Peer-review Journal Publications

[37] Xiaojiang Li, Guoqing Wang, Examining runner's outdoor heat exposure using urban microclimate modeling and GPS trajectory mining, Computers, Environment and Urban Systems, (Accepted).

[36] Xiaojiang Li, Guoqing Wang, GPU parallel computing for mapping urban outdoor heat exposure, Theoretical and Applied Climatology, (Accepted).

[35] Xiaojiang Li, Investigating the spatial distribution of resident’s outdoor heat exposure across neighborhoods of Philadelphia, Pennsylvania using urban microclimate modeling, Sustainable Cities and Society, (Accepted).

[34] A, Sevtsuk, Xiaojiang Li, R, Basu, A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco, Travel Behaviour and Society, (Accepted).

[33] J. Mennis, Xiaojiang Li, M. Meenar, J. D.Coatsworth, T. P. McKeon, Residential Greenspace and Urban Adolescent Substance Use: Exploring Interactive effects with Peer Network Health, Sex, and Executive Function, International Journal of Environmental Research and Public Health, (Accepted).

[32] Ruoyu Wang, Zhiqiang Feng, Jamie Pearcea, Yao Yao, Xiaojiang Li, Ye Liu. The distribution of greenspace quantity and quality and their association with neighbourhood socioeconomic conditions in Guangzhou, China: A new approach using deep learning method and street view images, Sustainable Cities and Society, (Accepted).

[31] Jian Lin, Qiang Wang, Xiaojiang Li, (2020), Socioeconomic and spatial inequalities of street tree abundance, species diversity, and size structure in New York City, Landscape and Urban Planning, (Accepted).

[30] Xiaojiang Li. (2020). Examining the spatial distribution and temporal change of green view index in New York City using Google Street View images and deep learning, Environment and Planning B: Urban Analytics and City Science, (Accepted).

[29] Xiaojiang Li, Abby Rudolph, Jeremy Mennis. (2020). The Association between Population Mobility Reductions and New COVID-19 Diagnoses in the US along the Urban-Rural Gradient, February-April, 2020, Preventing Chronic Disease, (Accepted).

[28] Y. Kumakoshi *, Sau Yee Chan, H. Koizumi, Xiaojiang Li, Y. Yoshimura, (2020). Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation, Sustainability, (Accepted).

[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. (Accepted).

[23] Xiaojiang Li, Carlo Ratti, (2018). Mapping the spatial-temporal distribution of solar radiation in street canyons of Boston using Google Street View panoramas, Landscape and Urban Planning (Accepted).

[22] Villeneuve, P, Ysseldyk, R, Root, R, Ambrose, S, DiMuzio, J, Kumar, N, Shehata, M, Xi, M, Seed, E, Shooshtari, M, Xiaojiang Li , Daniel Rainham, (2018). 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).

[21] Xiaojiang Li, Paolo Santi, ..., Carlo Ratti. (2018). Investigating the association between streetscapes and human walking activities using Google Street View and human trajectory data. Transactions in GIS. (Accepted).

[20] W Zhang, C Witharana, W Li, C Zhang, Xiaojiang Li, J Parent. (2018). Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images. Sensors. (Accepted).

[19] Xiaojiang Li, Bill Yang Cai, Carlo Ratti. (2018). Using Street-level Images and Deep Learning for Urban Landscape Analysis, Landscape Architecture Frontier, (Accepted).

[18] Fangying Gong, Zhaocheng Zeng, Fan Zhang, Xiaojiang Li, Edward Ng, Les Norford. (2018). Mapping sky, tree, and building view factors of street canyons in a high-density urban environment, Building and Environment. 134, 155-167.

[17] Xiaojiang Li, et al. (2018), 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.

[16] Xiaojiang Li, Carlo Ratti, Ian Seiferling. (2018). 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, (link). (One of most downloaded papers).

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

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

[13] 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).

[12] Zhang, W., Li, W., Zhang, C., Hanink, D. M., Xiaojiang Li., & Wang, W. (2017). Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York City for urban land use classification in Brooklyn, New York City. Data in brief, 12, 175-179.

[11] Zhang W., W. Li, C. Zhang, and Xiaojiang Li. (2017). 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, Chuanrong Zhang, Environmental inequities in terms of different types of urban greenery in Hartford, Connecticut, USA. Urban Forestry and Urban Greening, 18(1). (Link) (PDF)

[9] Xiaojiang Li, Chuanrong Zhang, Weidong Li, (2016). Modelling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data, Journal of Spatial Science, (2016): 1-15.

[8] Xiaojiang Li, Zhang, C, Li, W, Kuzovkina, Y, Weiner, D. (2015). Who lives in greener neighborhoods? The distribution of street greenery and its association with residents’ social conditions in Hartford, Connecticut, USA, Urban Forestry and Urban Greening, 2015, 14(4). (Link)

[7] Xiaojiang Li, Zhang, C., & Li, W. (2015). 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, 4(3), 1166-1183.

[6] Xiaojiang Li, Zhang, C., Li, W., Ricard, R., Meng, Q., Zhang, W. (2015). Assessing street-level urban greenery using Google Street View and a modified green view index, Urban Forestry and Urban Greening, 2015, 14(3) (PDF). (One of most cited and downloaded papers).

