doi:10.1016/j.jag.2024.104034 (explore)

doi:10.1016/j.jag.2024.104034

Full identifier: https://doi.org/10.1016/j.jag.2024.104034

References

Nanopublication	Part	Subject	Predicate	Object	Published By	Published On
https://w3id.org/sciencelive/np/RAicgrnxx5... RAicgrnxx5	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.1016/j.jag.2024.104034	http://www.w3.org/2000/01/rdf-schema#comment comment	The Fire_cci51 dataset is available at https://dx.doi.org/10.5285/b1bd715112ca43ab948226d11d72b85e and MODIS MCD64A1 can be found at HTTPS://DOI.ORG/10.5067/MODIS/MCD64A1.061	(unknown)	2026-03-01T20:22:03.850Z
https://w3id.org/sciencelive/np/RAicgrnxx5... RAicgrnxx5	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	doi:10.1016/j.jag.2024.104034	http://purl.org/spar/cito/hasQuotedText hasQuotedText	Popular global wildfire burnt area products, like Fire_cci51 and MODIS MCD64A1, are frequently used to examine wildfire dynamics, evaluate fire impacts on ecosystems, and improve fire forecasts (Chen et al., 2023)	(unknown)	2026-03-01T20:22:03.850Z
https://w3id.org/sciencelive/np/RAicgrnxx5... RAicgrnxx5	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	https://w3id.org/np/o/ntemplate/CREATOR CREATOR	http://purl.org/spar/cito/quotes quotes	doi:10.1016/j.jag.2024.104034	(unknown)	2026-03-01T20:22:03.850Z
https://w3id.org/sciencelive/np/RAWH-UbI2t... RAWH-UbI2t	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/Bi-tempor...single-input%20U-Net%20models. Bi-temporal input incorporating both pre-fire and post-fire Sentinel-2 imagery enhanced model performance across diverse environmental areas compared to traditional single-input U-Net models.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RA50Jdjkbj... RA50Jdjkbj	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20com...the%20Chico%20wildfire%20case. The combination of Dice Loss and Focal Loss as the loss function significantly improved model performance in handling imbalanced datasets, with BiAU-Net achieving a Balanced Accuracy score of 0.965 compared to 0.930 for BiU-Net in the Chico wildfire case.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAdgNrDWah... RAdgNrDWah	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/Attention...xels%20are%20mixed%20together. Attention mechanisms in the U-Net architecture enabled the model to focus on burnt areas and improve accuracy and efficiency, especially in detecting edges and small areas where burnt and non-burnt pixels are mixed together.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RADZdyAENx... RADZdyAENx	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20BiA...lation%20Coefficient%20values. The BiAU-Net model achieved the highest overall performance compared to state-of-the-art wildfire burnt area detection models, as evidenced by the highest F1-score and Matthews Correlation Coefficient values.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RAp5gH3TiU... RAp5gH3TiU	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/BiAU-Net%...en%20overlook%20small%20fires. BiAU-Net using Sentinel-2 imagery at 20 m spatial resolution offers higher accuracy than global wildfire products Fire_cci51 and MODIS MCD64A1, which have limitations at 250 m and 500 m resolution and often overlook small fires.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RA2WfRb51B... RA2WfRb51B	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/The%20BiA...20%20m%20spatial%20resolution. The BiAU-Net model was trained using Sentinel-2 MSI Level 2A product with a false color band combination of Band12 (SWIR: 2114.9-2289.9 μm), Band11 (SWIR: 1568.2-1659.2 μm), and Band8A (Narrow NIR: 854.2-875.2 μm) at 20 m spatial resolution.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
https://w3id.org/sciencelive/np/RA2Vk9rSp5... RA2Vk9rSp5	links a nanopublication to its assertion http://www.nanopub.org/nschema#hasAssertion assertion	http://purl.org/aida/For%20the...on%20Coefficient%20of%200.929. For the Chico, California test area, BiAU-Net with DLoss+FLoss achieved an Overall Accuracy of 0.968, Precision of 0.974, Recall of 0.949, F1-score of 0.961, Balanced Accuracy of 0.965, Kappa Coefficient of 0.934, and Matthews Correlation Coefficient of 0.929.	http://purl.org/spar/cito/obtainsSupportFrom obtainsSupportFrom	doi:10.1016/j.jag.2024.104034	claude-ai-agent	2026-03-01T17:22:03.000Z
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