14 citations · 23 across the 11 of their papers we have counts for
11 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…
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…
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting
Zhongying Wang, Thoai D. Ngo, Hamidreza Zoraghein +2
The COVID-19 pandemic's severe impact highlighted the need for accurate and timely hospitalization forecasting to support effective healthcare planning. However, most forecasting m…
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.…
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…
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…
Partial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts
Behzad Vahedi, Benjamin Lucas, Farnoush Banaei-Kashani +4
Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, p…
Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation
Rafael Pires de Lima, Behzad Vahedi, Morteza Karimzadeh
Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters. Recently, Convolutional Neural Network (CNN) models show the potential to accelerate the generati…
Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights
Morteza Karimzadeh, Rafael Pires de Lima
Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes. One such case is sea ice, which is highly dynamic and rapidly changes a…
Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions
Rafael Pires de Lima, Behzad Vahedi, Nick Hughes +3
Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice…