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20232026
most citedEnhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

14 citations · 23 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024★ 3 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2023

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…