7 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…
Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation
Samira Alkaee Taleghan, Morteza Karimzadeh, Andrew P. Barrett +2
Accurate segmentation and mapping of sea ice types is crucial for safe polar navigation, offshore operations, and climate monitoring. While deep learning has demonstrated strong po…
A Genealogy of Foundation Models in Remote Sensing
Kevin Lane, Morteza Karimzadeh
Foundation models have garnered increasing attention for representation learning in remote sensing. Many such foundation models adopt approaches that have demonstrated success in c…
Performance and Generalizability Impacts of Incorporating Location Encoders into Deep Learning for Dynamic PM2.5 Estimation
Morteza Karimzadeh, Zhongying Wang, James L. Crooks
Deep learning has shown strong performance in geospatial prediction tasks, but the role of geolocation information in improving accuracy and generalizability remains underexamined.…
Investigating the Effect of Spatial Context on Multi-Task Sea Ice Segmentation
Behzad Vahedi, Rafael Pires de Lima, Sepideh Jalayer +3
Capturing spatial context at multiple scales is crucial for deep learning-based sea ice segmentation. However, the optimal specification of spatial context based on observation res…