A spatial scene reconstruction framework in emergency response scenario
Rapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Journal of Safety Science and Resilience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666449624000434 |
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| _version_ | 1850167863277518848 |
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| author | Nan Zheng Danhuai Guo |
| author_facet | Nan Zheng Danhuai Guo |
| author_sort | Nan Zheng |
| collection | DOAJ |
| description | Rapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for emergency management, but existing generative models have limited understanding of spatial relationships and lack high-quality training samples. To address these issues, this paper proposes a novel spatial scene reconstruction framework. Specifically, the BERT based spatial information knowledge graph extraction method is used to encode the input text, label and classify the encoded text, identify spatial objects and relationships in the text, and accurately extract spatial information. Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. In addition, a high-quality training sample set of “text-scene-knowledge graph” was constructed. |
| format | Article |
| id | doaj-art-203ec3792cd9438b9caa6c46236dfb7a |
| institution | OA Journals |
| issn | 2666-4496 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Safety Science and Resilience |
| spelling | doaj-art-203ec3792cd9438b9caa6c46236dfb7a2025-08-20T02:21:07ZengKeAi Communications Co., Ltd.Journal of Safety Science and Resilience2666-44962024-12-015440041210.1016/j.jnlssr.2024.05.004A spatial scene reconstruction framework in emergency response scenarioNan Zheng0Danhuai Guo1Corresponding author.; College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, ChinaRapid and accurate acquisition and analysis of information is crucial for emergency management, but traditional methods have limitations such as incomplete information acquisition and slow processing speed. The natural language oriented spatial scene reconstruction method provides a new solution for emergency management, but existing generative models have limited understanding of spatial relationships and lack high-quality training samples. To address these issues, this paper proposes a novel spatial scene reconstruction framework. Specifically, the BERT based spatial information knowledge graph extraction method is used to encode the input text, label and classify the encoded text, identify spatial objects and relationships in the text, and accurately extract spatial information. Additionally, a large number of manual experiments were conducted to explore quantitative biases in human spatial cognition, and based on the obtained biases, a greedy resolution method based on cost functions was used to fine tune the layout of conflicting spatial objects and solve the conflicting spatial information in the spatial information knowledge graph. Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. In addition, a high-quality training sample set of “text-scene-knowledge graph” was constructed.http://www.sciencedirect.com/science/article/pii/S2666449624000434Spatial sceneSpatial cognitive biasNatural language processingEmergency management |
| spellingShingle | Nan Zheng Danhuai Guo A spatial scene reconstruction framework in emergency response scenario Journal of Safety Science and Resilience Spatial scene Spatial cognitive bias Natural language processing Emergency management |
| title | A spatial scene reconstruction framework in emergency response scenario |
| title_full | A spatial scene reconstruction framework in emergency response scenario |
| title_fullStr | A spatial scene reconstruction framework in emergency response scenario |
| title_full_unstemmed | A spatial scene reconstruction framework in emergency response scenario |
| title_short | A spatial scene reconstruction framework in emergency response scenario |
| title_sort | spatial scene reconstruction framework in emergency response scenario |
| topic | Spatial scene Spatial cognitive bias Natural language processing Emergency management |
| url | http://www.sciencedirect.com/science/article/pii/S2666449624000434 |
| work_keys_str_mv | AT nanzheng aspatialscenereconstructionframeworkinemergencyresponsescenario AT danhuaiguo aspatialscenereconstructionframeworkinemergencyresponsescenario AT nanzheng spatialscenereconstructionframeworkinemergencyresponsescenario AT danhuaiguo spatialscenereconstructionframeworkinemergencyresponsescenario |