19 citations · 46 across the 8 of their papers we have counts for
8 papers
Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
Steffen Knoblauch, Hao Li, Gengchen Mai +3
Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advances in self-supervised learni…
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…
EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images
Yijie Zheng, Weijie Wu, Zhuoyao He +5
While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these…
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…
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery
Konstantin Klemmer, Esther Rolf, Caleb Robinson +2
Geographic information is essential for modeling tasks in fields ranging from ecology to epidemiology. However, extracting relevant location characteristics for a given task can be…
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…