Matches in Nanopublications for { ?s ?p <https://www.wikidata.org/wiki/Q4302480> ?g. }
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- The%20BiAU-Net%20model%20demonstrated%20good%20generalizability%20across%20five%20testing%20areas%20in%20different%20continents%20%28United%20States%2C%20Spain%2C%20Australia%2C%20Indonesia%2C%20and%20Kenya%29%20when%20trained%20on%20a%20single%20wildfire%20event%20in%20California. about Q4302480 assertion.
- Bi-temporal%20input%20incorporating%20both%20pre-fire%20and%20post-fire%20Sentinel-2%20imagery%20enhanced%20model%20performance%20across%20diverse%20environmental%20areas%20compared%20to%20traditional%20single-input%20U-Net%20models. about Q4302480 assertion.
- The%20BiAU-Net%20model%20achieved%20the%20highest%20overall%20performance%20compared%20to%20state-of-the-art%20wildfire%20burnt%20area%20detection%20models%2C%20as%20evidenced%20by%20the%20highest%20F1-score%20and%20Matthews%20Correlation%20Coefficient%20values. about Q4302480 assertion.
- BiAU-Net%20using%20Sentinel-2%20imagery%20at%2020%20m%20spatial%20resolution%20offers%20higher%20accuracy%20than%20global%20wildfire%20products%20Fire_cci51%20and%20MODIS%20MCD64A1%2C%20which%20have%20limitations%20at%20250%20m%20and%20500%20m%20resolution%20and%20often%20overlook%20small%20fires. about Q4302480 assertion.
- The%20BiAU-Net%20model%20was%20trained%20using%20Sentinel-2%20MSI%20Level%202A%20product%20with%20a%20false%20color%20band%20combination%20of%20Band12%20%28SWIR%3A%202114.9-2289.9%20%CE%BCm%29%2C%20Band11%20%28SWIR%3A%201568.2-1659.2%20%CE%BCm%29%2C%20and%20Band8A%20%28Narrow%20NIR%3A%20854.2-875.2%20%CE%BCm%29%20at%2020%20m%20spatial%20resolution. about Q4302480 assertion.
- BiAU-Net%20demonstrated%20superior%20performance%20in%20detecting%20small%20burnt%20areas%20and%20boundary%20regions%20where%20burnt%20and%20non-burnt%20pixels%20are%20mixed%20together%2C%20addressing%20key%20limitations%20of%20previous%20deep%20learning%20models. about Q4302480 assertion.
- In%20test%20areas%20with%20significantly%20different%20environmental%20conditions%20from%20the%20training%20area%20%28such%20as%20Nairobi%2C%20Kenya%20with%20red%20soil%29%2C%20bi-temporal%20input%20enabled%20the%20model%20to%20consider%20spectral%20changes%20caused%20by%20wildfires%20as%20features%2C%20achieving%20an%20Overall%20Accuracy%20of%200.816%20despite%20minimal%20spectral%20similarity. about Q4302480 assertion.
- For%20areas%20with%20similar%20environmental%20conditions%20to%20the%20training%20area%20%28Bredbo%2C%20Australia%20and%20Tarragona%2C%20Spain%29%2C%20BiAU-Net%20achieved%20Overall%20Accuracy%20of%200.941%20and%200.939%20respectively%2C%20demonstrating%20robust%20performance%20across%20Mediterranean%20and%20temperate%20forest%20ecosystems. about Q4302480 assertion.