147 citations · 354 across the 17 of their papers we have counts for
17 papers
A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway
Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen +5
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains un…
Now We Know? A Systematic Comparison of TerraMind and THOR
Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling +5
Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, ho…
NOFE - Neural Operator Function Embedding
Lars Uebbing, Harald L. Joakimsen, Siyan Chen +6
Most dimensionality reduction methods treat data as discrete point clouds, ignoring the continuous domain structure inherent to many real-world processes. To bridge this gap, we in…
THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications
Theodor Forgaard, Jarle H. Reksten, Anders U. Waldeland +4
Current Earth observation foundation models are architecturally rigid, struggle with heterogeneous sensors and are constrained to fixed patch sizes. This limits their deployment in…
DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
Muhammad Sarmad, Arnt-Børre Salberg, Michael Kampffmeyer
This paper presents DiffFuSR, a modular pipeline for super-resolving all 12 spectral bands of Sentinel-2 Level-2A imagery to a unified ground sampling distance (GSD) of 2.5 meters.…
Uncertainties of Satellite-based Essential Climate Variables from Deep Learning
Junyang Gou, Arnt-Børre Salberg, Mostafa Kiani Shahvandi +9
Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the E…
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Multi-modality data is becoming readily available in remote sensing (RS) and can provide complementary information about the Earth's surface. Effective fusion of multi-modal inform…
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Capturing global contextual representations by exploiting long-range pixel-pixel dependencies has shown to improve semantic segmentation performance. However, how to do this effici…
The 1st Agriculture-Vision Challenge: Methods and Results
Mang Tik Chiu, Xingqian Xu, Kai Wang +39
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially f…
Self-Constructing Graph Convolutional Networks for Semantic Labeling
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we…
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
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-Co…
Dense Dilated Convolutions Merging Network for Land Cover Classification
Qinghui Liu, Michael Kampffmeyer, Robert Jessen +1
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotat…
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutio…
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and co…
Deep Divergence-Based Approach to Clustering
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +3
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminati…
Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen
Automatic urban land cover classification is a fundamental problem in remote sensing, e.g. for environmental monitoring. The problem is highly challenging, as classes generally hav…
Spectral Clustering using PCKID - A Probabilistic Cluster Kernel for Incomplete Data
Sigurd Løkse, Filippo Maria Bianchi, Arnt-Børre Salberg +1
In this paper, we propose PCKID, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of…