Hongjo Kim

2.6k total citations · 1 hit paper
46 papers, 1.6k citations indexed

About

Hongjo Kim is a scholar working on Civil and Structural Engineering, Geology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hongjo Kim has authored 46 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Civil and Structural Engineering, 13 papers in Geology and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hongjo Kim's work include Infrastructure Maintenance and Monitoring (26 papers), 3D Surveying and Cultural Heritage (13 papers) and Occupational Health and Safety Research (10 papers). Hongjo Kim is often cited by papers focused on Infrastructure Maintenance and Monitoring (26 papers), 3D Surveying and Cultural Heritage (13 papers) and Occupational Health and Safety Research (10 papers). Hongjo Kim collaborates with scholars based in South Korea, United States and Canada. Hongjo Kim's co-authors include Hyoungkwan Kim, Somin Park, Seongdeok Bang, Kinam Kim, Hyeran Byun, Youngjib Ham, Ho Young Jeong, Wontae Kim, Hoyoung Jeong and Kyeongseok Kim and has published in prestigious journals such as Building and Environment, Automation in Construction and Journal of Construction Engineering and Management.

In The Last Decade

Hongjo Kim

40 papers receiving 1.6k citations

Hit Papers

Encoder–decoder network for pixel-level road crack detect... 2019 2026 2021 2023 2019 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Encoder–decoder network for pixel-level road crack detection in black-box images
    2019 347 citations Seongdeok Bang, Somin Park et al. Computer-Aided Civil and Infrastructure Engineering profile →

Peers

Hongjo Kim
Ehsan Rezazadeh Azar Canada
Man-Woo Park South Korea
Xincong Yang China
Christian Koch Germany
Seongdeok Bang South Korea
Jingdao Chen United States
Stephanie German Paal United States
Koshy Varghese India
Faridaddin Vahdatikhaki Netherlands
Ehsan Rezazadeh Azar Canada
Hongjo Kim
Citations per year, relative to Hongjo Kim Hongjo Kim (= 1×) peers Ehsan Rezazadeh Azar

Countries citing papers authored by Hongjo Kim

Since Specialization
Citations

This map shows the geographic impact of Hongjo Kim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hongjo Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hongjo Kim more than expected).

Fields of papers citing papers by Hongjo Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hongjo Kim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hongjo Kim. The network helps show where Hongjo Kim may publish in the future.

Co-authorship network of co-authors of Hongjo Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Hongjo Kim. A scholar is included among the top collaborators of Hongjo Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hongjo Kim. Hongjo Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kim, Taegeon, et al.. (2025). Optimizing large vision-language models for context-aware construction safety assessment. Automation in Construction. 180. 106510–106510.
2.
Kim, Taegeon, et al.. (2024). Image hashing-based shallow object tracker for construction worker monitoring in scaffolding scenes. Automation in Construction. 166. 105604–105604. 1 indexed citations
3.
Kim, Seokhwan, et al.. (2024). Context-Adaptive CCTV Pan-Tilt-Zoom method for Personal Protective Equipment Detection. Proceedings of the ... ISARC.
4.
Ahn, Seungjun, et al.. (2024). Effectiveness of retrieval augmented generation-based large language models for generating construction safety information. Automation in Construction. 170. 105926–105926. 11 indexed citations
5.
Kim, Hyoungkwan, et al.. (2024). Exploring the Long-Term Impact of Preventive Road Treatments on Municipal Highways. Journal of Management in Engineering. 40(4).
6.
Nguyen, Tam, et al.. (2023). Context-aware safety assessment system for far-field monitoring. Automation in Construction. 149. 104779–104779. 14 indexed citations
7.
Kim, Taegeon, et al.. (2023). Self-supervised sub-category exploration for Pseudo label generation. Automation in Construction. 151. 104862–104862.
8.
Nguyen, Tam, et al.. (2023). Semi-supervised domain adaptation for segmentation models on different monitoring settings. Automation in Construction. 149. 104773–104773. 9 indexed citations
9.
Jeong, Hoyoung, et al.. (2019). Optimization Procedure for Climate Change Adaptation Investment Planning: Case of Flood Disaster Prevention in Seoul. Journal of Water Resources Planning and Management. 146(2). 3 indexed citations
10.
Bang, Seongdeok, et al.. (2019). Encoder–decoder network for pixel-level road crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering. 34(8). 713–727. 347 indexed citations breakdown →
11.
Park, Somin, et al.. (2019). Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks. Journal of Computing in Civil Engineering. 33(3). 115 indexed citations
12.
Kim, Hongjo, et al.. (2018). Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation. Automation in Construction. 92. 188–198. 75 indexed citations
13.
Kim, Hongjo, et al.. (2018). Image retrieval using BIM and features from pretrained VGG network for indoor localization. Building and Environment. 140. 23–31. 97 indexed citations
14.
Jeong, Hoyoung, et al.. (2017). Prediction of Flexible Pavement Deterioration in Relation to Climate Change Using Fuzzy Logic. Journal of Infrastructure Systems. 23(4). 38 indexed citations
15.
Kim, Hongjo, et al.. (2017). Algorithm for Economic Assessment of Infrastructure Adaptation to Climate Change. Natural Hazards Review. 18(4). 6 indexed citations
16.
Bang, Seongdeok, et al.. (2017). UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Automation in Construction. 84. 70–80. 83 indexed citations
17.
Kim, Hongjo, et al.. (2017). Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning. Journal of Computing in Civil Engineering. 32(2). 192 indexed citations
18.
Kim, Hongjo, et al.. (2016). Data-driven scene parsing method for recognizing construction site objects in the whole image. Automation in Construction. 71. 271–282. 55 indexed citations
19.
Kim, Hongjo, et al.. (2015). Vision-Based Object-Centric Safety Assessment Using Fuzzy Inference: Monitoring Struck-By Accidents with Moving Objects. Journal of Computing in Civil Engineering. 30(4). 135 indexed citations
20.
Kim, Hongjo, et al.. (2014). On-Site Safety Management Using Image Processing and Fuzzy Inference. Computing in Civil and Building Engineering (2014). 1013–1020. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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