Mo Su

1.3k total citations · 1 hit paper
16 papers, 466 citations indexed

About

Mo Su is a scholar working on Transportation, Global and Planetary Change and Health. According to data from OpenAlex, Mo Su has authored 16 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Transportation, 7 papers in Global and Planetary Change and 3 papers in Health. Recurrent topics in Mo Su's work include Land Use and Ecosystem Services (5 papers), Urban Transport and Accessibility (5 papers) and Health disparities and outcomes (3 papers). Mo Su is often cited by papers focused on Land Use and Ecosystem Services (5 papers), Urban Transport and Accessibility (5 papers) and Health disparities and outcomes (3 papers). Mo Su collaborates with scholars based in China, Hong Kong and United States. Mo Su's co-authors include Wuyang Hong, Renzhong Guo, Wei Hu, Chengyun Yang, Fangfang Zhang, Qi Liao, Bing Xu, Бин Чэн, Hao Tang and Liuxin Chen and has published in prestigious journals such as Remote Sensing of Environment, International Journal of Environmental Research and Public Health and Remote Sensing.

In The Last Decade

Mo Su

14 papers receiving 457 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.

Deep learning for urban land use category classification: A review and experimental assessment

2024 65 citations Ziming Li, Бин Чэн et al. Remote Sensing of Environment profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mo Su China	9	299	125	72	70	69	16	466
Fanan Ujoh United Kingdom	12	379 1.3×	71 0.6×	60 0.8×	82 1.2×	52 0.8×	25	588
Qiang Bie China	12	235 0.8×	143 1.1×	74 1.0×	91 1.3×	56 0.8×	27	385
Narumasa Tsutsumida Japan	13	302 1.0×	184 1.5×	111 1.5×	65 0.9×	55 0.8×	48	545
Davoud Omarzadeh Iran	10	208 0.7×	111 0.9×	70 1.0×	44 0.6×	82 1.2×	15	427
Lakshmi N. Kantakumar India	9	427 1.4×	128 1.0×	110 1.5×	126 1.8×	35 0.5×	10	553
Arif Masrur United States	10	329 1.1×	160 1.3×	121 1.7×	125 1.8×	82 1.2×	16	555
Xiaoran Huang China	12	382 1.3×	175 1.4×	114 1.6×	158 2.3×	73 1.1×	36	686
Lisha Tang China	5	354 1.2×	102 0.8×	76 1.1×	65 0.9×	41 0.6×	10	472
Christiane Weber France	11	176 0.6×	64 0.5×	66 0.9×	64 0.9×	25 0.4×	38	405
Hang Chen China	12	282 0.9×	96 0.8×	30 0.4×	92 1.3×	133 1.9×	31	466

Countries citing papers authored by Mo Su

Since Specialization

Citations

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

Fields of papers citing papers by Mo Su

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mo Su

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

All Works

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16 of 16 papers shown

Li, Ziming, Бин Чэн, Shengbiao Wu, et al.. (2024). Deep learning for urban land use category classification: A review and experimental assessment. Remote Sensing of Environment. 311. 114290–114290. 65 indexed citations breakdown →

Huang, Huabing, et al.. (2023). Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities. Remote Sensing of Environment. 298. 113802–113802. 35 indexed citations

Hu, Wei, Lin Li, & Mo Su. (2022). Modeling Health Seeking Behavior Based on Location-Based Service Data: A Case Study of Shenzhen, China. ISPRS International Journal of Geo-Information. 11(5). 295–295. 8 indexed citations

Yang, Jun, Yixiong Xiao, Shaoqing Shen, et al.. (2021). Using Internet Search Queries to Assess Public Awareness of the Healthy Cities Approach: A Case Study in Shenzhen, China. International Journal of Environmental Research and Public Health. 18(8). 4264–4264. 2 indexed citations

Su, Mo, et al.. (2021). A multi-level visualization method for IT system structure. Procedia Computer Science. 183. 661–668.

Yang, Jun, Yixiong Xiao, Shaoqing Shen, et al.. (2020). Comparing the Use of Spatially Explicit Indicators and Conventional Indicators in the Evaluation of Healthy Cities: A Case Study in Shenzhen, China. International Journal of Environmental Research and Public Health. 17(20). 7409–7409. 9 indexed citations

Su, Mo, Renzhong Guo, Бин Чэн, et al.. (2020). Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sensing. 12(9). 1497–1497. 25 indexed citations

Hu, Wei, Lin Li, & Mo Su. (2019). Spatial Inequity of Multi-Level Healthcare Services in a Rapid Expanding Immigrant City of China: A Case Study of Shenzhen. International Journal of Environmental Research and Public Health. 16(18). 3441–3441. 23 indexed citations

Hong, Wuyang & Mo Su. (2019). Influence of Rapid Transit on Accessibility Pattern and Economic Linkage at Urban Agglomeration Scale in China. Open Geosciences. 11(1). 804–814. 10 indexed citations

10.

Su, Mo, Renzhong Guo, & Wuyang Hong. (2018). Institutional transition and implementation path for cultivated land protection in highly urbanized regions: A case study of Shenzhen, China. Land Use Policy. 81. 493–501. 76 indexed citations

11.

Hu, Wei, Mo Su, & Chengyun Yang. (2018). A Comprehensive Spatial Assessment Method of Medical Facilities Under Hierarchical Medical System: A Case Study in Shenzhen City. 1–9.

12.

Hong, Wuyang, Renzhong Guo, Mo Su, et al.. (2017). Sensitivity evaluation and land-use control of urban ecological corridors: A case study of Shenzhen, China. Land Use Policy. 62. 316–325. 77 indexed citations

13.

Hong, Wuyang, et al.. (2016). Establishing an ecological vulnerability assessment indicator system for spatial recognition and management of ecologically vulnerable areas in highly urbanized regions: A case study of Shenzhen, China. Ecological Indicators. 69. 540–547. 133 indexed citations

14.

Li, Manchun, et al.. (2007). Research on comprehensive evaluation to land consolidation supported by GIS: a case study of Shaoshan city in Hunan province. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6754. 675439–675439. 1 indexed citations

15.

Li, Manchun, et al.. (2007). Study on geographical information services. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6753. 67533A–67533A. 1 indexed citations

16.

Su, Mo, et al.. (2007). Cartographic generalization of cultivated land in hilly areas based on skeleton algorithm. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6751. 67510W–67510W. 1 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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