Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning

Esther Rolf, Konstantin Klemmer, Caleb Robinson, Hannah Kerner
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:42691-42706, 2024.

Abstract

Satellite data has the potential to inspire a seismic shift for machine learning—one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world impact, our field is at a crossroads. We can either continue applying ill-suited approaches, or we can initiate a new research agenda that centers around the unique characteristics and challenges of satellite data. This position paper argues that satellite data constitutes a distinct modality for machine learning research and that we must recognize it as such to advance the quality and impact of SatML research across theory, methods, and deployment. We outline research directions, critical discussion questions and actionable suggestions to transform SatML from merely an intriguing application area to a dedicated research discipline that helps move the needle on big challenges for machine learning and society.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-rolf24a, title = {Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning}, author = {Rolf, Esther and Klemmer, Konstantin and Robinson, Caleb and Kerner, Hannah}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {42691--42706}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/rolf24a/rolf24a.pdf}, url = {https://proceedings.mlr.press/v235/rolf24a.html}, abstract = {Satellite data has the potential to inspire a seismic shift for machine learning—one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world impact, our field is at a crossroads. We can either continue applying ill-suited approaches, or we can initiate a new research agenda that centers around the unique characteristics and challenges of satellite data. This position paper argues that satellite data constitutes a distinct modality for machine learning research and that we must recognize it as such to advance the quality and impact of SatML research across theory, methods, and deployment. We outline research directions, critical discussion questions and actionable suggestions to transform SatML from merely an intriguing application area to a dedicated research discipline that helps move the needle on big challenges for machine learning and society.} }
Endnote
%0 Conference Paper %T Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning %A Esther Rolf %A Konstantin Klemmer %A Caleb Robinson %A Hannah Kerner %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-rolf24a %I PMLR %P 42691--42706 %U https://proceedings.mlr.press/v235/rolf24a.html %V 235 %X Satellite data has the potential to inspire a seismic shift for machine learning—one in which we rethink existing practices designed for traditional data modalities. As machine learning for satellite data (SatML) gains traction for its real-world impact, our field is at a crossroads. We can either continue applying ill-suited approaches, or we can initiate a new research agenda that centers around the unique characteristics and challenges of satellite data. This position paper argues that satellite data constitutes a distinct modality for machine learning research and that we must recognize it as such to advance the quality and impact of SatML research across theory, methods, and deployment. We outline research directions, critical discussion questions and actionable suggestions to transform SatML from merely an intriguing application area to a dedicated research discipline that helps move the needle on big challenges for machine learning and society.
APA
Rolf, E., Klemmer, K., Robinson, C. & Kerner, H.. (2024). Position: Mission Critical – Satellite Data is a Distinct Modality in Machine Learning. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:42691-42706 Available from https://proceedings.mlr.press/v235/rolf24a.html.

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