Hiroya Maeda

2.4k total citations · 4 hit papers
21 papers, 1.5k citations indexed

About

Hiroya Maeda is a scholar working on Civil and Structural Engineering, Ocean Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Hiroya Maeda has authored 21 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Civil and Structural Engineering, 12 papers in Ocean Engineering and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Hiroya Maeda's work include Infrastructure Maintenance and Monitoring (14 papers), Geophysical Methods and Applications (9 papers) and Asphalt Pavement Performance Evaluation (6 papers). Hiroya Maeda is often cited by papers focused on Infrastructure Maintenance and Monitoring (14 papers), Geophysical Methods and Applications (9 papers) and Asphalt Pavement Performance Evaluation (6 papers). Hiroya Maeda collaborates with scholars based in Japan, India and United States. Hiroya Maeda's co-authors include Yoshihide Sekimoto, Takehiro Kashiyama, Hiroshi Omata, Toshikazu Seto, Deeksha Arya, Durga Toshniwal, Sanjay Kumar Ghosh, Alexander Mráz, Hiroyasu Kobayashi and Seiji Miyashita and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Remote Sensing.

In The Last Decade

Hiroya Maeda

19 papers receiving 1.4k citations

Hit Papers

align trajectories

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.

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

2018 592 citations Hiroya Maeda, Yoshihide Sekimoto et al. Computer-Aided Civil and Infrastructure Engineering profile →
Generative adversarial network for road damage detection

2020 239 citations Hiroya Maeda, Takehiro Kashiyama et al. Computer-Aided Civil and Infrastructure Engineering profile →
Deep learning-based road damage detection and classification for multiple countries

2021 181 citations Deeksha Arya, Hiroya Maeda et al. Automation in Construction profile →
RDD2020: An annotated image dataset for automatic road damage detection using deep learning

2021 155 citations Deeksha Arya, Hiroya Maeda et al. SHILAP Revista de lepidopterología profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Hiroya Maeda Japan	11	1.3k	351	333	212	155	21	1.5k
Takehiro Kashiyama Japan	12	1.1k 0.8×	290 0.8×	291 0.9×	187 0.9×	164 1.1×	36	1.4k
Hiroshi Omata Japan	6	890 0.7×	229 0.7×	215 0.6×	160 0.8×	101 0.7×	10	1.0k
Yichang Tsai United States	23	1.6k 1.3×	362 1.0×	316 0.9×	272 1.3×	205 1.3×	139	2.1k
Yahui Liu China	14	737 0.6×	307 0.9×	246 0.7×	232 1.1×	34 0.2×	32	1.3k
Baoxian Li China	10	1.5k 1.2×	88 0.3×	210 0.6×	364 1.7×	101 0.7×	24	1.7k
Enhui Yang China	15	1.5k 1.2×	87 0.2×	184 0.6×	409 1.9×	109 0.7×	35	1.7k
Anh Duc Le Japan	11	791 0.6×	272 0.8×	121 0.4×	237 1.1×	49 0.3×	28	1.2k
Ju Huyan China	17	1.1k 0.8×	106 0.3×	138 0.4×	266 1.3×	53 0.3×	37	1.2k
Vedhus Hoskere United States	16	1.3k 1.0×	328 0.9×	125 0.4×	217 1.0×	85 0.5×	33	1.6k

Countries citing papers authored by Hiroya Maeda

Since Specialization

Citations

This map shows the geographic impact of Hiroya Maeda'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 Hiroya Maeda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hiroya Maeda more than expected).

Fields of papers citing papers by Hiroya Maeda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hiroya Maeda. 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 Hiroya Maeda. The network helps show where Hiroya Maeda may publish in the future.

