19 citations · 46 across the 8 of their papers we have counts for
8 papers
SLED: Scalable Location Encoding via Distillation
Kevin Lane, Zhongying Wang, Esther Rolf +1
The plethora of readily available geospatial data offers exciting opportunities to learn high quality representations of the planet, but the sheer size of the Earth Observations (E…
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…
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…