About Me

Yang CHEN (陈阳) is a PhD candidate at Nanjing Normal University, China, with expertise in GIS, Remote Sensing, and Cartography, as well as an interdisciplinary background in geomorphology and terrain modeling. His research focuses on urban modeling and urban morphology by leveraging multi-source geospatial data, such as street view imagery, digital elevation models, and remote sensing imagery. Recently, Yang’s work has concentrated on estimating building heights at the footprint level using machine learning methods and reconstructing the 3D typology of road networks on a large scale. He is also involved in projects that integrate urban analytics with environmental sustainability. He aims to contribute to sustainable urban development through innovative data-driven solutions.

Interests
  • 3D Urban Analytics
  • Geomorphometry
  • Urban Morphology
Education

  • Visiting Scholar in Urban Analytics Lab

    College of Design and Engineering, National University of Singapore

  • PhD Candidate in Cartography and Geographic Information Systems

    School of Geography, Nanjing Normal University, China

  • BSc Geographic Information Science

    Zhejiang Agriculture & Forest University, China

📚 My Research

I’m a scholar in the Digital Terrain Analytics team at Nanjing Normal University. And now, I am a visiting scholar at the UAL, National University of Singapore. My research focuses on the intersection of Geography, Computer Science, and Urban Science.

I apply a range of qualitative and quantitative methods to comprehensively investigate the urban issues and artificial landscape.

Please reach out to collaborate 😃

One hour coding a day, keep the bugs away. 🐞

Featured Publications
15-Minute City

Vertical 15-minute city: Modeling urban density and functional mix with multi-source geospatial data

This study introduces a 3D framework for assessing 15-minute accessibility in Nanjing, China. Using natural language processing and rule-based methods, we construct a 3D functional composition dataset from multi-source data. We then develop floor-level proximity indices that account for both horizontal travel time and vertical circulation (e.g., stairs, elevators). Analyzing over 90 million simulated trips, we find that accessibility generally declines with building height, though access to offices and commercial facilities improves at 20th or higher floors. Spatial inequalities emerge not only between central and peripheral zones but also across building levels and regional GDP levels, with a U-shaped disparity tied to distance from downtown. Notably, 11%–17% of trips considered accessible in 2D analyses exceed the 15-minute threshold when vertical travel is included. Our findings highlight the need to incorporate vertical space in 15-minute city evaluations and offer a scalable method to support inclusive, fair, and livable 3D urban planning with the background of 15-minute city.

Anthropogenic Landforms

Characterizing spatial patterns and regionalization of anthropogenic landforms using multi-source geospatial data: Insights from Loess Plateau of China

Anthropogenic forces have become a significant factor in the development of geomorphology, influencing landform morphology and usage in diverse ways across natural and societal environments. Despite their growing impact, few studies have quantified the morphology and distribution of anthropogenic landforms at large scales to reveal the spatial patterns of human modification on the surface. To address them, we propose a framework that includes three classical anthropogenic landforms, i.e., terraces, check dams, and impervious surfaces, on the Chinese Loess Plateau (CLP) to measure the morphology differences at the basin level. This research provides a new perspective and robust methodological framework for analyzing the morphology pattern of anthropogenic landforms. It could be a data foundation for future studies exploring the interactions between human activities and geomorphological processes.

Refining urban morphology: An explainable machine learning method for estimating footprint-level building height
Mapping Global Basic Landforms Using 30-meter DEMs
Recent Publications
Pengyuan Liu, Yang Chen, Xiucheng Liang, Hao Li, Filip Biljecki, Rudi Stouffs (2025). A graph neural network for small-area estimation: integrating spatial regularisation, heterogeneous spatial units, and Bayesian inference. International Journal of Geographical Information Science.
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Yang Chen, Xin Yang, Xin Li, Ling Yang, Sifan Cheng, Guoan Tang, Filip Biljecki (2025). Vertical 15-minute city: Modeling urban density and functional mix with multi-source geospatial data. Cities.
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Xin Yang, Sijin Li, Junfei Ma, Yang Chen, Xingyu Zhou, Fayuan Li, Liyang Xiong, Chenghu Zhou, Guoan Tang, Michael E. Meadows (2025). A typology of global relief classes derived from digital elevation models at 1 arcsec resolution. Earth Syst. Sci. Data.
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Wenjie Sun, Yang Chen, Xingyu Zhou, Xin Yang, Junfei Ma, Sijin Li, Guoan Tang (2025). Understanding the hydrological valley landscape: A multi-scenario adaptive framework for delineating valley floors. CATENA.
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Yang Chen, Xin Yang, Huangjunxi Fu, Chenrui Li, Guoan Tang (2025). Characterizing spatial patterns and regionalization of anthropogenic landforms using multi-source geospatial data: Insights from Loess Plateau of China. Geomorphology.
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