61 citations · 62 across the 6 of their papers we have counts for
8 papers
From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk +1
High-resolution rainfall observations are crucial for weather forecasting, water management, and hazard mitigation. Traditional operational measurements are often biased and low-re…
Participatory AI: A Scandinavian Approach to Human-Centered AI
Niklas Elmqvist, Eve Hoggan, Hans-Jörg Schulz +45
AI's transformative impact on work, education, and everyday life makes it as much a political artifact as a technological one. Current AI models are opaque, centralized, and overly…
RainPro-8: An Efficient Deep Learning Model to Estimate Rainfall Probabilities Over 8 Hours
Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk +3
We present a deep learning model for high-resolution probabilistic precipitation forecasting over an 8-hour horizon in Europe, overcoming the limitations of radar-only deep learnin…
Multi-Source Temporal Attention Network for Precipitation Nowcasting
Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk +3
Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning mod…
RainAI -- Precipitation Nowcasting from Satellite Data
Rafael Pablos Sarabia, Joachim Nyborg, Morten Birk +1
This paper presents a solution to the Weather4Cast 2023 competition, where the goal is to forecast high-resolution precipitation with an 8-hour lead time using lower-resolution sat…
Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding
Joachim Nyborg, Charlotte Pelletier, Ira Assent
Large-scale crop type classification is a task at the core of remote sensing efforts with applications of both economic and ecological importance. Current state-of-the-art deep lea…
Weakly-Supervised Cloud Detection with Fixed-Point GANs
Joachim Nyborg, Ira Assent
The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-…
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
Joachim Nyborg, Charlotte Pelletier, Sébastien Lefèvre +1
The recent developments of deep learning models that capture complex temporal patterns of crop phenology have greatly advanced crop classification from Satellite Image Time Series…