4 papers
A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders
Zhongying Wang, Kevin Lane, Levi Cai +2
Supervised learning with Earth observation inputs is often limited by the sparsity of high-quality labeled or in-situ measured data to use as training labels. With the abundance of…
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…
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…
Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery
Arjun Rao, Esther Rolf
A large variety of geospatial data layers is available around the world ranging from remotely-sensed raster data like satellite imagery, digital elevation models, predicted land co…