Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (1): 46-58.doi: 10.12082/dqxxkx.2019.180311
Previous Articles Next Articles
Liying ZHANG1,2(), Tao PEI3,4,*(
), Yijin CHEN1, Ci SONG3, Xiaoqian LIU5
Received:
2018-07-04
Revised:
2018-10-22
Online:
2019-01-20
Published:
2019-01-20
Contact:
Tao PEI
E-mail:lyzhang1980@cup.edu.cn;peit@lreis.ac.cn
Supported by:
Liying ZHANG, Tao PEI, Yijin CHEN, Ci SONG, Xiaoqian LIU. A Review of Urban Environmental Assessment based on Street View Images[J].Journal of Geo-information Science, 2019, 21(1): 46-58.DOI:10.12082/dqxxkx.2019.180311
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Tab. 1
Street view image APIs"
街景API | 覆盖范围 | 图像最大分辨率(宽度 | 使用样例 |
---|---|---|---|
谷歌 | 114个国家及地区 | 2048 | |
百度 | 中国372座城市 | 1024 | |
腾讯 | 中国296座城市 | 960 | http://apis.map.qq.com/ws/streetview/v1/image?size=600 |
Tab. 2
Comparison of street view image, remote sensing image and geo-tagged social media data"
数据类别 | 采样方式 | 优缺点 | 代表性研究 |
---|---|---|---|
街景图像 | 地面拍摄 | 优点:从微观和人的视角精细化记录城市街道层级的立体剖面景象;覆盖范围广、数据量大、成本低 缺点:数据在空间分布不均匀 | 社区环境[11]、城市安全感[21]、收入预测[22]、建筑特色[23] |
遥感影像 | 空中拍摄 | 优点:从宏观和高空鸟瞰的视角记录城市,覆盖范围广、数据在空间分布均匀 缺点:成本高、分辨率低 | 地表变化分析[12]、农作物识别[13]、空气质量评价[14]、灾情评价[15]、城市热环境变化[16] |
地理标记社交 媒体数据 | 网络用户发布 | 优点:用户交互的内容不仅有文本信息,还包含地理位置、时间、图像、视频、情感等信息;具有动态性、时效性和交互性; 缺点:数据稀疏、数据存在口语化、错误拼写和缩写、使用特殊符号等问题 | 空气质量[17]、台风灾害[18]、旅游景点评价[19]、城市风貌感知[20] |
Tab. 3
Urban environment evaluation based on street view image"
类别 | 评价要素 | 数据集 | 方法 | 代表性研究 |
---|---|---|---|---|
物理环境 | 绿色植物、行人安全、行人道设施、机动交通、建筑、交通标志等 | 谷歌街景 | 相关性分析 泊松回归 机器学习 | 1.街景图像审计社区环境的可行性:Rundle[11],Badland[30],Clarke[45]; 2.城市安全性:Kronkvist[46],Li等[44],Mooney[47] 3.土地利用类型:Li[29] |
社会环境 | 汽车、人行道、行人、建筑、天空等 | 谷歌街景 | 机器学习 深度学习 | 1.人口分布与政治倾向:Gebru等[48];2.城市可步行性:Yin等[34],Hara等6],Yin[4];3.城市安全感:Porzi等[21],Li[27] |
经济环境 | 绿色植被、地面,建筑物、树、天空 | 谷歌街景 | 基于像元的图像分析机器学习 | 1.收入预测:Glaeser[22];2. 收入与物理环境的关系:Li[26],Arietta[49] |
美学环境 | 行道树、绿色植被、建筑物 | 谷歌街景百度街景 腾讯街景 | 机器学习 深度学习 图像分析 | 1.街道绿化:郝新华等[3],Berland[50],Li等[24]; 2. 城市风貌:Liu等[36],Cheng等[51] ,唐婧娴等[52]; 3. 建筑特色:Doersch等[53],Lee等[23] |
[1] | 沈清基. 城市生态与城市环境[M].上海:同济大学出版社,1998. |
[ Shen Q J.Urban ecology and urban environment[M]. Shanghai: Tongji University Press, 1998. ] | |
[2] | 海热提·涂尔逊,杨志峰.试论城市环境与可持续发展[J].环境科学进展,1998,6(6):48-55. |
[ Hai Re Ti·Tu E X, Yang Z F. Sustainable development advances in environmental science, 1998,6(6):48-55. ] | |
[3] | 郝新华,龙瀛. 街道绿化:一个新的可步行性评价指标[J].上海城市规划,2017(1):32-36,49. |
[ Hao X H, Long Y.Street greenery: A new indicator for evaluating walkability[J]. Shanghai Urban Planning Review, 2017(1):32-36,49. ] | |
[4] |
Yin L, Wang Z.Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery[J]. Applied Geography, 2016,76:147-153.
doi: 10.1016/j.apgeog.2016.09.024 |
[5] | Dubey A, Naik N, Parikh D, et al.Deep learning the city: Quantifying urban perception at a global scale[C]. European Conference on Computer Vision, New York: Springer, 2016:196-212. |
[6] | Hara K, Le V, Froehlich J.Combining crowdsourcing and google street view to identify street-level accessibility problems[C]. Proceedings of the SIGCHI conference on human factors in computing systems, ACM, 2013:631-640. |
[7] |
Kelly C M, Wilson J S, Baker E A, et al.Using google street view to audit the built environment: Inter-rater reliability results[J]. Annals of Behavioral Medicine, 2013,45(1):108-112.
doi: 10.1007/s12160-012-9419-9 pmid: 23054943 |
[8] | Google Street View (GSV)[EB/OL]. |
[9] | Baidu Street View (BSV)[EB/OL]. . |
[10] | Tencent Street View (TSV)[EB/OL]. . |
[11] |
Rundle A G, Bader M D M, Richards C A, et al. Using google street view to audit neighborhood environments[J]. American Journal of Preventive Medicine, 2011,40(1):94-100.
doi: 10.1016/j.amepre.2010.09.034 |
[12] |
Hansen M C, Loveland T R.A review of large area monitoring of land cover change using Landsat data[J]. Remote Sensing of Environment, 2012,122(1):66-74.
doi: 10.1016/j.rse.2011.08.024 |
[13] |
宋盼盼,杜鑫,吴良才,等.基于光谱时间序列拟合的中国南方水稻遥感识别方法研究[J].地球信息科学学报,2017,19(1):117-124.
doi: 10.3724/SP.J.1047.2017.00117 |
[ Song P P, Du X, Wu L C, et al.Research on the method of rice remote sensing identification based on spectral time-series fitting in southern China[J]. Journal of Geo-information Science, 2017,19(1):117-124. ]
doi: 10.3724/SP.J.1047.2017.00117 |
|
[14] |
Olaguer E P, Stutz J, Erickson M H, et al.Real time measurement of transient event emissions of air toxics by tomographic remote sensing in tandem with mobile monitoring[J]. Atmospheric Environment, 2017,150:220-228.
doi: 10.1016/j.atmosenv.2016.11.058 |
[15] | 苏亚丽,郭旭东,雷莉萍,等.基于多源卫星遥感的暴雨灾情时空动态信息的提取[J].地球信息科学学报,2018,20(7):1004-1013. |
[ Su Y L, Guo X D, Lei L P, et al.Spatio-temporal dynamics of the impacts of rainstorm disaster on crop growing using multi-satellites remote sensing[J]. Journal of Geo-information Science, 2018,20(7):1004-1013. ] | |
[16] | 侯浩然,丁凤,黎勤生.近20年来福州城市热环境变化遥感分析[J].地球信息科学学报, 2018,20(3):385-395. |
[ Hou H R, Ding F, Li Q S. Remote sensing analysis of changes of urban thermal environment of Fuzhou City in China in the past 20 years[J]. Journal of Geo-information Science, 2018,20(3):385-395. ] | |
[17] |
Wang S, Paul M J, Dredze M.Social media as a sensor of air quality and public response in China[J]. Journal of medical Internet research, 2015,17(3):e22.
doi: 10.2196/jmir.3875 pmid: 4400579 |
[18] | 梁春阳,林广发,张明锋,等.社交媒体数据对反映台风灾害时空分布的有效性研究[J].地球信息科学学报,2018,20(6):807-816. |
[ Liang C Y, Lin G F, Zhang M F, et al.Assessing the effectiveness of social media data in mapping the distribution of Typhoon disasters[J]. Journal of Geo-information Science, 2018,20(6):807-816. ] | |
[19] |
刘逸,保继刚,朱毅玲.基于大数据的旅游目的地情感评价方法探究[J].地理研究,2017,36(6):1091-1105.
