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Publications:

[27] Xiaojiang Li, Bill Yang Cai, Waishan Qiu, Jinhua Zhao, Carlo Ratti, (2019), A novel method for predicting and mapping the occurrence of sun glare using Google Street View, Transportation Research, C Emerging Technologies.

[26] Xiaojiang Li, Fabio Duate, Carlo Ratti, (2019), Analyzing the obstruction effects of the obstacles on light pollution cased by lighting system in Cambridge, Massachusetts, Environment and Planning B, Urban Analytics and City Science (In press).

[25] Xiaojiang Li, Carlo Ratti, (2019), Using Google Street View for street-level urban form analysis, The Mathematics of Urban Morphology , (In press).

[24] Xiaojiang Li, Debarchana Ghosh, (2018). Associations between body mass index and urban "green" streetscape in Cleveland, Ohio. International Journal of Environmental Research and Public Health. (Link).

[23] Villeneuve, P, Ysseldyk, R, Root, R, Ambrose, S, DiMuzio, J, Kumar, N, Shehata, M, Xi, M, Seed, E, Shooshtari, M, Xiaojiang Li , Daniel Rainham, Are greener and more walkable neighbourhoods associated with recreational physical activity and self-rated health in Ottawa, Canada? International Journal of Environmental Research and Public Health. (Accepted).

[22] Xiaojiang Li, Carlo Ratti. Mapping the spatial-temporal distribution solar radiation in street canyons of Boston using Google Street View panoramas. Landscape and Urban Planning. (Link).

[21] Xiaojiang Li, Paolo Santi, ..., Carlo Ratti. Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Transactions in GIS. (Link).

[20] W Zhang, C Witharana, W Li, C Zhang, Xiaojiang Li, J Parent. Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images. Sensors. (Accepted).

[19] Bill Yang Cai, Xiaojiang Li, Ian Seiferling, Carlo Ratti. Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover, IEEE BigData Congress. (PDF).

[18] Xiaojiang Li, Bill Yang Cai, Carlo Ratti. Using Street-level Images and Deep Learning for Urban Landscape Analysis, Landscape Architecture Frontier. (PDF).

[17] Fangying Gong, Zhaocheng Zeng, Fan Zhang, Xiaojiang Li. Mapping sky, tree, and building view factors of street canyons in a high-density urban environment, Building and Environment, 134, 155-167. (PDF)

[16] Xiaojiang Li, et al. Mapping the spatial distribution of shade provision of street trees in Boston using Google Street View panoramas, Urban Forestry and Urban Greening, 31, 109-119. (Link)

[15] Xiaojiang Li, Carlo Ratti, Ian Seiferling. Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View, Landscape and Urban Planning, 169, 81-91. (One of the most downloaded papers).

[14] Xiaojiang Li, et al. Building block level urban land use information retrieval based on Google Street View images, GIScience and Remote Sensing, 2017, 1-17. (PDF)

[13] Xiaojiang Li, Carlo Ratti, Ian Seiferling. Mapping Urban Landscapes Along Streets Using Gooogle Street View, Advances in Cartography and GIScience, 2017 (PDF)

[12] Zhang W., W. Li, C. Zhang. D. Hanink, Xiaojiang Li, and W. Wang. 2017. Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Computers, Environment and Urban Systems, 64, (215-268).

[11] Zhang W., W. Li, C. Zhang, and Xiaojiang Li. Incorporating Spectral Similarity into Markov Chain Geostatistical Cosimulation for Reducing Smoothing Effect in Land Cover Post-Classification". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, doi: doi: 10.1109/JSTARS.2016.2596040.

[10] Xiaojiang Li, Zhang, C., Li, W., Yulia A. Kuzovkina. Environmental inequities in terms of different types of urban greenery in Hartford, Connecticut, Urban Forestry and Urban Greening, 2016, 18, 163-172. (PDF).

[9] Xiaojiang Li, Weidong Li, Qingyan Meng, Chuanrong Zhang, Tamas Jancso, Kangli Wu. Modeling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data, Journal of Spatial Science, 2016,1-15. (PDF).

[8] Xiaojiang LI, Zhang, C., Li, W., Yulia A. Kuzovkina, Daniel Weiner. Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA, Urban Forestry and Urban Greening, 2015,14,751-759. (PDF).

[7] Xiaojiang Li, Zhang, C., Li, W. Does the visibility of greenery increase perceived safety in urban areas? Evidence from the Place Pulse 1.0 dataset. ISPRS International Journal of Geo-Information, 2015, 4(3), 1166-1183. (PDF)

[6] Xiaojiang Li, Zhang, C., Li, W., Ricard, R., Meng, Q., Zhang, W. Assessing Street-Level Urban Greenery Using Google Street View and a Modified Green View Index, Urban Forestry and Urban Greening, 2015,14(3), 675-685. (One of the most downloaded and cited papers).

[5] Xiaojiang Li, Meng, Q., Li, W., Zhang, C., Jancso, T., & Mavromatis, S. (2014). An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery. Annals of GIS, 20(3), 193-203.

[4] Xiaojiang Li., Meng, Q., Gu, X., Jancso, T., Yu, T., Wang, K., & Mavromatis, S. (2013). A hybrid method combining pixel-based and object-oriented methods and its application in Hungary using Chinese HJ-1 satellite images. International journal of Remote Sensing, 34(13), 4655-4668.

[3] Xiaojiang Li, Qingyan Meng, Chunmei Wang, Miao Liu. A Hybrid Model of Object- and Pixel Based Classification of Remotely Sensed Data, Journal of Geographic Infomation Science. 2013, 15(5).

[2] WANG Ke, GU Xing-fa, YU Tao, LIN Jin-tang, WU Gui-ping and LI Xiao-jiang. Segmentation of high-resolution remotely sensed imagery combining spectral similarity with phase congruency. J.Infrared Millim.Waves,2013,32(1):73~79.

[1] Chunzhu Wei, Qingyan Meng, Wenfeng Zheng, Xiaojiang Li, Xi Wei, Liang Wang, The study of Quantitative relationship between Land surface temperature and land cover of Guangzhou, Remote Sensing Technology and Application, 2013, 28(6): 955-963 (In Chinese)