As human populations become concentrated in larger,more intensely urbanized areas connected through glob-alization,the relationships of cities to their surrounding landscapes are open to social,ecological,and economic...As human populations become concentrated in larger,more intensely urbanized areas connected through glob-alization,the relationships of cities to their surrounding landscapes are open to social,ecological,and economic reinterpretation.In particular,the value of access to nature in the form of nearby undeveloped wildland to ur-ban populations implies a relatively novel type of synergistic city-region relationship.We develop a robust and replicable metric-the urban-wildland juxtaposition(UWJ)-that quantifies critical dimensions of the juxtapo-sition of the urbanicity of cities with the quantity of nearby unbuilt wildlands,based on the spatial proximity and relative intensities of these two contrasting system types.Using a distance-decay gravity model,this analysis provides documentation on the calculation of the UWJ and its component metrics,urbanicity(U)and wildland(W)and then presents U,W,and UWJ metrics for 36 urbanized areas representing all regions of the U.S.,pro-viding the basis for comparisons and analysis.We explore the potential of the metric by testing correlations with“creative class”employment and public health measures.The UWJ has implications and potential applications for demographic,economic,social,and quality-of-life trends across the U.S.and internationally.展开更多
The devastating effects of wildland fire are an unsolved problem,resulting in human losses and the destruction of natural and economic resources.Convolutional neural network(CNN)is shown to perform very well in the ar...The devastating effects of wildland fire are an unsolved problem,resulting in human losses and the destruction of natural and economic resources.Convolutional neural network(CNN)is shown to perform very well in the area of object classification.This network has the ability to perform feature extraction and classification within the same architecture.In this paper,we propose a CNN for identifying fire in videos.A deep domain based method for video fire detection is proposed to extract a powerful feature representation of fire.Testing on real video sequences,the proposed approach achieves better classification performance as some of relevant conventional video based fire detection methods and indicates that using CNN to detect fire in videos is efficient.To balance the efficiency and accuracy,the model is fine-tuned considering the nature of the target problem and fire data.Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in closed-circuit television surveillance systems compared to state-of-the-art methods.展开更多
文摘As human populations become concentrated in larger,more intensely urbanized areas connected through glob-alization,the relationships of cities to their surrounding landscapes are open to social,ecological,and economic reinterpretation.In particular,the value of access to nature in the form of nearby undeveloped wildland to ur-ban populations implies a relatively novel type of synergistic city-region relationship.We develop a robust and replicable metric-the urban-wildland juxtaposition(UWJ)-that quantifies critical dimensions of the juxtapo-sition of the urbanicity of cities with the quantity of nearby unbuilt wildlands,based on the spatial proximity and relative intensities of these two contrasting system types.Using a distance-decay gravity model,this analysis provides documentation on the calculation of the UWJ and its component metrics,urbanicity(U)and wildland(W)and then presents U,W,and UWJ metrics for 36 urbanized areas representing all regions of the U.S.,pro-viding the basis for comparisons and analysis.We explore the potential of the metric by testing correlations with“creative class”employment and public health measures.The UWJ has implications and potential applications for demographic,economic,social,and quality-of-life trends across the U.S.and internationally.
基金National Natural Science Foundation of China(No.61573095)Natural Science Foundation of Shanghai,China(No.6ZR1446700)
文摘The devastating effects of wildland fire are an unsolved problem,resulting in human losses and the destruction of natural and economic resources.Convolutional neural network(CNN)is shown to perform very well in the area of object classification.This network has the ability to perform feature extraction and classification within the same architecture.In this paper,we propose a CNN for identifying fire in videos.A deep domain based method for video fire detection is proposed to extract a powerful feature representation of fire.Testing on real video sequences,the proposed approach achieves better classification performance as some of relevant conventional video based fire detection methods and indicates that using CNN to detect fire in videos is efficient.To balance the efficiency and accuracy,the model is fine-tuned considering the nature of the target problem and fire data.Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in closed-circuit television surveillance systems compared to state-of-the-art methods.