doi: 10.11821/dlyj201706008 |
[ Liu Y, Bao J G, Zhu Y L.Exploring emotion methods of tourism destination evaluation: A big-data approach[J]. Geographical Research, 2017,36(6):1091-1105. ]
doi: 10.11821/dlyj201706008 |
|
[20] |
易峥,李继珍,冷炳荣,等.基于微博语义分析的重庆主城区风貌感知评价[J].地理科学进展,2017,36(9):1058-1066.
doi: 10.18306/dlkxjz.2017.09.002 |
[ Yi Z, Li J Z, Leng B R, et al.Perception and evaluation of cityscape characteristics using semantic analysis on microblog in the main urban area of Chongqing municipality[J]. Progress In Geography, 2017,36(9):1058-1066. ]
doi: 10.18306/dlkxjz.2017.09.002 |
|
[21] | Porzi L, Rota Bulò S, Lepri B, et al.Predicting and understanding urban perception with convolutional neural networks[C]. Proceedings of the 23rd ACM international conference on Multimedia, ACM, 2015:139-148. |
[22] |
Glaeser E L, Kominers S D, Luca M, et al.Big data and big cities: The promises and limitations of improved measures of urban life[J]. Economic Inquiry, 2018,56(1):114-137.
doi: 10.1111/ecin.12364 |
[23] | Lee S, Maisonneuve N, Crandall D, et al.Linking past to present: Discovering style in two centuries of architecture[C]. IEEE International Conference on Computational Photography, IEEE, 2015:1-10. |
[24] |
Li X, Zhang C, Li W, et al.Assessing street-level urban greenery using Google Street View and a modified green view index[J]. Urban Forestry & Urban Greening, 2015,14(3):675-685.
doi: 10.1016/j.ufug.2015.06.006 |
[25] |
Li X, Meng Q, Gu X, et al.A hybrid method combining pixel-based and object-oriented methods and its application in Hungary using Chinese HJ-1 satellite images[J]. International Journal of Remote Sensing, 2013,34(13):4655-4668.
doi: 10.1080/01431161.2013.780669 |
[26] |
Li X, Zhang C, Li W, et al.Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA[J]. Urban Forestry & Urban Greening, 2015,14(4):751-759.
doi: 10.1016/j.ufug.2015.07.006 |
[27] |
Li X, Zhang C, Li W.Does the visibility of greenery increase perceived safety in urban areas? Evidence from the place pulse 1.0 dataset[J]. ISPRS International Journal of Geo-information, 2015,4(3):1166-1183.
doi: 10.3390/ijgi4031166 |
[28] | Li X, Ratti C, Seiferling I.Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View[J]. Landscape & Planning, 2018,169:81-91. |
[29] |
Li X, Zhang C, Li W.Building block level urban land-use information retrieval based on Google street view images[J]. Giscience & Remote Sensing, 2017,54(6):819-835.
doi: 10.1080/15481603.2017.1338389 |
[30] |
Badland H M, Opit S, Witten K, et al.Can virtual streetscape audits reliably replace physical streetscape audits?[J]. Journal of Urban Health, 2010,87(6):1007-1016.
doi: 10.1007/s11524-010-9505-x |
[31] | 谢家平. 绿色设计评价与优化[M].北京:中国地质大学出版社,2004. |
[ Xie J P.Green design evaluation and optimization[M]. Beijing: China University of Geosciences Press, 2004. ] | |
[32] |
苗夺谦,张清华,钱宇华,等.从人类智能到机器实现模型——粒计算理论与方法[J].智能系统学报,2016,11(6):743-757.
doi: 10.11992/tis.201612014 |
[ Miao D Q, Zhang Q H,Qian Y H,et al.From human intelligence to machine implementation model: Theories and applications based on granular computing[J].CAAI Transactions on Intelligent Systems,2016,11(6):743-757. ]
doi: 10.11992/tis.201612014 |
|
[33] |
Hazelhoff L, Creusen I M.Exploiting street-level panoramic images for large-scale automated surveying of traffic signs[J]. Machine vision and applications, 2014,25(7):1893-1911.
doi: 10.1007/s00138-014-0628-z |
[34] |
Yin L, Cheng Q, Wang Z, et al."Big data" for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts[J]. Applied Geography, 2015,63:337-345.
doi: 10.1016/j.apgeog.2015.07.010 |
[35] | Goodfellow I, Bengio Y, Courville A, et al.Deep learning[M]. Cambridge: MIT press Cambridge, 2016. |
[36] |
Liu L, Silva E A, Wu C, et al.A machine learning-based method for the large-scale evaluation of the qualities of the urban environment[J]. Computers, Environment and Urban Systems, 2017,65:113-125.
