19 citations · 46 across the 8 of their papers we have counts for
5 papers · 1 filter
OT on the Map: Quantifying Domain Shifts in Geographic Space
Haoran Zhang, Livia Betti, Konstantin Klemmer +2
In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts…
Localized, High-resolution Geographic Representations with Slepian Functions
Arjun Rao, Ruth Crasto, Tessa Ooms +3
Geographic data is fundamentally local. Disease outbreaks cluster in population centers, ecological patterns emerge along coastlines, and economic activity concentrates within coun…
Measuring the Intrinsic Dimension of Earth Representations
Arjun Rao, Marc Rußwurm, Konstantin Klemmer +1
Within the context of representation learning for Earth observation, geographic Implicit Neural Representations (INRs) embed low-dimensional location inputs (longitude, latitude) i…
Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning
Esther Rolf, Konstantin Klemmer, Caleb Robinson +1
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…
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
Marc Rußwurm, Konstantin Klemmer, Esther Rolf +2
Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology…