Conrad M Albrecht

915 total citations · 1 hit paper
34 papers, 542 citations indexed

About

Conrad M Albrecht is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Conrad M Albrecht has authored 34 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Media Technology, 11 papers in Computer Vision and Pattern Recognition and 8 papers in Artificial Intelligence. Recurrent topics in Conrad M Albrecht's work include Remote-Sensing Image Classification (12 papers), Remote Sensing in Agriculture (7 papers) and Advanced Image and Video Retrieval Techniques (7 papers). Conrad M Albrecht is often cited by papers focused on Remote-Sensing Image Classification (12 papers), Remote Sensing in Agriculture (7 papers) and Advanced Image and Video Retrieval Techniques (7 papers). Conrad M Albrecht collaborates with scholars based in Germany, United States and France. Conrad M Albrecht's co-authors include Yi Wang, Xiao Xiang Zhu, Nassim Ait Ali Braham, Lichao Mou, Levente J. Klein, Hendrik F. Hamann, Chenying Liu, Marcus Freitag, Sandro Wimberger and Zhitong Xiong and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review B and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Conrad M Albrecht

31 papers receiving 525 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.

Self-Supervised Learning in Remote Sensing: A review

2022 194 citations Yi Wang, Conrad M Albrecht et al. IEEE Geoscience and Remote Sensing Magazine profile →

Peers

Conrad M Albrecht

CVPR

ECOLO

Runyu Fan China

Chunju Zhang China

Corneliu Octavian Dumitru Germany

Gencer Sumbül Germany

Giovanni Marchisio United States

Vladimir A. Krylov Italy

Arnaud Le Bris France

Bo Dang China

Alaitz Zabala Spain

Runyu Fan China

Conrad M Albrecht

258 ×1.2

158 ×1.2

CVPR

158 ×1.4

59 ×0.7

ECOLO

87 ×1.0

Citations per year, relative to Conrad M Albrecht Conrad M Albrecht (= 1×) peers Runyu Fan

Countries citing papers authored by Conrad M Albrecht

Since Specialization

Citations

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

Fields of papers citing papers by Conrad M Albrecht

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Conrad M Albrecht. 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 Conrad M Albrecht. The network helps show where Conrad M Albrecht may publish in the future.

Co-authorship network of co-authors of Conrad M Albrecht

This figure shows the co-authorship network connecting the top 25 collaborators of Conrad M Albrecht. A scholar is included among the top collaborators of Conrad M Albrecht 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 Conrad M Albrecht. Conrad M Albrecht 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

Liu, Chenying, et al.. (2025). CromSS: Cross-Modal Pretraining With Noisy Labels for Remote Sensing Image Segmentation. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–17. 1 indexed citations

Wang, Yi, et al.. (2024). Feature Guided Masked Autoencoder for Self-Supervised Learning in Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 321–336. 13 indexed citations

Freitag, Marcus, et al.. (2024). Climatic & Anthropogenic Hazards to the Nasca World Heritage: Application of Remote Sensing, AI, and Flood Modelling. elib (German Aerospace Center). 2212–2215. 2 indexed citations

Wang, Yi, et al.. (2024). Multilabel-Guided Soft Contrastive Learning for Efficient Earth Observation Pretraining. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–16. 4 indexed citations

Wang, Yi, Conrad M Albrecht, & Xiao Xiang Zhu. (2024). Multi-Label Guided Supervised Contrastive Learning for Earth Observation Pretraining. elib (German Aerospace Center). 7568–7571.

Liu, Chenying, et al.. (2024). AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing. elib (German Aerospace Center). 2023–2027. 2 indexed citations

Lu, Siyuan, et al.. (2024). AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs and sheds light on their purpose. Proceedings of the National Academy of Sciences. 121(40). e2407652121–e2407652121. 5 indexed citations

Liu, Chenying, Conrad M Albrecht, Yi Wang, & Xiao Xiang Zhu. (2024). Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation. elib (German Aerospace Center). 7040–7044. 2 indexed citations

Wang, Yi, Nassim Ait Ali Braham, Zhitong Xiong, et al.. (2023). SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine. 11(3). 98–106. 61 indexed citations

10.

Song, Qian, Conrad M Albrecht, Zhitong Xiong, & Xiao Xiang Zhu. (2023). Biomass Estimation and Uncertainty Quantification From Tree Height. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 16. 4833–4845. 8 indexed citations

11.

Pande, Shivam, Nassim Ait Ali Braham, Yi Wang, et al.. (2023). Semi-Supervised Learning for Hyperspectral Images by Non Parametrically Predicting View Assignment^CRediT. elib (German Aerospace Center). 6085–6088.

12.

Wang, Yi, Chenying Liu, Micha Silver, et al.. (2022). Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection. 2022 IEEE International Conference on Big Data (Big Data). 4888–4892.

13.

Liu, Chenying, Conrad M Albrecht, Yi Wang, & Xiao Xiang Zhu. (2022). Peaks Fusion assisted Early-stopping Strategy for Overhead Imagery Segmentation with Noisy Labels. 2022 IEEE International Conference on Big Data (Big Data). 4842–4847. 2 indexed citations

14.

Albrecht, Conrad M, et al.. (2021). AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning. 2021 IEEE International Conference on Big Data (Big Data). 1779–1786. 10 indexed citations

15.

Albrecht, Conrad M, Bruce G. Elmegreen, Oki Gunawan, et al.. (2020). Next-generation geospatial-temporal information technologies for disaster management. IBM Journal of Research and Development. 64(1/2). 5:1–5:12. 11 indexed citations

16.

Zhang, Rui, Conrad M Albrecht, Wei Zhang, et al.. (2020). Map Generation from Large Scale Incomplete and Inaccurate Data Labels. 2514–2522. 12 indexed citations

17.

Elmegreen, Bruce G., et al.. (2019). Physical Analytics Integrated Repository and Services for Astronomy: PAIRS-A. Bulletin of the American Astronomical Society. 51(7). 28. 1 indexed citations

18.

Klein, Levente J., Conrad M Albrecht, Wang Zhou, et al.. (2019). N-dimensional geospatial data and analytics for critical infrastructure risk assessment. 4. 5637–5643. 4 indexed citations

19.

Albrecht, Conrad M, et al.. (2019). Learning and Recognizing Archeological Features from LiDAR Data. arXiv (Cornell University). 5630–5636. 23 indexed citations

20.

Klein, Levente J., Conrad M Albrecht, Marcus Freitag, et al.. (2015). PAIRS: A scalable geo-spatial data analytics platform. 1290–1298. 50 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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