Co-authorship network of co-authors of Hiroya Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroya Maeda. A scholar is included among the top collaborators of Hiroya Maeda 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 Hiroya Maeda. Hiroya Maeda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Arya, Deeksha, Hiroya Maeda, & Yoshihide Sekimoto. (2024). From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection. Advanced Engineering Informatics. 60. 102388–102388. 22 indexed citations

Arya, Deeksha, Hiroshi Omata, Hiroya Maeda, & Yoshihide Sekimoto. (2024). ORDDC’2024: State of the art Solutions for Optimized Road Damage Detection. 8430–8438. 5 indexed citations

Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, & Yoshihide Sekimoto. (2024). RDD2022: A multi‐national image dataset for automatic road damage detection. Geoscience Data Journal. 31 indexed citations

Ogawa, Yoshiki, et al.. (2024). End-to-End Framework for the Automatic Matching of Omnidirectional Street Images and Building Data and the Creation of 3D Building Models. Remote Sensing. 16(11). 1858–1858.

Kashiyama, Takehiro, et al.. (2022). Citywide reconstruction of traffic flow using the vehicle-mounted moving camera in the CARLA driving simulator. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 21. 2292–2299. 4 indexed citations

Kashiyama, Takehiro, et al.. (2022). Vehicle re-identification and trajectory reconstruction using multiple moving cameras in the CARLA driving simulator. 2022 IEEE International Conference on Big Data (Big Data). 1858–1865. 2 indexed citations

Arya, Deeksha, et al.. (2022). Road Rutting Detection using Deep Learning on Images. 2022 IEEE International Conference on Big Data (Big Data). 1362–1368. 6 indexed citations

Kashiyama, Takehiro, et al.. (2022). Real-time citywide reconstruction of traffic flow from moving cameras on lightweight edge devices. ISPRS Journal of Photogrammetry and Remote Sensing. 192. 115–129. 19 indexed citations

Maeda, Hiroya, et al.. (2022). Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset. Sensors. 22(24). 9992–9992. 6 indexed citations

10.

Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, et al.. (2022). Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022). 2022 IEEE International Conference on Big Data (Big Data). 6378–6386. 44 indexed citations

11.

Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, & Yoshihide Sekimoto. (2021). RDD2020: An annotated image dataset for automatic road damage detection using deep learning. SHILAP Revista de lepidopterología. 36. 107133–107133. 155 indexed citations breakdown →

12.

Kashiyama, Takehiro, et al.. (2021). Citywide reconstruction of cross-sectional traffic flow from moving camera videos. 2021 IEEE International Conference on Big Data (Big Data). 1670–1678. 10 indexed citations

13.

Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, et al.. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction. 132. 103935–103935. 181 indexed citations breakdown →

14.

Arya, Deeksha, Hiroya Maeda, Sanjay Kumar Ghosh, et al.. (2020). Global Road Damage Detection: State-of-the-art Solutions. arXiv (Cornell University). 5533–5539. 108 indexed citations

15.

Maeda, Hiroya, Takehiro Kashiyama, Yoshihide Sekimoto, Toshikazu Seto, & Hiroshi Omata. (2020). Generative adversarial network for road damage detection. Computer-Aided Civil and Infrastructure Engineering. 36(1). 47–60. 239 indexed citations breakdown →

16.

Seto, Toshikazu, Yoshihide Sekimoto, Hiroshi Omata, et al.. (2019). The Development of Open Source Based Citizen Collaboration Applications for Infrastructure Management: My City Report. Proceedings of the ICA. 2. 1–4.

17.

Maeda, Hiroya, Yoshihide Sekimoto, Toshikazu Seto, Takehiro Kashiyama, & Hiroshi Omata. (2018). Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images. Computer-Aided Civil and Infrastructure Engineering. 33(12). 1127–1141. 592 indexed citations breakdown →

18.

Maeda, Hiroya, Yoshihide Sekimoto, & Toshikazu Seto. (2016). An Easy Infrastructure Management Method Using On-Board Smartphone Images and Citizen Reports by Deep Neural Network. 111–113. 4 indexed citations

19.

Maeda, Hiroya, Yoshihide Sekimoto, & Toshikazu Seto. (2016). Lightweight road manager. 37–45. 21 indexed citations

20.

Kimura, Mutsumi, et al.. (2000). An area‐ratio gray‐scale method to achieve image uniformity in TFT‐LEPDs. Journal of the Society for Information Display. 8(2). 93–97. 16 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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