Marc Rußwurm

University of Bonn · Germany

Unclaimed · AI-structured profileORCID OpenAlex

Geospatial ML at University of Bonn: remote sensing for agriculture, foundation models, and location embeddings

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-17) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.

Research profile

Output—

Not yet scored: the snapshot provides yearly work counts from OpenAlex indexing, but lacks the complete per-publication records behind Evolve's output composite; this axis would measure annual research production volume and its consistency over time.

Impact—

Not yet scored: an h-index of 20 and i10-index of 28 appear in the snapshot, but per-paper citation records are insufficient for Evolve's impact composite; this axis would measure citation influence and uptake of the work.

Focus100/100

Focus scores 100. The profile maps to a single declared field (geospatial machine learning / remote sensing), and topic counts concentrate accordingly: Remote-Sensing Image Classification (54), Remote Sensing in Agriculture (31) and Smart Agriculture and AI (16) dominate, with applied side threads in Microplastics and Plastic Pollution (15) and Oil Spill Detection and Mitigation (14).

Collaboration51/100

The radar collaboration score is 51 (mid-range). The snapshot lists 12 distinct coauthors, and the recurring-coauthor list spans geospatial ML (Esther Rolf, Konstantin Klemmer, Gengchen Mai, Nathan Jacobs, Hannah Kerner, Isaac Corley) and AI-for-agriculture (Ioannis N. Athanasiadis). On both 2026 catalog papers he is a middle author and not corresponding, indicating team-based rather than solo-led work.

Activity100/100

Activity scores 100, the top of the scale. OpenAlex countsByYear rises from 1 indexed work in 2015 to 11 in 2023, 16 in 2025 and 21 in 2026 — the strongest years on record — and 47 works fall within the most recent three-year window, indicating a steep, ongoing acceleration in research production.

Reproducibility100/100

Reproducibility scores 100, with a reported reproducibility mean of 100 — but this rests on a single paper (reproPapers: 1). That paper, MIND the Gap, is rated high for reproducibility readiness, with CoordBench evaluation and spatial cross-validation; the perfect score should therefore be read as promising early evidence, not an established track record.

Research summary

Marc Rußwurm is a researcher at the University of Bonn (DE) working at the intersection of geospatial machine learning and remote sensing, with an applied emphasis on agriculture. His OpenAlex-indexed profile maps to a single declared field — geospatial machine learning / remote sensing — and topic counts are led by Remote-Sensing Image Classification (54), Remote Sensing in Agriculture (31), Smart Agriculture and AI (16), Microplastics and Plastic Pollution (15), and Oil Spill Detection and Mitigation (14). Output has risen sharply: from 1 indexed work in 2015 to 11 in 2023, 16 in 2025 and 21 in 2026, with 47 works in the most recent three-year window. The snapshot reports an h-index of 20 and an i10-index of 28, though Evolve's output and impact composites remain unscored pending fuller per-paper data. Two 2026 arXiv papers sit in the Evolve catalog: MIND the Gap (a geographic implicit neural representation distilling geospatial teachers into a Matryoshka coordinate INR, rated high for reproducibility readiness) and Foundation Models Meet Agriculture: Challenges Beyond Pretraining. On both he appears as a middle author rather than corresponding author, consistent with a team-oriented, collaboratively produced record across remote sensing, deep learning, and AI-for-agriculture.

Collaboration pattern

Rußwurm's collaboration score is 51 (mid-range), with 12 distinct coauthors in the snapshot. His recurring-coauthor list bridges two communities: geospatial ML and remote sensing (Isaac Corley, Esther Rolf, Konstantin Klemmer, Gengchen Mai, Nathan Jacobs, Hannah Kerner) and AI for agriculture (Ioannis N. Athanasiadis), plus applied-ML collaborators such as Arjun Rao, Evan Shelhamer, Nils Lehmann, Vishal Nedungadi and Xingguo Xiong. On both 2026 catalog papers he is a middle, non-corresponding author — a team-player pattern within larger consortia.

Research keywords

remote sensingdeep learninggeospatial machine learningremote-sensing image classificationagricultural remote sensingAI for agriculturefoundation modelsimplicit neural representationslocation embeddingsMatryoshka representation learningknowledge distillationspatial cross-validation

Highlights

MIND the Gap: adjustable-scale geographic implicit neural representations
This 2026 arXiv paper distills geospatial teacher models into a Matryoshka coordinate implicit neural representation with an adjustable spatial scale. Evolve's verdict calls the CoordBench evaluation solid while flagging US-heavy targets and teacher–task overlap for scrutiny; reproducibility readiness is rated high. Rußwurm is a middle author.
Foundation Models Meet Agriculture: Challenges Beyond Pretraining
A second 2026 arXiv paper, with Rußwurm as a middle author, examines what foundation models still lack when applied to agriculture beyond pretraining. It sits in the Evolve catalog without a review verdict, keywords, or readiness rating yet, so no assessment beyond its existence and authorship position is available.
A decade of compounding output in remote-sensing AI
Indexed works grow from 1 (2015) to 11 (2023), 16 (2025) and 21 (2026), with 47 works in the last three years; topic mass sits in remote-sensing image classification (54) and agricultural remote sensing (31), showing volume and specialization rising together.

Timeline

2015First indexed work appears in the OpenAlex countsByYear (1 work), marking the start of the visible publication record.
2019Early growth phase: 6 indexed works in the year, following 3 in 2017 and 4 in 2018.
2023Output accelerates to 11 indexed works, up from 3 in 2022 — the first double-digit year in the record.
202516 indexed works, continuing the steep climb and setting up the current peak.
2026Peak year so far with 21 indexed works; both Evolve-catalog papers appear — MIND the Gap and Foundation Models Meet Agriculture.

Papers in the Evolve catalog

2026-09-21
MIND the Gap: A Geographic Implicit Neural Representation with Adjustable Spatial Scale

Frequent co-authors

Isaac Corley×1 Arjun Rao×1 Esther Rolf×1 Konstantin Klemmer×1 Evan Shelhamer×1 Nils Lehmann×1 Gengchen Mai×1 Nathan Jacobs×1 Hannah Kerner×1 Vishal Nedungadi×1 Xingguo Xiong×1 Ioannis N. Athanasiadis×1

About this profile

This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.

Bibliometrics come from an OpenAlex snapshot (identity: openalex); h-index (20) and i10-index (28) are quoted as provided and not independently verified.
The reproducibility score of 100 is computed from only one reviewed paper (reproPapers: 1), so it is a thin signal rather than an established record.
The output and impact radar axes are unscored because the underlying per-paper data is insufficient in this snapshot.
Only two papers from the Evolve catalog underlie the reviewed-paper evidence; the profile is name-resolved from public bibliographic data tied to ORCID 0000-0001-6612-5744.