Matches in Nanopublications for { ?s <http://schema.org/about> ?o ?g. }
- 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.
- 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 Q199687 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 Q199687 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q197536 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q197536 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q169950 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q169950 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q99 assertion.
- For%20the%20Chico%2C%20California%20test%20area%2C%20BiAU-Net%20with%20DLoss%2BFLoss%20achieved%20an%20Overall%20Accuracy%20of%200.968%2C%20Precision%20of%200.974%2C%20Recall%20of%200.949%2C%20F1-score%20of%200.961%2C%20Balanced%20Accuracy%20of%200.965%2C%20Kappa%20Coefficient%20of%200.934%2C%20and%20Matthews%20Correlation%20Coefficient%20of%200.929. about Q99 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q197536 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q197536 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q2539 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q2539 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q192776 assertion.
- The%20model%20training%20utilized%20a%20batch%20size%20of%206%2C%20conducted%2050%20epochs%2C%20initialized%20the%20learning%20rate%20at%200.1%20with%20scheduled%20decrease%20by%20a%20factor%20of%200.5%20between%20the%2030th%20to%2050th%20epochs%2C%20and%20used%20Stochastic%20Gradient%20Descent%20optimizer. about Q192776 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q169950 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q169950 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q2539 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q2539 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q192776 assertion.
- BiAU-Net%20outperformed%20traditional%20machine%20learning%20models%20including%20Support%20Vector%20Machines%20and%20Random%20Forest%2C%20which%20require%20handcrafted%20engineering%20and%20often%20perform%20well%20in%20training%20areas%20but%20poorly%20when%20transferred%20to%20different%20areas%20due%20to%20varying%20environments. about Q192776 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q197536 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q197536 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q169950 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q169950 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q99 assertion.
- The%20training%20dataset%20for%20BiAU-Net%20included%20306%20tiles%20from%20the%20Chico%20wildfire%20with%20278%20tiles%20%28approximately%2091%25%29%20used%20for%20training%20and%2028%20tiles%20%28approximately%209%25%29%20for%20validation%2C%20with%20each%20tile%20including%20256%C3%97256%20pixels. about Q99 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 Q197536 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 Q197536 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 Q169950 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 Q169950 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.
- 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.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q309823 assertion.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q309823 assertion.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q169950 assertion.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q169950 assertion.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q1130645 assertion.
- The%20code%20and%20dataset%20for%20BiAU-Net%20are%20publicly%20available%20on%20GitHub%20at%20https%3A%2F%2Fgithub.com%2FTangSui122%2FBi-temporal-Wildfire-Burnt-Area-Detection.git%2C%20facilitating%20reproducibility%20and%20follow-on%20research%20efforts. about Q1130645 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q197536 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q197536 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q2539 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q2539 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q192776 assertion.
- Focal%20Loss%20addresses%20class%20imbalance%20by%20down-weighting%20easy-to-classify%20examples%20and%20placing%20more%20emphasis%20on%20hard-to-classify%20examples%20through%20tunable%20parameters%20%CE%B1%20%28weight%20between%20positive%20and%20negative%20samples%29%20and%20%CE%B3%20%28focusing%20parameter%20for%20adjusting%20loss%20magnitude%29. about Q192776 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 Q169950 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 Q169950 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.
- 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.
- 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 Q114 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 Q114 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q197536 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q197536 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q169950 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q169950 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q2539 assertion.
- Data%20augmentation%20methods%20including%20rotating%20from%2010%C2%B0%20left%20to%2010%C2%B0%20right%20with%20mirroring%2C%20reversing%20left%20and%20right%2C%20and%20zooming%20by%20a%20factor%20of%200.85%20were%20implemented%20to%20enhance%20model%20training. about Q2539 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 Q197536 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 Q197536 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 Q169950 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 Q169950 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.
- 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.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q197536 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q197536 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q169950 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q169950 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q192776 assertion.
- The%20BiAU-Net%20architecture%20incorporates%20attention%20gates%20at%20each%20up-sampling%20step%20that%20dynamically%20assess%20feature%20relevance%20to%20create%20attention%20weights%2C%20amplifying%20significant%20features%20and%20reducing%20the%20influence%20of%20unnecessary%20ones. about Q192776 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q197536 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q197536 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q192776 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q192776 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q199687 assertion.
- The%20model%20processes%20pre-fire%20and%20post-fire%20satellite%20images%20through%20two%20symmetrical%20convolutions%20and%20max-pooling%20in%20the%20encoder%20phase%20to%20extract%20features%2C%20with%20four%20rounds%20of%20down-sampling%20producing%20four%20pairs%20of%20pre-%20and%20post-fire%20feature%20maps%20across%20varying%20scales. about Q199687 assertion.
- ea792c69-9037-4d06-84a8-6fded7356e12 about 5338 assertion.
- ro-crate-metadata.json about ea792c69-9037-4d06-84a8-6fded7356e12 assertion.
- 24600867-23ca-4b97-be14-3aa63883c056 about 3949 assertion.
- ro-crate-metadata.json about 24600867-23ca-4b97-be14-3aa63883c056 assertion.
- 7c0fe5d9-6901-411b-b377-c8516f42058e about 3956 assertion.
- 7c0fe5d9-6901-411b-b377-c8516f42058e about 3949 assertion.
- ro-crate-metadata.json about 7c0fe5d9-6901-411b-b377-c8516f42058e assertion.
- 49a44dd4-efc3-45a0-8dd3-790081990133 about 3956 assertion.
- 49a44dd4-efc3-45a0-8dd3-790081990133 about 3949 assertion.
- ro-crate-metadata.json about 49a44dd4-efc3-45a0-8dd3-790081990133 assertion.
- d006ed2d-2fa9-438d-b830-a7d4aef81469 about 1892 assertion.
- d006ed2d-2fa9-438d-b830-a7d4aef81469 about 632 assertion.
- d006ed2d-2fa9-438d-b830-a7d4aef81469 about 3949 assertion.
- ro-crate-metadata.json about d006ed2d-2fa9-438d-b830-a7d4aef81469 assertion.
- e9de4f85-969b-411f-993b-bbb9178b37a9 about 7360 assertion.
- ro-crate-metadata.json about e9de4f85-969b-411f-993b-bbb9178b37a9 assertion.
- a2c3cebf-095b-441e-95b6-b6828932430c about 3949 assertion.
- ro-crate-metadata.json about a2c3cebf-095b-441e-95b6-b6828932430c assertion.
- 52654482-5442-45ea-a4b7-80a9af510c0b about 3952 assertion.
- ro-crate-metadata.json about 52654482-5442-45ea-a4b7-80a9af510c0b assertion.
- b6e01d7a-9f25-4b37-82df-32ef2e7171e3 about 3941 assertion.
- ro-crate-metadata.json about b6e01d7a-9f25-4b37-82df-32ef2e7171e3 assertion.
- 10dc322d-eedd-43ff-a4af-7adb6281cb6e about c_a935cf3f assertion.
- ro-crate-metadata.json about 10dc322d-eedd-43ff-a4af-7adb6281cb6e assertion.
- 972ba092-9239-4947-9bf6-495c53e57266 about c_a935cf3f assertion.