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Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning

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arxiv 2402.01444 v1 pith:YPBRIZFV submitted 2024-02-02 cs.LG cs.AIcs.CV

Esther Rolf , Konstantin Klemmer , Caleb Robinson , Hannah Kerner This is my paper · ORCID
classification cs.LGcs.AIcs.CV
keywords datalearningmachinesatelliteresearchsatmlchallengescritical
verification ladder T0 review T1 audit T2 compute T3 formal
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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 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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    TARDIS detects out-of-distribution satellite images by clustering a model's internal activations to create surrogate labels, then training a binary classifier on those labels.

  2. Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning Approach

    cs.LG 2025-01 conditional novelty 4.0 of 10

    Training multi-view EO models on all combinations of missing views with dynamic fusion improves robustness to moderate missingness, but does not consistently improve full-view accuracy.

  3. Sims: An Interactive Tool for Geospatial Matching and Clustering

    cs.CV 2024-12 conditional novelty 4.0 of 10

    Sims is an open-source tool that lets users explore geospatial layers by clustering and similarity search without coding, demonstrated on Rwandan maize yields.

  4. Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps

    cs.CV 2024-12 conditional novelty 4.0 of 10

    A teacher-student model trained on local Kenyan data produced land-use maps with higher F1 and IoU than three global maps in Murang'a County.

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