RISE DL Research Group

RISE DL Research Group

Foundational and applied machine learning for the Earth

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CCAI Nordics is live!

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Climate Change AI Nordics is a network for researchers in the Nordics working on problems related to tackling climate change using AI and machine learning. We at the DL-group have now offially initiated this network and the website is online!

Two FORMAS grants obtained

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Two FORMAS grants obtained: (i) Grey to green: Using AI to detect and prioritize conversion of impervious surfaces to multifunctional nature-based solutions and (ii) PRACTICAL WISION – Automatically identifying and mapping different weed species through the practical application of AI-based image analysis models. The DL-group will lead a work package each within the respective projects.

ML models in space

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ML models for cloud optical thickness estimation developed my members of the DL group (available here) have been running on-device in satellites orbiting in space. Read more about it here.

Funding of project on ML-based wind farm planning obtained from the Swedish Energy Agency

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We are happy to announce that we have received funding from the Swedish Energy Agency for the project AI-based Power Production Models for Increased Wind Farm Efficiency. Project summary: Wind energy is a promising source of power but is not easy to utilize effectively. Wind farms consist of many turbines that have complex interactions with each other and their surroundings. Factors such as terrain, wind trail effect (wake) between turbines, and ice accumulation on the blades influence the amount of power generated. Predicting the power output of wind farms typically relies on time-consuming simulations, but an emerging paradigm based on AI can drastically speed up prediction methods while maintaining their reliability. In this project we will develop new methods that use AI trained on real-world data to get accurate prediction of wind farm power output at a low computational cost. As the turbines and their relationships can be seen as a graph, we will use Graph Neural Networks (GNNs) to model them. Our method can have a big impact as the number of wind farms keeps growing, improving their efficiency and planning, and enabling more sustainable and affordable energy.

Visipedia workshop @ Pioneer Centre for AI

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The DL group will be represented at the Visipedia workshop hosted at the Pioneer Centre for AI in Copenhagen (to be held on April 12, 2024). The Visipedia project is jointly led by Serge Belongie’s group (University of Copenhagen) and Pietro Perona’s group (Caltech). Visipedia’s goal, broadly speaking, is to make computer vision systems that can be queried and used by large communities of experts to help foster the curation and generation of new knowledge.

Four new master theses initiated

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We wecolme eight new master thesis workers to the DL group during the spring of 2024. The four associated master theses will revolve around diverse topics: ML for detecting coffee berry disease, data-efficient ML for EO, active learning for soundscape analysis, and distributed ML.

Agrifood TEF

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portfolio

publications

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.