Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point clou...Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.展开更多
Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neur...Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.展开更多
Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a...Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.展开更多
文摘Panoramic images are widely used in many scenes,especially in virtual reality and street view capture.However,they are new for street furniture identification which is usually based on mobile laser scanning point cloud data or conventional 2D images.This study proposes to perform semantic segmentation on panoramic images and transformed images to separate light poles and traffic signs from background implemented by pre-trained Fully Convolutional Networks(FCN).FCN is the most important model for deep learning applied on semantic segmentation for its end to end training process and pixel-wise prediction.In this study,we use FCN-8s model that pre-trained on cityscape dataset and finetune it by our own data.Then replace cross entropy loss function with focal loss function in the FCN model and train it again to produce the predictions.The results show that in all results from pre-trained model,fine-tuning,and FCN model with focal loss,the light poles and traffic signs are detected well and the transformed images have better performance than panoramic images in the prediction according to the Recall and IoU evaluation.
基金supported by Natural Science Foundation of Chongqing in China(No.cstc2020jcyj-jqX0004)the Ministry of education of Humanities and Social Science project(No.20YJA790016)+1 种基金the National Natural Science Foundation of China(Grant No.42171298)We thank the patent supporting the method section of the paper(No.202110750360.1).
文摘Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.
基金supported by National Natural Science Foundation of China[No.41971349,No.41930107,No.42090010 and No.41501434]National Key Research and Development Program of China[No.2017YFB0503704 and No.2018YFC0809806].
文摘Without explicit description of map application themes,it is difficult for users to discover desired map resources from massive online Web Map Services(WMS).However,metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples,mixed vocabularies,variable length and content arbitrariness of text fields.In this paper,we propose a novel multi-label text classification method,Text GCN-SW-KNN,based on geographic semantics and collaborative training to improve classifica-tion accuracy.The semi-supervised collaborative training adopts two base models,i.e.a modified Text Graph Convolutional Network(Text GCN)by utilizing Semantic Web,named Text GCN-SW,and widely-used Multi-Label K-Nearest Neighbor(ML-KNN).Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text.The distances are calculated with the Semantic Web of Earth and Environmental Terminology(SWEET)and WordNet dictionaries.Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines,and demonstrate better stability in repeating experiments and robustness to less training data.Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
