Portrait de Ruben Cartuyvels

Ruben Cartuyvels

Alumni

Publications

Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko
Kristof Van Tricht
Gabriel Tseng
Giorgia Milli
David Rolnick
Ruben Cartuyvels
Inbal Becker Reshef
Zoltan Szantoi
Hannah Kerner
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover cl… (voir plus)assification, environmental monitoring, and change detection. Despite promising benchmark results, the deployment of these models in operational settings is challenging and rare. Standardized evaluation tasks often fail to capture real-world complexities relevant for end-user adoption such as data heterogeneity, resource constraints, and application-specific requirements. This paper presents a structured approach to integrate geospatial foundation models into operational mapping systems. Our protocol has three key steps: defining application requirements, adapting the model to domain-specific data and conducting rigorous empirical testing. Using the Presto model in a case study for crop mapping, we demonstrate that fine-tuning a pre-trained model significantly improves performance over conventional supervised methods. Our results highlight the model’s strong spatial and temporal generalization capabilities. Our protocol provides a replicable blueprint for practitioners and lays the groundwork for future research to operationalize foundation models in diverse remote sensing applications. Application of the protocol to the WorldCereal global crop-mapping system showcases the framework’s scalability.
2025-06-10
ICML.cc/2025/Workshop/TerraBytes (publié)
doi.org
openreview.net
Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng
Ruben Cartuyvels
Ivan Zvonkov
Mirali Purohit
David Rolnick
Hannah Kerner
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficu… (voir plus)lt or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.
2023-04-27
ArXiv (preprint)
doi.org
openreview.net
Explaining by Analogy: Case-based Abductive Natural Language Inference
Ruben Cartuyvels
Graham Spinks
Marie Francine
Peter Clark
Isaac Cowhey
Oren Etzioni
Tushar Khot
Rajarshi Das
Ameya Godbole
Shehzaad Dhuliawala
Manzil Zaheer
Andrew McCallum
Dung Ngoc Thai
Ameya
Ethan Godbole
Jay-Yoon Perez
Lee
Lizhen
Ramón López De Mántaras
David Mcsherry … (voir 37 de plus)
David Bridge
Barry Leake
Susan Smyth
Craw.
Boi
Maryalice Faltings
Michael T Maher
Ken-552 Cox
Dorottya Demszky
Kelvin Guu
Percy Liang
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Daniel Fried
Peter Jansen
Gus Hahn-Powell
Higher-575
Rebecca Emilie Sharp
M. Surdeanu
Zhengnan Xie
Sebastian Thiem
Jaycie Ryrholm Martin
Eliz-721 abeth Wainwright
Steven Marmorstein
Wenhan Xiong
Xiang Lorraine Li
Srini Iyer
Jingfei Du
Vikas Yadav
Steven Bethard
Zhilin Yang
Peng Qi
Saizheng Zhang
Yoshua Bengio
William W Cohen
Russ Salakhutdinov
Existing accounts of explanation emphasise 001 the role of prior experience and analogy in 002 the solution of new problems. However, most 0… (voir plus)03 of the contemporary models for multi-hop tex-004 tual inference construct explanations consider-005 ing each test case in isolation. This paradigm 006 is known to suffer from semantic drift, which 007 causes the construction of spurious explana-008 tions leading to wrong predictions. In con-009 trast, we propose an abductive framework for 010 multi-hop inference that adopts the retrieve - 011 reuse - revise paradigm largely studied in case-012 based reasoning . Specifically, we present 013 ETNA ( E xplana t io n by A nalogy), a novel 014 model that addresses unseen inference prob-015 lems by retrieving and adapting prior expla-016 nations from similar training examples. We 017 empirically evaluate the case-based abductive 018 framework on downstream commonsense and 019 scientific reasoning tasks. Our experiments 020 demonstrate that ETNA can be effectively in-021 tegrated with sparse and dense encoding mech-022 anisms or downstream transformers, achiev-023 ing strong performance when compared to ex-024 isting explainable approaches. Moreover, we 025 study the impact of the retrieve - reuse - revise 026 paradigm on explainability and semantic drift, 027 showing that it boosts the quality of the con-028 structed explanations, resulting in improved 029 downstream inference performance. 030