5 citations · 10 across the 18 of their papers we have counts for
19 papers
GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy +19
Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict…
How to Embed Matters: Evaluation of EO Embedding Design Choices
Luis Gilch, Isabelle Wittmann, Maximilian Nitsche +3
Earth observation (EO) missions produce petabytes of multispectral imagery, increasingly analyzed using large Geospatial Foundation Models (GeoFMs). Alongside end-to-end adaptation…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
Alban Puech, Matteo Mazzonelli, Celia Cintas +11
We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solve…
Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model for Microclimate Impact Prediction
Jannis Fleckenstein, David Kreismann, Tamara Rosemary Govindasamy +3
As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air t…
Quantizing Space and Time: Fusing Time Series and Images for Earth Observation
Gianfranco Basile, Johannes Jakubik, Benedikt Blumenstiel +2
We propose a task-agnostic framework for multimodal fusion of time series and single timestamp images, enabling cross-modal generation and robust downstream performance. Our approa…
NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation
Rikard Vinge, Isabelle Wittmann, Jannik Schneider +4
We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach b…
Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space
Ruben Gonzalez Avilés, Linus Scheibenreif, Nassim Ait Ali Braham +8
Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by l…
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci +8
Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public da…
TerraMind: Large-Scale Generative Multimodality for Earth Observation
Johannes Jakubik, Felix Yang, Benedikt Blumenstiel +13
We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale…
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation
Clive Tinashe Marimo, Benedikt Blumenstiel, Maximilian Nitsche +2
Vision-language models for Earth observation (EO) typically rely on the visual spectrum of data as the only model input, thus failing to leverage the rich spectral information avai…
Lossy Neural Compression for Geospatial Analytics: A Review
Carlos Gomes, Isabelle Wittmann, Damien Robert +24
Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satel…
Multispectral to Hyperspectral using Pretrained Foundational model
Ruben Gonzalez, Conrad M Albrecht, Nassim Ait Ali Braham +6
Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH4 and NO2. However, its application is constrain…
Accelerating Quasi-Static Time Series Simulations with Foundation Models
Alban Puech, François Mirallès, Jonas Weiss +5
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. Howev…
Optimal Power Grid Operations with Foundation Models
Alban Puech, Jonas Weiss, Thomas Brunschwiler +1
The energy transition, crucial for tackling the climate crisis, demands integrating numerous distributed, renewable energy sources into existing grids. Along with climate change an…
Foundation Models for the Electric Power Grid
Hendrik F. Hamann, Thomas Brunschwiler, Blazhe Gjorgiev +24
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets throug…
Neural Embedding Compression For Efficient Multi-Task Earth Observation Modelling
Carlos Gomes, Thomas Brunschwiler
As repositories of large scale data in earth observation (EO) have grown, so have transfer and storage costs for model training and inference, expending significant resources. We i…
Multi-Spectral Remote Sensing Image Retrieval Using Geospatial Foundation Models
Benedikt Blumenstiel, Viktoria Moor, Romeo Kienzler +1
Image retrieval enables an efficient search through vast amounts of satellite imagery and returns similar images to a query. Deep learning models can identify images across various…
AB2CD: AI for Building Climate Damage Classification and Detection
Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl +1
We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset,…