doi: 10.1016/j.compenvurbsys.2017.06.003 |
[37] | Lowe D G.Object recognition from local scale-invariant features[C]. The proceedings of the seventh IEEE international conference on, IEEE, 1999:1150-1157. |
[38] | Krizhevsky A, Sutskever I, Hinton G E.Imagenet classification with deep convolutional neural networks[C]. Advances in neural information processing systems, ACM,2012:1097-1105. |
[39] | Szegedy C, Liu W, Jia Y, et al.Going deeper with convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2015:1-9. |
[40] | Liu X, Chen Q, Zhu L, et al.Place-centric Visual Urban Perception with Deep Multi-instance Regression[C]. Proceedings of the 2017 ACM on multimedia conference, ACM, 2017:19-27. |
[41] | 郭仁忠. 空间分析[M].北京:高等教育出版社,2001. |
[ Guo R Z. Spacial analysis[M]. Beijing: Higher Education Press, 2001. ] | |
[42] |
杨志恒. GIS空间分析研究进展综述[J].安徽农业科学,2012,40(3):1918-1919.
doi: 10.3969/j.issn.0517-6611.2012.03.230 |
[ Yang Z H.Review on research progress of GIS spatial analysis[J]. Journal of Anhui Agricultural Sciences, 2012,40(3):1918-1919. ]
doi: 10.3969/j.issn.0517-6611.2012.03.230 |
|
[43] |
Salesses P, Schechtner K, Hidalgo C A.The collaborative image of the city: Mapping the inequality of urban perception[J]. PloS one, 2013,8(7):e68400.
doi: 10.1371/journal.pone.0068400 pmid: 23894301 |
[44] |
Li H, Páez A, Liu D.Built environment and violent crime: An environmental audit approach using Google Street View[J]. Computers Environment & Urban Systems, 2017,66:83-95.
doi: 10.1016/j.compenvurbsys.2017.08.001 |
[45] |
Clarke P, Ailshire J, Melendez R, et al.Using Google Earth to conduct a neighborhood audit: Reliability of a virtual audit instrument[J]. Health & Place, 2010,16(6):1224-1229.
doi: 10.1016/j.healthplace.2010.08.007 pmid: 20797897 |
[46] | Kronkvist K.Virtual observations of urban neighborhood physical disorder using Google street view[C]. The Stockholm Criminology Symposium 2014: Program and Abstracts, The Swedish National Council for Crime Prevenion, 2014. |
[47] |
Mooney S J, Dimaggio C J, Lovasi G S, et al.Use of google street view to assess environmental contributions to Pedestrian injury[J]. American Journal of Public Health, 2016,106(3):462-469.
doi: 10.2105/AJPH.2015.302978 pmid: 26794155 |
[48] |
Gebru T, Krause J, Wang Y, et al.Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the United States[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017,114(50):13108-13113.
doi: 10.1073/pnas.1700035114 |
[49] |
Arietta S M, Efros A A, Ramamoorthi R, et al.City forensics: Using visual elements to predict non-visual city attributes[J]. IEEE transactions on visualization and computer graphics, 2014,20(12):2624-2633.
doi: 10.1109/TVCG.2014.2346446 pmid: 26356976 |
[50] |
Berland A, Lange D A.Google Street View shows promise for virtual street tree surveys[J]. Urban Forestry & Urban Greening, 2017,21:11-15.
doi: 10.1016/j.ufug.2016.11.006 |
[51] |
Cheng L, Chu S, Zong W, et al.Use of tencent street view imagery for visual perception of streets[J]. International Journal of Geo-information, 2017,6(9):265.
doi: 10.3390/ijgi6090265 |
[52] |
唐婧娴,龙瀛,翟炜,等.街道空间品质的测度变化评价与影响因素识别——基于大规模多时相街景图片的分析[J]. 新建筑,2016(5):110-115.
doi: 10.3969/j.issn.1000-3959.2016.05.021 |
[ Tang J X, Long Y, Zhuo W, et al.Measuring quality of street space, its temporal variation and impact factors: An analysis based on massive street view pictures[J]. New Architecture, 2016,(5):110-115. ]
doi: 10.3969/j.issn.1000-3959.2016.05.021 |
|
[53] |
Doersch C, Singh S, Gupta A, et al.What makes paris look like paris?[J]. ACM Transactions on Graphics, 2012,31(4):1-9.
