To effectively address the complexity of the environment,information uncertainty,and variability among decision-makers in the event of an enterprise emergency,a multi-granularity binary semantic-based emergency decisi...To effectively address the complexity of the environment,information uncertainty,and variability among decision-makers in the event of an enterprise emergency,a multi-granularity binary semantic-based emergency decision-making method is proposed.Decision-makers use preferred multi-granularity non-uniform linguistic scales combined with binary semantics to represent the evaluation information of key influencing factors.Secondly,the weights were determined based on the proposed method.Finally,the proposed method’s effectiveness is validated using a case study of a fire incident in a chemical company.展开更多
High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection...High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.展开更多
文摘To effectively address the complexity of the environment,information uncertainty,and variability among decision-makers in the event of an enterprise emergency,a multi-granularity binary semantic-based emergency decision-making method is proposed.Decision-makers use preferred multi-granularity non-uniform linguistic scales combined with binary semantics to represent the evaluation information of key influencing factors.Secondly,the weights were determined based on the proposed method.Finally,the proposed method’s effectiveness is validated using a case study of a fire incident in a chemical company.
基金supported by National Key Research and Development Program of China under grant number 2022YFB3903404National Natural Science Foundation of China under grant number 42325105,42071350LIESMARS Special Research Funding.
文摘High resolution satellite images are becoming increasingly available for urban multi-temporal semantic understanding.However,few datasets can be used for land-use/land-cover(LULC)classification,binary change detection(BCD)and semantic change detection(SCD)simultaneously because classification datasets always have one time phase and BCD datasets focus only on the changed location,ignoring the changed classes.Public SCD datasets are rare but much needed.To solve the above problems,a tri-temporal SCD dataset made up of Gaofen-2(GF-2)remote sensing imagery(with 11 LULC classes and 60 change directions)was built in this study,namely,the Wuhan Urban Semantic Understanding(WUSU)dataset.Popular deep learning based methods for LULC classification,BCD and SCD are tested to verify the reliability of WUSU.A Siamese-based multi-task joint framework with a multi-task joint loss(MJ loss)named ChangeMJ is proposed to restore the object boundaries and obtains the best results in LULC classification,BCD and SCD,compared to the state-of-the-art(SOTA)methods.Finally,a large spatial-scale mapping for Wuhan central urban area is carried out to verify that the WUsU dataset and the ChangeMJ framework have good application values.
