19 citations · 46 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…