The classification of urban functional areas plays an important role in urban planning and resource management.Although previous studies have confirmed that different urban func-tional areas have different morphologic...The classification of urban functional areas plays an important role in urban planning and resource management.Although previous studies have confirmed that different urban func-tional areas have different morphological structures and Land Surface Temperature(LST)characteristics,these two types of characteristics have rarely been fully integrated and used for functional area classification.In this paper,a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features.First,metrics are constructed from three levels,namely,building,road and region,which are used to portray urban morphology;LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics:the average temperature,maximum temperature,temperature difference and standard deviation of temperature.Then,the functional areas are classified into four categories:service/public land,commercial land,residential land and industrial land.A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas.The effectiveness of the proposed framework is tested in the study area of Shenzhen City,Guangdong Province.The results show that the combined classification accuracy of the proposed classification method is 0.85,which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features.The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.展开更多
Web maps represent an effective source for land cover mapping in capturing human activities.However,due to spatial heterogeneity,previous research has mainly focused on generating land cover maps in partial areas.Infe...Web maps represent an effective source for land cover mapping in capturing human activities.However,due to spatial heterogeneity,previous research has mainly focused on generating land cover maps in partial areas.Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale.This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping.First,linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification.Then,spatial factors are constructed from point and polygon features to identify the spatial variety of Web maps,with an artificial neural network classifier being adopted to classify land cover automatically.Land cover mapping is finally proposed by combining classified parcels and existing polygon features.The proposed method is applied in Guangzhou,Guangdong Province,using a Web map from AutoNavi.The results show an approximately 88%classification accuracy and an overall mapping accuracy of 85.06%.The results indicate that the proposed approach has the potential to be utilised in land cover mapping,and the constructed spatial factors are effective at characterising land cover information.展开更多
基金supported by the National Natural Science Foundation of China[grant Nos 41971406,41871292]the Science and Technology Program of Guangdong Province[grant number 2018B020207002]the Science and Technology Program of Guangzhou,China[grant number 201803030034].
文摘The classification of urban functional areas plays an important role in urban planning and resource management.Although previous studies have confirmed that different urban func-tional areas have different morphological structures and Land Surface Temperature(LST)characteristics,these two types of characteristics have rarely been fully integrated and used for functional area classification.In this paper,a new framework for classifying urban functional areas is proposed by combining urban morphological features and LST features.First,metrics are constructed from three levels,namely,building,road and region,which are used to portray urban morphology;LST is retrieved using thermal infrared remote sensing to reflect LST features with four metrics:the average temperature,maximum temperature,temperature difference and standard deviation of temperature.Then,the functional areas are classified into four categories:service/public land,commercial land,residential land and industrial land.A random forest algorithm is used to effectively fuse the features of these two categories and classify the functional areas.The effectiveness of the proposed framework is tested in the study area of Shenzhen City,Guangdong Province.The results show that the combined classification accuracy of the proposed classification method is 0.85,which is 0.26 higher than that of the classification model based on urban morphology and 0.1 higher than that of the classification model based on LST features.The proposed framework verifies that the integration of LST features into urban functional area classification is reliable and effectively combines urban morphology and LST features for functional area classification.
基金This research was supported by the National Natural Science Foundation of China(Grant Nos.41501420,41301377).
文摘Web maps represent an effective source for land cover mapping in capturing human activities.However,due to spatial heterogeneity,previous research has mainly focused on generating land cover maps in partial areas.Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale.This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping.First,linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification.Then,spatial factors are constructed from point and polygon features to identify the spatial variety of Web maps,with an artificial neural network classifier being adopted to classify land cover automatically.Land cover mapping is finally proposed by combining classified parcels and existing polygon features.The proposed method is applied in Guangzhou,Guangdong Province,using a Web map from AutoNavi.The results show an approximately 88%classification accuracy and an overall mapping accuracy of 85.06%.The results indicate that the proposed approach has the potential to be utilised in land cover mapping,and the constructed spatial factors are effective at characterising land cover information.
