Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose ...Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.展开更多
针对热带气旋灾害的复杂性和不确定性,文章基于贝叶斯网络和地理信息系统(geographic information system,GIS)提出了一种新的热带气旋灾害风险评估模型。该模型能够从客观历史数据中自动挖掘灾害影响因素间的因果关系,并以概率形式进...针对热带气旋灾害的复杂性和不确定性,文章基于贝叶斯网络和地理信息系统(geographic information system,GIS)提出了一种新的热带气旋灾害风险评估模型。该模型能够从客观历史数据中自动挖掘灾害影响因素间的因果关系,并以概率形式进行表达和推理,从而对不确定灾害风险进行评估预测。基于1980—2016年中国东南沿海三省(广东、福建、浙江)的热带气旋灾害历史数据进行风险评估实验,选取致灾因子危险性、孕灾环境敏感性、承灾体脆弱性3个方面共计12个评估指标作为模型输入,直接经济损失量化为灾害风险等级作为模型输出,构建基于贝叶斯网络的风险评估模型。然后利用2017—2021年热带气旋灾害数据进行模型检验,评估预测的准确率为80.75%。模型预测的极低、低、中、高和极高风险的相对误差分别为27.72%、8.45%、18.58%、16.52%和19.12%,风险预测值的区划结果在空间形态上与实际灾害损失分布高度一致。此外,还将评估模型构建方法应用于“莫兰蒂”台风灾害个例的风险评估。结果表明,模型评估出的灾害高风险和极高风险区域与实际灾情报告基本一致。由此可见,本研究建立的热带气旋灾害风险评估模型具有较高的准确率和可信度,为热带气旋灾害风险评估提供了一种新的方法途径和技术支撑。展开更多
基金funded by the Chongqing Normal University Startup Foundation for PhD(22XLB021)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2023B40).
文摘Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmentalmonitoring.Addressing the limitations of conventional convolutional neural networks,we propose an innovative transformer-based method.This method leverages transformers,which are adept at processing data sequences,to enhance cloud detection accuracy.Additionally,we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction,thereby aiding in the retention of critical details often lost during cloud detection.Our extensive experimental validation shows that our approach significantly outperforms established models,excelling in high-resolution feature extraction and precise cloud segmentation.By integrating Positional Visual Transformers(PVT)with this architecture,our method advances high-resolution feature delineation and segmentation accuracy.Ultimately,our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains.
文摘针对热带气旋灾害的复杂性和不确定性,文章基于贝叶斯网络和地理信息系统(geographic information system,GIS)提出了一种新的热带气旋灾害风险评估模型。该模型能够从客观历史数据中自动挖掘灾害影响因素间的因果关系,并以概率形式进行表达和推理,从而对不确定灾害风险进行评估预测。基于1980—2016年中国东南沿海三省(广东、福建、浙江)的热带气旋灾害历史数据进行风险评估实验,选取致灾因子危险性、孕灾环境敏感性、承灾体脆弱性3个方面共计12个评估指标作为模型输入,直接经济损失量化为灾害风险等级作为模型输出,构建基于贝叶斯网络的风险评估模型。然后利用2017—2021年热带气旋灾害数据进行模型检验,评估预测的准确率为80.75%。模型预测的极低、低、中、高和极高风险的相对误差分别为27.72%、8.45%、18.58%、16.52%和19.12%,风险预测值的区划结果在空间形态上与实际灾害损失分布高度一致。此外,还将评估模型构建方法应用于“莫兰蒂”台风灾害个例的风险评估。结果表明,模型评估出的灾害高风险和极高风险区域与实际灾情报告基本一致。由此可见,本研究建立的热带气旋灾害风险评估模型具有较高的准确率和可信度,为热带气旋灾害风险评估提供了一种新的方法途径和技术支撑。
