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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16740295 |
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| _version_ | 1866901267465371648 |
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| author | Goldstein, Evan Farquhar, Elizabeth, Liz Anarde, Katherine |
| author_facet | Goldstein, Evan Farquhar, Elizabeth, Liz Anarde, Katherine |
| contents | <p>This dataset is was used to train the ML model presented in the Paper:</p> <p>"Detection of coastal flooding with TinyCamML: a low-cost, privacy-preserving cellular-connected camera with onboard ML"</p> <p>A total of 17,448 images are divided into three folders: Train/Validation/Test.</p> <p>The code to develop the model is on Github:</p> <p>https://github.com/TinyCamML/Flood-Model/blob/main/src/Classification_asCat-Octt2024.ipynb</p> <p>Note that some of the images come from:</p> <p>Wang, Y. (2024). Urban Flood Image Dataset, HydroShare, http://www.hydroshare.org/resource/24866122a6ee456c8f7c80aa87a9abcb</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16740295 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dataset for Detection of coastal flooding with TinyCamML: a low-cost, privacy-preserving cellular-connected camera with onboard ML Goldstein, Evan Farquhar, Elizabeth, Liz Anarde, Katherine <p>This dataset is was used to train the ML model presented in the Paper:</p> <p>"Detection of coastal flooding with TinyCamML: a low-cost, privacy-preserving cellular-connected camera with onboard ML"</p> <p>A total of 17,448 images are divided into three folders: Train/Validation/Test.</p> <p>The code to develop the model is on Github:</p> <p>https://github.com/TinyCamML/Flood-Model/blob/main/src/Classification_asCat-Octt2024.ipynb</p> <p>Note that some of the images come from:</p> <p>Wang, Y. (2024). Urban Flood Image Dataset, HydroShare, http://www.hydroshare.org/resource/24866122a6ee456c8f7c80aa87a9abcb</p> |
| title | Dataset for Detection of coastal flooding with TinyCamML: a low-cost, privacy-preserving cellular-connected camera with onboard ML |
| url | https://doi.org/10.5281/zenodo.16740295 |