doi: 10.1145/2185520.2185597 |
[54] |
Cheadle A, Samuels S E, Rauzon S, et al.Approaches to measuring the extent and impact of environmental change in three California community-level obesity prevention initiatives[J]. American Journal of Public Health, 2010,100(11):2129-2136.
doi: 10.2105/AJPH.2010.300002 pmid: 20935262 |
[55] |
Wilson J S, Kelly C M, Schootman M, et al.Assessing the built environment using omnidirectional imagery[J]. American Journal of Preventive Medicine, 2012,42(2):193-199.
doi: 10.1016/j.amepre.2011.09.029 pmid: 22261217 |
[56] |
Naik N, Kominers S D, Raskar R, et al.Computer vision uncovers predictors of physical urban change[J]. Proceedings of the National Academy of Sciences, 2017,114(29):7571-7576.
doi: 10.1073/pnas.1619003114 |
[57] |
Anderson J M, Macdonald J M, Bluthenthal R, et al.Reducing crime by shaping the built environment with zoning: An empirical study of Los Angeles[J]. University of Pennsylvania Law Review, 2013,161(3):699-756.
doi: 10.2139/ssrn.2109511 |
[58] |
Greenberg S W, Rohe W M, Williams J R.Safety in urban neighborhoods: A comparison of physical characteristics and informal territorial control in high and low crime neighborhoods[J]. Population and Environment, 1982,5(3):141-165.
doi: 10.1007/BF01257054 |
[59] |
Dimaggio C, Li G.Roadway characteristics and pediatric pedestrian injury[J]. Epidemiologic reviews, 2011,34(1):46-56// Retting R A, Ferguson S A, Mccartt A T. A review of evidence-based traffic engineering measures designed to reduce pedestrian: Motor vehicle crashes[J]. American Journal of Public Health, 2003,93(9):46-56.
doi: 10.1093/epirev/mxr021 pmid: 22084212 |
[60] |
Forsyth A.What is a walkable place? The walkability debate in urban design[J]. Urban Design International, 2015,20(4):274-292.
doi: 10.1057/udi.2015.22 |
[61] |
Frank L D, Schmid T L, Sallis J F, et al.Linking objectively measured physical activity with objectively measured urban form[J]. American Journal of Preventive Medicine, 2005,28(2):117-125.
doi: 10.1016/j.amepre.2004.11.001 |
[62] |
Ewing R, Handy S.Measuring the unmeasurable: Urban design qualities related to walkability[J]. Journal of Urban design, 2009,14(1):65-84.
doi: 10.1080/13574800802451155 |
[63] |
White J T.Measuring urban design: Metrics for livable places[J]. Journal of Urban Design, 2013,20(2):1-2.
doi: 10.1080/13574809.2015.1008881 |
[64] | 邓小军,王洪刚.绿化率,绿地率,绿视率[J].新建筑,2002,(6):75-76. |
[ Deng X J, Wang H G.Green ratio, green space ratio, green looking ratio[J]. New Architecture, 2002,(6):75-76. ] | |
[65] |
赵庆,唐洪辉,魏丹,等.基于绿视率的城市绿道空间绿量可视性特征[J].浙江农林大学学报,2016,33(2):288-294.
doi: 10.11833/j.issn.2095-0756.2016.02.014 |
[ Zhao Q, Tang H H, Wei D, et al.Spatial visibility of green areas of urban greenway using the green appearance percentage[J]. Journal of Zhejiang A&F University, 2016,33(2):288-294. ]
doi: 10.11833/j.issn.2095-0756.2016.02.014 |
|
[66] |
Mullaney J, Lucke T, Trueman S J.A review of benefits and challenges in growing street trees in paved urban environments[J]. Landscape and Urban Planning, 2015,134:157-166.
doi: 10.1016/j.landurbplan.2014.10.013 |
[67] |
Seiferling I, Naik N, Ratti C, et al.Green streets-Quantifying and mapping urban trees with street-level imagery and computer vision[J]. Landscape and Urban Planning, 2017,165:93-101.
doi: 10.1016/j.landurbplan.2017.05.010 |
[68] |
Ye Q X, Gao W, Wang W Q, et al.A color image segmentation algorithm by using color and spatial information[J]. Journal of Software, 2004,15(4):522-530.
