Jinha Jung Researcher Spotlight
researcher spotlight
This month’s IDAAS Affiliate Spotlight features Jinha Jung, an Associate Professor in Purdue’s Lyles School of Civil and Construction Engineering whose work advances geospatial data science and remote sensing. As an IDAAS affiliate, he contributes research in UAS‑based sensing, LiDAR analytics, and open‑source geospatial platforms that strengthen data‑driven innovation across disciplines.
Jinha Jung
Jinha Jung is an Associate Professor in the Lyles School of Civil and Construction Engineering at Purdue University, specializing in Geomatics Engineering. He joined Purdue’s faculty in 2019, bringing a research background that spans remote sensing, geospatial data science, and high‑performance computing.
Outside of his academic work, Jinha enjoys staying active through running, tennis, pickleball, and other sports that keep him energized and outdoors.
Jinha Jung
Associate Professor of Civil and Construction Engineering | IDAAS Affiliate
Jinha Jung Associate Professor of Civil and Construction Engineering | IDAAS Affiliate
About the research
Jinha Jung’s research focuses on advancing geospatial data science through cutting‑edge remote sensing and high‑performance computing. His work spans unmanned aircraft systems (UAS), LiDAR, multisource remote sensing data fusion, and WebGIS development, with applications in agriculture, environmental monitoring, and large‑scale geospatial analytics.
He has made significant contributions to areas such as UAS‑based high‑throughput phenotyping, automated feature extraction, vegetation structural analysis, and algorithms for processing hyperspectral and full‑waveform LiDAR data. His research also emphasizes building open‑source geospatial platforms to support data management, visualization, and reproducible analysis.
Overall, Jung’s work integrates advanced sensing technologies with computational tools to extract meaningful, actionable insights from complex geospatial datasets.
Q&A
My research group (Geospatial Data Science Lab) conducts interdisciplinary research at the intersection of geomatics, remote sensing, and computational science. My research focuses on developing advanced sensing and data analytics technologies for large-scale environmental and agricultural applications. My research group is widely recognized for its pioneering work on uncrewed aircraft systems (UAS)-based high-throughput phenotyping systems that enable efficient and precise agricultural monitoring. Our research includes developing novel algorithms for processing and analyzing UAS data, with particular emphasis on feature extraction, vegetation characterization, and spatial analysis.
My research group is focusing on developing Geospatial AI (GeoAI) algorithms using large-scale geospatial data, including UAS HTP data and national-scale initiatives such as USGS 3DEP and NAIP. We specifically developed a Data to Science (D2S) open source ecosystem to manage big geospatial data to become a leader in GeoAI development.
The Data to Science (D2S) platform is an innovative, open-source initiative designed to facilitate data sharing and collaboration among researchers worldwide. D2S aims to create a data-driven open science community that promotes sustained innovation. Researchers can upload, manage, and share their UAV data, making it accessible to a broader audience. This collaborative approach helps in advancing research by providing a centralized repository of valuable datasets from various projects worldwide. The platform is open-source, allowing anyone to deploy it in their own environment, ensuring flexibility and adaptability to different research needs.
Yes: Anyone who is interested in taking advantage of big geospatial data for agricultural applications. A prior degree is not required, but I am looking for someone who is curious, creative, and proactive, eager to become a leader in their profession.
I would love to collaborate with groups with their own domain expertise, while they need geospatial data science capabilities to solve challenging problems.