5 citations · 5 across the 6 of their papers we have counts for
6 papers
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann +2
In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne Earth observation (EO) data for a…
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…
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…
TerraCodec: Compressing Optical Earth Observation Data
Julen Costa-Watanabe, Isabelle Wittmann, Benedikt Blumenstiel +1
Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression r…
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…