doi: 10.1023/B:APIN.0000033637.51909.04 |
[69] | Zhang L, Gu Z, Li H.SDSP: A novel saliency detection method by combining simple priors[C]. Processing (ICIP), 2013 20th IEEE International Conference, IEEE,2013: 171-175. |
[1] | CHEN Ang, YANG Xiuchun, XU Bin, JIN Yunxiang, ZHANG Wenbo, GUO Jian, XING Xiaoyu, YANG Dong. Research on Recognition Methods of Elm Sparse Forest based on Object-based Image Analysis and Deep Learning [J]. Journal of Geo-information Science, 2020, 22(9): 1897-1909. |
[2] | CUI Cheng, REN Hongyan, ZHAO Lu, ZHUANG Dafang. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery [J]. Journal of Geo-information Science, 2020, 22(6): 1330-1338. |
[3] | MAO Wenshan, ZHAO Hongli, SUN Fengjiao, JIANG Yunzhong, JIANG Qian, ZHU Yanru. Personalized Recommendation Method of Thematic Map Products based on Item2Vec with Negative Sampling Optimization [J]. Journal of Geo-information Science, 2020, 22(11): 2128-2139. |
[4] | GUO Zihui, LIU Wei. Land Type Interpretation Authenticity Check of Vector Patch Supported by Deep Learning and Remote Sensing Image [J]. Journal of Geo-information Science, 2020, 22(10): 2051-2061. |
[5] | SU Fenzhen, WU Wenzhou, ZHANG Yu, KANG Lu, LI Xiaoen. From Geographic Information System to Intelligent Geographic System [J]. Journal of Geo-information Science, 2020, 22(1): 2-10. |
[6] | SONG Guanfu, LU Hao, WANG Chenliang, HU Chenpu, HUANG Kejia. A Tentative Study on System of Software Technology for Artificial Intelligence GIS [J]. Journal of Geo-information Science, 2020, 22(1): 76-87. |
[7] | CAI Bowen,WANG Shugen,WANG Lei,SHAO Zhenfeng. Extraction of Urban Impervious Surface from High-Resolution Remote Sensing Imagery based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(9): 1420-1429. |
[8] | Huihui CAI, Wei ZHU, Zixuan LI, Yuanyuan LIU, Longbin LI, Chang LIU. Prediction Method of Tungsten-molybdenum Prospecting Target Area based on Deep Learning [J]. Journal of Geo-information Science, 2019, 21(6): 928-936. |
[9] | Ying FANG, Lianfa LI. Spatiotemporal Estimation of High-Accuracy and High-Resolution Meteorological Parameters based on Machine Learning [J]. Journal of Geo-information Science, 2019, 21(6): 799-813. |
[10] | Gang ZHANG, Wenbin LIU, Nan ZHANG. Progressive Morphological Filtering Method of Dense Matching Point Cloud based on Region Feature Segmentation [J]. Journal of Geo-information Science, 2019, 21(4): 615-622. |
[11] | Yao YAO, Shuliang REN, Junyi WANG, Qingfeng GUAN. Mapping the Fine-Scale Housing Price Distribution by Integrating a Convolutional Neural Network and Random Forest [J]. Journal of Geo-information Science, 2019, 21(2): 168-177. |
[12] | Yilan LIU, Xiaoxia HUANG, Hongga LI, Ze LIU, Chong CHENG, Xin'ge WANG. Extraction of Irregular Solid Waste in Rural based on Convolutional Neural Network and Conditional Random Field Method [J]. Journal of Geo-information Science, 2019, 21(2): 259-268. |
[13] | LIU Hao, LUO Jiancheng, HUANG Bo, YANG Haiping, HU Xiaodong, XU Nan, XIA Liegang. Building Extraction based on SE-Unet [J]. Journal of Geo-information Science, 2019, 21(11): 1779-1789. |
[14] | LIU Wentao,LI Shihua,QIN Yuchu. Automatic Building Roof Extraction with Fully Convolutional Neural Network [J]. Journal of Geo-information Science, 2018, 20(11): 1562-1570. |
[15] | WANG Jinchuan,TAN Xicheng,WANG Zhaohai,ZHONG Yanfei,DONG Huaping,ZHOU Songtao,CHENG Buyi. Faster R-CNN Deep Learning Network Based Object Recognition of Remote Sensing Image [J]. Journal of Geo-information Science, 2018, 20(10): 1500-1508. |
|