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Sökning: WFRF:(Zhang Zhuo) > (2020-2024) > Vectorized dataset ...

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FältnamnIndikatorerMetadata
00008069naa a2200517 4500
001oai:DiVA.org:kth-318188
003SwePub
008220916s2022 | |||||||||||000 ||eng|
009oai:DiVA.org:mdh-59932
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3181882 URI
0247 a https://doi.org/10.5194/essd-14-4057-20222 DOI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-599322 URI
040 a (SwePub)kthd (SwePub)mdh
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Qian, Zhenu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
2451 0a Vectorized dataset of roadside noise barriers in China using street view imagery
264 c 2022-09-06
264 1b Copernicus GmbH,c 2022
338 a print2 rdacarrier
500 a QC 20220916
520 a Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).
650 7a NATURVETENSKAPx Kemix Fysikalisk kemi0 (SwePub)104022 hsv//swe
650 7a NATURAL SCIENCESx Chemical Sciencesx Physical Chemistry0 (SwePub)104022 hsv//eng
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Systemvetenskap, informationssystem och informatik0 (SwePub)102022 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Information Systems0 (SwePub)102022 hsv//eng
7001 a Chen, Minu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.;Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China.4 aut
7001 a Yang, Yueu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Zhong, Tengu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Zhang, Fanu MIT, Senseable City Lab, Cambridge, MA 02139 USA.4 aut
7001 a Zhu, Ruiu Hong Kong Polytech Univ, Dept Land Surveying & Geo Informat, Kowloon, Hong Kong, Peoples R China.4 aut
7001 a Zhang, Kaiu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Zhang, Zhixinu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;Nanjing Univ, Coll Geog & Marine, POB 2100913, Nanjing, Peoples R China.4 aut
7001 a Sun, Zhuou Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Ma, Peilongu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Lu, Guonianu Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.4 aut
7001 a Ye, Yuu Tongji Univ, Dept Architecture, Coll Architecture & Urban Planning, Shanghai, Peoples R China.4 aut
7001 a Yan, Jinyue,d 1959-u Mälardalens universitet,KTH,Energiprocesser,Mälardalen Univ, Future Energy Ctr, S-72123 Västerås, Sweden.,Framtidens energi,KTH Royal Inst Technol, Dept Chem Engn, S-10044 Stockholm, Sweden.4 aut0 (Swepub:mdh)jyn01
7102 a Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.b Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ PRC, Nanjing 210023, Peoples R China.;State Key Lab Cultivat Base Geog Environm Evolut, Nanjing 210023, Peoples R China.;Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.;Nanjing Normal Univ, Sch Math Sci, Jiangsu Prov Key Lab NSLSCS, Nanjing 210023, Peoples R China.4 org
7730 t Earth System Science Datad : Copernicus GmbHg 14:9, s. 4057-4076q 14:9<4057-4076x 1866-3508x 1866-3516
8564 u https://doi.org/10.5194/essd-14-4057-2022y Fulltext
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-318188
8564 8u https://doi.org/10.5194/essd-14-4057-2022
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-59932

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