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Arctic Field Summer Schools: training and awareness in the Arctic
(Conference object; Konferansebidrag, 2020-01)
A series of three summer schools engaged nearly fifty graduate students in exploring science questions related to current Arctic challenges, and brought together leading Arctic researchers from the partner institutions. Each partner organised a Field School and each had their own unique styles and emphasis. This collaboration aimed to deepen the Arctic knowledge of the young generation, and to create ...
Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation
(Conference object; Konferansebidrag, 2020-07-28)
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent variables to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs, we leverage ...
High spatial and temporal resolution L- and C-band Synthetic Aperture Radar data analysis from the yearlong MOSAiC expedition
(Conference object; Konferansebidrag, 2021-04)
In the yearlong MOSAIC expedition (2019-2020) R/V Polarstern drifted with sea ice through the Arctic Ocean, with the goal to continually monitor changes in the coupled ocean-ice-atmosphere system throughout the seasons. A substantial amount of synthetic aperture radar (SAR) satellite images overlapping the campaign was collected. Here, we investigate the change in polarimetric features over sea ice ...
Learning Nanoscale Motion Patterns of Vesicles in Living Cells
(Conference object; Konferansebidrag, 2020-08-05)
Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution (~250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We propose an integrative approach, built upon physics based simulations, nanoscopy algorithms, and shallow ...
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
(Conference object; Konferansebidrag, 2020-02-06)
Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems.
Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA).
However, due to the structural limitation of vanilla RNN which holds unit-length internal ...
Photonic-chip: a multimodal imaging tool for histopathology
(Conference object; Konferansebidrag, 2021-04)
We propose the photonic-chip as a multimodal imaging platform for histopathological assessment, allowing large fields-of-view across diverse microscopy methods including total internal reflection fluorescence and single-molecule localization.
Change Detection with Heterogeneous Remote Sensing Data: From Semi-Parametric Regression to Deep Learning
(Conference object; Konferansebidrag, 2020)
Heterogeneous Change Detection with Self-supervised Deep Canonically Correlated Autoencoders
(Conference object; Konferansebidrag, 2020)
Generation of Lidar-Predicted Forest Biomass Maps from Radar Backscatter with Conditional Generative Adversarial Networks
(Conference object; Konferansebidrag, 2020)
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
(Conference object; Konferansebidrag, 2020)
In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four areas with live forest and four areas with dead trees were identified. Quad-polarimetric SAR data from RADARSAT-2 was collected from the same area, and the complex multilook polarimetric ...