About the Urban Analytics Lab
We are a multidisciplinary research group focusing on urban data management and analysis, geographic data science, and digital twins at the National University of Singapore (NUS), a leading global university centred in Asia.
Our mission is to leverage and make sense of big geospatial data at different scales for urban applications and catalyse the development of spatial data infrastructures and digital twins in the realm of smart cities and the built environment. We are particularly interested in the interface of emerging urban datasets such as street-level imagery, dynamic/sensor data, and 3D city models with the state of the art of artificial intelligence to solve contemporary urban challenges and provide a strong foundation to advance urban informatics. Crowdsourcing plays an important role in our research, as we follow and contribute to vibrant developments in Volunteered Geographic Information (VGI) and engage such data in our research.
The research group was established in 2019 by its Director/PI Dr Filip Biljecki, Assistant Professor at the NUS College of Design and Engineering and the NUS Business School. You can read more about our research agenda also in an interview with the PI.
We are involved in several international collaborations such as the International Society for Photogrammetry and Remote Sensing (ISPRS) and the Open Geospatial Consortium (OGC). Within NUS, we collaborate primarily with three sister labs: the Building and Urban Data Science (BUDS) Lab, the Integrated Data, Energy Analysis + Simulation (IDEAS) Lab, and the Urban Climate Design Lab (UCDL), forming a constellation of research groups with complementary activities operating at converging scales.
Some of our activities are also featured in a recent video:
You are welcome to follow our work through Twitter, LinkedIn, blog, and papers. You may also be interested in our home-grown data and code that we released openly.
The full list of our interests and key words: Geographical Information Science (GIS), geospatial machine learning, GeoAI, geographic data science, spatial data infrastructure (SDI), 3D city modelling / 3D GIS / digital twins, 3D urban analytics, street view imagery, spatial data quality and standardisation (CityJSON, CityGML), thermography, Volunteered geoinformation (VGI) and OpenStreetMap (OSM), Building Information modelling (BIM) 3D underground data modelling, and 3D cadastre.