar, Cesar and Mateo-García, Gonzalo and Acciarini, Giacomo and Růžička, Vít and Meoni, Gabriele and Longépé, Nicolas and Gómez-Chova, Luis}, year = {2024}, note = {Conference Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, pages = {19518--19529} }

Acknowledgments

DTACSNet has been developed by Trillium Technologies. It has been funded by ESA Cognitive Cloud Computing in Space initiative project number D-TACS I-2022-00380.

Owner

  • Name: SpaceML
  • Login: spaceml-org
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Mateo-Garcia"
  given-names: "Gonzalo"
  orcid: "https://orcid.org/0000-0002-0569-393X"
  affiliation: "Universitat de Valencia"
title: "dtacs"
version: 1.0.9
doi: 10.1109/JSTARS.2024.3480520
date-released: 2024-12-01
url: "https://github.com/spaceml-org/DTACSNet"

GitHub Events

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  • Release event: 1
  • Watch event: 2
  • Member event: 1
  • Push event: 16
  • Create event: 2
Last Year
  • Release event: 1
  • Watch event: 2
  • Member event: 1
  • Push event: 16
  • Create event: 2

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 28
  • Total Committers: 1
  • Avg Commits per committer: 28.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 28
  • Committers: 1
  • Avg Commits per committer: 28.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Gonzalo Mateo García g****8@g****m 28

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Last synced: 8 months ago

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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 19 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
pypi.org: dtacs

🛰️ Cloud Detection and Atmospheric Correction With Efficient Deep Learning Models

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 19 Last month
Rankings
Dependent packages count: 9.9%
Average: 32.9%
Dependent repos count: 56.0%
Maintainers (1)
gonzmg88
Last synced: 7 months ago

Dependencies

requirements.txt pypi
  • numpy *
  • rasterio *
  • segmentation_models_pytorch *
  • torch >=1.13