My research centers on enhancing urban resilience through advanced 3D urban mapping and analyses, employing deep learning methods and digital twin simulations. I am a Ph.D. candidate in Geomatics at Purdue University’s Civil Engineering Department, working in the Geospatial Data Science Lab. Having successfully passed my Ph.D. defense, I anticipate graduating in May 2024.

← Me in Pasadena, CA, while attending the 2023 IEEE International Geoscience and Remote Sensing Symposium. :)


Research Projects

The 3D Landscape Clustering Framework

H. Song, A.Shreevastava, G. Cervini, J. Jung

Urban areas, increasingly dense and expanding, confront significant climate change challenges, particularly urban warming exacerbated by their landscapes. Addressing the complexity of urban temperature phenomena requires understanding the interplay of landscape features, geographical conditions, human activities, and climate factors. Our research introduces the 3D Landscape Clustering (3LC) Framework, which employs 3D land cover maps to enhance urban landscape analysis for climate studies. This framework aims to provide a precise and scalable approach to dissecting urban heat phenomena by effectively incorporating landscape variables. It represents a step forward in developing targeted strategies for urban planning and climate mitigation, offering insights into the nuanced effects of urban landscapes on temperature dynamics.

Geospatial Digital Twin

H. Song, J. Jung

We're embarking on an ambitious project to create digital twin cities across the U.S., with funding from the National Geospatial-Intelligence Agency (NGA)! These virtual replicas will serve a crucial role in innovative studies aimed at promoting urban equity and resilience. A 3D land cover map would be one of the foundational layers for the twins.

Harnessing Digital Twin Simulations for Heat-Resilient Urban Planning

H. Song, G. Cervini, J. Jung

I'm interested in analyzing urban heat using digital twin simulations and deep learning models, specifically focusing on the role of 3D urban canopies. Thrillingly, my preliminary findings (slide) were recognized, earning 2nd place in a podium presentation at the 28th Environmental Engineering & Science Symposium, UIUC. Eager to present these findings in a paper soon! :)

Accessible Area Mapper — Paving the Way for Inclusive Urban Mobility

H. Song, J. Carpenter, J. Froehlich, J. Jung

Historically, navigation systems relied mainly on image data or GPS, overlooking details like pathway width and slope vital for diverse mobility needs. This oversight stemmed from the dominant use of deep learning road mapping, which often used biased bird's-eye-view imagery. To address this, I introduced the "Accessible Area Mapper" that understands 3D environments to identify navigable routes. This system caters to multiple mobility needs, ensuring accessibility for all, including people with disabilities. We collaborate with a human-centered computing expert from the University of Washington with extensive experience in "Project Sidewalk".

LiDAR for 3D Hydrography

H. Song, J. Jung
Paper is on the way!

We developed a unique surface water mapping technique using only airborne LiDAR data. This method utilizes the principle that locally connected surface water maintains a consistent elevation due to surface tension. By integrating this "physical constraint" into the decision-making process, our method surpasses previous methods. As it depends solely on airborne LiDAR and operates unsupervised, it's scalable for extensive topographic mapping, making it more practical and reliable for large-scale projects.

A New Digital Terrain Model (DTM)

H. Song, J. Jung
Paper (Remote Sensing)

DTM is a three-dimensional representation of the bare earth surface that excludes objects such as trees and buildings. Conventional DTM generation methods consider the local-neighborhood of a 3D point and struggle to clearly define what an object is. In contrast, our method considers the global context and provides a clear definition of both objects and ground. Our method has a unique property in that it consistently considers bridges and overpasses as ground. With this unique property of our DTM, only objects standing on the ground can remain in the normalized DSM. This offers significant advantages in large-area 3D urban object mapping.

Unsupervised 3D building mapping with LiDAR

H. Song, J. Jung

3D building maps are essential for digital twin cities and offer valuable information for various smart city applications. We developed an unsupervised 3D building mapping pipeline using airborne LiDAR, which outperforms deep-learning-based methods in accuracy. This pipeline won 1st place at the ACM SIGSPATIAL GIS Cup 2022!

Check out the following 3D building models via the web! The taller the building, the darker color it has.
* 3D New York City
* 3D Denver
* 3D New Orleans

3D building maps will be kept updated for the entire US. Please email me if you want to get TIFF image (Files are HUGE!)

Weekly supervised learning with crowdsourced data

H. Song, HL. Yang, J. Jung

The crowdsourced label has a great potential for deep learning. However, the low quality of labels hampers the successful learning of the deep model. In this work, we propose a self-filtered learning that enables deep models to learn effectively with low-quality OpenStreetMap labels. Our method progressively refines training samples during the training process based on the loss of training samples.

Photovoltaic (PV) power forecast with meteorological satellite

H. Song,, G. Kim, M. Kim, Y. Kim

An accurate PV power forecast is important for efficient power system planning in smart cities. We proposed a deep model that integrates multi-temporal meteorological satellite images and historical PV data for short-term PV forecasting. Our framework is being used on the platform of SKT, South Korea's largest wireless carrier. Also, I am very proud to have worked as the project manager for this project. (Thanks SKT for providing the cool GIF!)

Here is another work with my colleagues where we compared the significance of implementing different meteorological satellite images for PV forecast.

Patch-based Light CNN: Land cover mapping with satellite images

H. Song, Y. Kim, Y. Kim
Paper (Remote Sensing)

The land cover map is quintessential data for various urban and environmental studies. The U.S. Geological Survey (USGS & MRLC) has been mapping land cover maps of the entire US for more than 20 years. One difficulty is the ground-truth is considerably site-dependent and not pure as one pixel's resolution is 30m by 30m. Our work presents that a very simply patch-based CNN model performs better by utilizing contextual information than conventional pixel-based algorithms.

Here is another piece of extended research for boosting performance based on the sample filtering.


Awards and Recognition

  • Podium Presentation Award (2nd place), 28th Environmental Engineering & Science Symposium, Champaign, IL, 2023.
  • GIS Cup Winner (1st place), 30th ACM SIGSPATIAL, Seattle, WA, 2022.
  • Roland S. Corning II Fellowship, Purdue University, 2022.
  • Frederick N. Andrews Fellowship, Purdue University, 2020-2024.
  • Best Student Paper Award, International Symposium on Remote Sensing, 2019.
  • Best Research Award (2nd place), Korea Meteorological Administration, 2019.
  • Merit-based Scholarship, Seoul National University, 2018.
  • Brain Korea 21 Plus Scholarship, National Research Foundation of Korea, 2018.
  • Certificate of Commendation, Korean Society of Survey, Geodesy, Photogrammetry, and Cartography, 2018.
  • Best Bachelor's Thesis Award (3rd place), Seoul National University, 2017.
  • National Scholarship for Science and Engineering, Korea Student Aid Foundation, 2014-2017.
  • Merit-based Scholarship, Seoul National University, 2012-2013.