Ribana Roscher

3.6k total citations · 2 hit papers
70 papers, 2.0k citations indexed

About

Ribana Roscher is a scholar working on Ecology, Plant Science and Artificial Intelligence. According to data from OpenAlex, Ribana Roscher has authored 70 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Ecology, 19 papers in Plant Science and 17 papers in Artificial Intelligence. Recurrent topics in Ribana Roscher's work include Remote Sensing in Agriculture (21 papers), Smart Agriculture and AI (16 papers) and Remote-Sensing Image Classification (16 papers). Ribana Roscher is often cited by papers focused on Remote Sensing in Agriculture (21 papers), Smart Agriculture and AI (16 papers) and Remote-Sensing Image Classification (16 papers). Ribana Roscher collaborates with scholars based in Germany, United Kingdom and China. Ribana Roscher's co-authors include Jochen Garcke, Marco F. Duarte, Bastian Bohn, Björn Waske, Anna Kicherer, Wen Yang, Anna Kruspe, Jianxiang Feng, Richard Bamler and Peter Jung and has published in prestigious journals such as SHILAP Revista de lepidopterología, Remote Sensing of Environment and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Ribana Roscher

64 papers receiving 1.9k 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.

Explainable Machine Learning for Scientific Insights and Discoveries

2020 590 citations Ribana Roscher, Jochen Garcke et al. profile →
A survey of uncertainty in deep neural networks

2023 509 citations Jakob Gawlikowski, Mohsin Ali et al. Artificial Intelligence Review profile →

Peers

Ribana Roscher

AI

PS

ECOLO

EE

CVPR

María Pérez‐Ortiz United Kingdom

François Petitjean Australia

Xin Hu China

Mohd Anul Haq Saudi Arabia

Pierre Gançarski France

Silvia Liberata Ullo Italy

Dino Ienco France

J. Senthilnath Singapore

María Pérez‐Ortiz United Kingdom

Ribana Roscher

450 ×0.8

AI

320 ×0.9

PS

214 ×0.7

ECOLO

143 ×0.5

EE

241 ×1.2

CVPR

Citations per year, relative to Ribana Roscher Ribana Roscher (= 1×) peers María Pérez‐Ortiz

Countries citing papers authored by Ribana Roscher

Since Specialization

Citations

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

Fields of papers citing papers by Ribana Roscher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ribana Roscher

This figure shows the co-authorship network connecting the top 25 collaborators of Ribana Roscher. A scholar is included among the top collaborators of Ribana Roscher 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 Ribana Roscher. Ribana Roscher 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

1.

Nuske, Marlon, et al.. (2025). Explainability of Subfield Level Crop Yield Prediction Using Remote Sensing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 4141–4161. 7 indexed citations

2.

Roscher, Ribana, et al.. (2025). Enhancing decision support in crop production: Analyzing conformal prediction for uncertainty quantification. Computers and Electronics in Agriculture. 237. 110559–110559.

3.

Roscher, Ribana, Marc Rußwurm, Caroline Gevaert, et al.. (2025). Better, Not Just More: Data-centric machine learning for Earth observation. IEEE Geoscience and Remote Sensing Magazine. 13(3). 512–532.

4.

Drees, Lukas, et al.. (2024). Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks. Plant Methods. 20(1). 93–93. 10 indexed citations

5.

Sertel, Elif, et al.. (2024). Assessment of soil salinity using explainable machine learning methods and Landsat 8 images. International Journal of Applied Earth Observation and Geoinformation. 130. 103879–103879. 15 indexed citations

6.

Roscher, Ribana, et al.. (2024). A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards. 120(9). 8953–8982. 2 indexed citations

7.

Rußwurm, Marc, Sherrie Wang, Benjamin Kellenberger, Ribana Roscher, & Devis Tuia. (2024). Meta-learning to address diverse Earth observation problems across resolutions. Communications Earth & Environment. 5(1). 3 indexed citations

8.

Gawlikowski, Jakob, Mohsin Ali, Jong‐Seok Lee, et al.. (2023). A survey of uncertainty in deep neural networks. Artificial Intelligence Review. 56(S1). 1513–1589. 509 indexed citations breakdown →

9.

Garcke, Jochen & Ribana Roscher. (2023). Explainable Machine Learning. SHILAP Revista de lepidopterología. 5(1). 169–170. 6 indexed citations

10.

Leonhardt, Johannes, et al.. (2023). Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery. Frontiers in Artificial Intelligence. 6. 1278118–1278118. 3 indexed citations

11.

Betancourt, Clara, et al.. (2022). Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties. Geoscientific model development. 15(11). 4331–4354. 16 indexed citations

12.

Roscher, Ribana, et al.. (2022). Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision. arXiv (Cornell University). 23 indexed citations

13.

Kicherer, Anna, et al.. (2022). Detection of Anomalous Grapevine Berries Using Variational Autoencoders. Frontiers in Plant Science. 13. 729097–729097. 8 indexed citations

14.

Kicherer, Anna, et al.. (2022). Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks. Frontiers in Artificial Intelligence. 5. 830026–830026. 32 indexed citations

15.

Betancourt, Clara, et al.. (2021). AQ-Bench: a benchmark dataset for machine learning on global air quality metrics. Earth system science data. 13(6). 3013–3033. 17 indexed citations

16.

Betancourt, Clara, et al.. (2021). Global fine resolution mapping of ozone metrics through explainable machine learning. 1 indexed citations

17.

Drees, Lukas, Jürgen Kusche, & Ribana Roscher. (2020). MULTI-MODAL DEEP LEARNING WITH SENTINEL-3 OBSERVATIONS FOR THE DETECTION OF OCEANIC INTERNAL WAVES. SHILAP Revista de lepidopterología. V-2-2020. 813–820. 9 indexed citations

18.

Behley, Jens, et al.. (2019). Hyperspectral Plant Disease Forecasting Using Generative Adversarial Networks. 1793–1796. 34 indexed citations

19.

Roscher, Ribana, et al.. (2017). Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation. 63. 244–256. 16 indexed citations

20.

Roscher, Ribana & Björn Waske. (2014). Shapelet-based sparse image representation for landcover classification of hyperspectral data. 12. 1–6. 4 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.

Explore authors with similar magnitude of impact