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

[4] Xiaojiang Li, Qingyan Meng, Xingfa Gu, Tamas Jasco. (2013). A hybrid method combining pixel based and object based methods and its applications in Hungary using Chinese HJ-1 Satellite image, International Journal of Remote Sensing, 2013, 34(13), 4655-4667.

[3] Xiaojiang Li, Qingyan Meng, Chunmei Wang, Miao Liu. (2013). A hybrid model of object- and pixel based classification of Remotely sensed data, Geo-spatial Information Science, 2013, 15(5) (In Chinese).

[2] Ke Wang, Xingfa Gu, Tao Yu, Jintang Lin, Guiping Wu and Xiaojiang Li, Segmentation of high resolution remotely sensed imagery combining spectral similarity with phase congruency, J. of Infrared Millim. Waves, 2013, 32(1):73-79 (In Chinese).

[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).

Peer-review Conference Proceedings

[5] Xiaojiang Li and Carlo Ratti, Using Deep Learning and Google Street View Images to Quantify the Shade Provision of Street Trees in Boston, Massachusetts. Proceding of AutoCarto-UCGIS 2018. (Link).

[4] Daniele Santucci, Umberto Fugiglando, Xiaojiang Li, Thomas Auer, Carlo Ratti. Methodological framework for evaluating liveability of urban spaces through human centred approach, 10th Windsor Conference 2018 – Rethinking Comfort – Proceedings.

[3] 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 (Accepted).

[2] Xiaojiang Li, Chuanrong Zhang, Urban land use information retrieval based on scene classification of Google Street View images, GIScience 2016, Montreal, Canada.

[1] Xiaojiang Li, Qingyan Meng. 2013, Using multi-source remotely sensed data to analyze green space at 3D perspective, Symposium of Remote Sensing Cross Taiwan Strait, National Central University, Taiwan.

Presentations

Li, X, 2019, Urban Environmental Informatic, Rutgers University, Nov, 2019. (Invited)

Li, X, 2019, A simpler approach for predicting pedestrian flows in cities based on empirical analysis of GPS walking traces in SF, ACSP, Greenville, SC, Oct, 2019

Li, X. 2018, Urban Sensing using Google Street View, Big data and Urban Planning, Harvard University, Graduate School of Design, Dec, 5th, 2018. (Invited)

Li, X. 2018. Mapping Urban Streetscape using Street-level images and AI, Second Spatial Data Science Conference, CARTO, New York City, October 2018.

L. Nesbitt, M. Andreani, I. Jarvis, Li. X, C. Ratti, I. Seiferling, P. Villeneuve, M. van den Bosch, Urban Transitions, Barcelona, Spain, 2018.

Li, X. and Carlo Ratti, 2018, Using deep learning and Google Street View images to quantify the shade provision of street trees in Boston, Massachusetts, UCGIS 2018 Symposium and CaGIS AutoCarto, Madison, WI, USA.

Li, X. 2018, (Panel discussion) Senseable Cities, 2018, CGA Conference: Illuminating Space and Time in Data Science, Harvard University, Cambridge, MA, USA.

Li, X. and Debarchana Ghosh, 2018, Associations between body mass index and urban green streetscape. Association of American Geographer Metting, New Orleans, LA, USA.

Li, X. Urban Sensing using Google Street View, CANUE, The Canadian Urban Environmental Health Research Consortium (Invited).

Li, X. 2017, Quantifying the shade provision of street greenery by combining Google Street View and remote sensing. The 25th International Conference on Geoinformatics, Buffalo, New York, USA.

Li, X. 2017, Mapping Urban Landscape along Street using Google Street View, The 28th International Conference of Cartography, Washington, D.C, USA.

Li, X. 2017, Quantifying the shade provision of street greenery using Google Street View panorama, Association of American Geographers Meeting, Boston, MA, USA.

Li, X. and Zhang, C. 2016. “Urban land use information retrieval based on scene classification of Google Street View images”, Paper presented at the 9th International Conference on Geographic Information Science, (GIScience 2016), Montreal, Canada.

Li, X. and Zhang, C. 2016. “Environmental inequities in terms of different types of urban greenery in Hartford, CT”, Paper presented at the Association of American Geographers meeting, San Francisco, CA.

Li, X. and Zhang, C. 2015. “Using Google Street View to map the distribution of street greenery”, Paper presented at the meeting, Annual Meeting of New England-St. Lawrence Valley Geographical Society, Bridgewater State University, MA.

Li, X. and Zhang, C. 2015. “Using Google Street view to map urban greenery in Hartford, CT” Paper presented at the Association of American Geographers meeting, Chicago, IL.

Li, X. and Meng, Q. 2013. “Using Multi-source remote sensing image to assess urban green space at Hungary”, 35th International Symposium on Remote Sensing of Environment, Beijing, China.

Li. X. 2013, “Using multi-source remotely sensed data to analyze green space at 3D perspective”, Paper presented at the Symposium, Symposium of Remote Sensing Cross Taiwan Strait, National Central University, Taiwan.