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A geometric framework that quantifies ambiguity of the input-target relation through the average kernel size, computed with data-dependent, i.e. model-independent, feasible sets derived from worst-case input and target noise is introduced.
Geospatial Foundation Models (GeoFMs) are most commonly ranked and selected by accuracy on standard benchmark conditions via averaged ranks. We show that this protocol is too narrow: the promised deployment in critical EO tasks requires further angles of analysis, mainly calibration, the agreement between a model's con...
Lunarfm is introduced, a multimodal foundation model that learns a general representation of the lunar surface from diverse orbital measurements, and demonstrates that this embedding space supports a diverse range of downstream applications, enabling efficient scientific investigation and resource-oriented analysis.
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