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Dr. Songnian Li

Professor and Assicate Chair for Graduate Studies
DepartmentCivil Engineering
Phone416-979-5000 ext. 6450

Dr. Songnian Li has been a faculty member (Full Professor) of Geomatics Engineering in the Department of Civil Engineering at Ryerson University since 2001.  He holds a B. Eng. degree in Surveying Engineering from the Wuhan Technical University of Surveying and Mapping (now Wuhan University), and a Ph.D. degree from the Department of Geodesy and Geomatics Engineering (external link, opens in new window)  of the University of New Brunswick in New Brunswick, Canada.  Prior to joining Ryerson University, Dr. Li had worked in a number of universities in China and spent one year at Lakehead University as a visiting scholar. He has been appointed as an Adjunct Professor or member of a number of universities in Canada, China and Japan, and was invited twice as a research fellow of Japan Society for the Promotion of Science (JSPS) in 2009 and 2012.

Dr. Li has been working on research projects related to geocollaboration, geospatial web, moving object data mining and knowledge discovery, spatio-temporal dynamics, public participation GIS, and solar energy potential mapping.

Professional Activities, Services, and Memberships:

Professional Services: Council Member (Treasurer), International Society for Photogrammetry and Remote Sensing (ISPRS) (2016-2020); President, ISPRS Technical Commission II (2012-2016); Canadian National Delegate, Commission 3 of the International Federation of Surveyors (FIG) (2007-2010, 2011-2014); Chair of ISPRS WG IV/5 of Technical Commission IV (2004 – 2008, 2008-2012); Council Member of Canadian Institute of Geomatics (CIG) and Chair of the Canadian National Committee for ISPRS (2009-2016); and Special Examiner of the Canadian Board of Examiners for Professional Surveyors (CBEPS) (since 2004).

Scholarly Services: Associate Editor of GIS and Cartography, the Editorial Board of Geomatica (since 2002); Member, Editorial Advisory Board of ISPRS Journal of Photogrammetry and Remote Sensing; Guest Editor of special/theme issues in ISPRS Journal of Photogrammetry and Remote Sensing, International Journal of Digital Earth, Transactions in GIS, and Geomatica; Scientific Reviewers of national and international journals, book series chapters, granting bodies (e.g., NSERC, Canada; RGC in Hong Kong), and workshop (e.g., URISA workshops).

Dr. Li is also an individual member of the following professional societies/associations:

  • Licensed member of Professional Engineers Ontario (PEO), Canada (since 2003)
  • Licensed member of Association Ontario of Land Surveyors (AOLS), Canada (since 2002)
  • Member of Canadian Institute of Geomatics, Canadian Remote Sensing Society, etc.


Undergraduate Courses

CVL323 Fundamentals of Surveying
CVL352 Geomatics Measurement Techniques (past)
CVL736 Geospatial Information Systems
CVL753 Spatial Information Management Systems I (past)
CVL853 Spatial Information Management Systems II (past)

Graduate Courses

CV8100 Directed Studies
CV8501 Advanced Geospatial Information Systems
CV8505 GIS for Civil Engineering
CV8508 Special Topics: Geomatics
ES8950 Independent Study in Environment Science and Management


Geospatial Collaboration: Theory, Systems and Applications

Urban, utility, and transportation developments, as well as emergency and disaster management, depend largely on innovative information technologies required for supporting their design/planning/decision-making processes in order to achieve beneficial economic, social and environmental outcomes. In contrast to traditional planning/decision-making processes which involve a relatively small group of experts, a democratic process has emerged requiring input from a large group of diverse stakeholders including the public. Geospatial collaboration, incorporating the principles of computer supported cooperative work (CSCW), provides methods and tools to facilitate this process to maximize the above benefits, minimize potential impact on the environment or make informed decisions by supporting geographically dispersed people in collaborating on a common decision task by sharing the same geographic representations and exploring alternatives in various “time-place” scenarios.

Event-driven Geospatial Infrastructure

Recent developments in physical and virtual sensor, sensor network, other data collection and information technologies have resulted in a huge amount of a continuous and ever-expanding stream of real-time information (or events), which is far beyond human’s capacity to process. In recent years, event-driven architecture (EDA) and the resurgence of complex event processing have formed a new technology paradigm for event-based applications. Event-driven architecture, along with service-oriented architecture and open source, represents another web trend for geospatial applications, and has been rated as one of the technologies having particularly high impact in the next 10 years. Shifted from the current “request/response” based system to a “sense/respond” or “publish/subscribe” environment, the event-driven design allows the system to “sense” things that are happening and actively “respond” to them. Systems and applications developed based on event-driven architecture are able to detect incoming events, process (identify and correlate) these events to find patterns based on the defined business/application rules, and ultimately respond to the events and/or take intelligent actions appropriately and expeditiously.

Geospatial Data Extractions and Knowledge Discovery

Global positioning system (GPS) data crowdsourced through GPS-enabled mobile devices, such as smart phones and in-car navigation systems, is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions, as well as extracting road network data. Algorithms and techniques are needed for extracting traffic information (e.g., traffic volume, patterns, etc.) and road network data (e.g., road centerlines, # of lanes, etc.). The current study focuses on the development of methods and software tools for extracting road networks from GPS data collected by smart phones. The results will provide an cost-effective way of updating existing road networks in a timely manner. This can supplement, if not replace, the current practices of acquiring road network data using either traditional survey or remote sensing approaches, which are expensive and time consuming.

Spatiotemporal Database Modeling and Sea Ice Data Services

Sea ice data has significant scientific value for climate, environmental impact and engineering studies leading to the construction of facilities in Arctic waters, as well as to support tourism and fishing planning. Large collections of such data are acquired, compiled, produced and maintained by national and international agencies. Aiming at developing a Canadian sea ice information infrastructure that manages historical, ongoing and in‐situ sea ice data for research and decision‐making, the cuurent focus is on spatiotemporal sea ice database and open web APIs for accessing sea ice information and web-based, user‐defined functions to support query and manipulation of sea ice data for basic data manipulation functions, and standard analytical and statistical tools.

Urban Solar Energy Modeling and Mapping

Solar energy potential in complex urban environments depends on available irradiance, geographic location, local environment, technology efficiency and social and economic factors. Factors such as terrain, size and orientation of building rooftops and facades, shading from buildings, trees and other structures, and snow covers also affect the actual solar irradiance received. 3D modeling of buildings and their rooftop geometries as well tree coverage allows for more accurate estimation of solar energy potential on certain facades and rooftops to predict for example hourly generation of electricity by solar panels. The research aims to develop innovative solar energy mapping and assessment system and methodologies for real-time, easy, reliable and accurate assessment and prediction of solar energy potential in relation to energy consumption in urban areas.