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arXiv · 2512.01788

Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks

Christian Mollière·Iker Cumplido·Marco Zeulner·Lukas Liesenhoff·Matthias Schubert·Julia Gottfriedsen
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Abstract

The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in this context to reduce data volumes while retaining crucial information. In detail, we compare traditional compression (JPEG 2000) versus a learned compression approach (Discretized Mixed Gaussian Likelihood) on three EO segmentation tasks: Fire, cloud, and building detection. Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery in both reconstruction quality (PSNR) and segmentation accuracy. However, traditional codecs remain competitive on smaller, single-channel thermal infrared datasets due to limited data and architectural constraints. Additionally, joint end-to-end optimization of compression and segmentation models does not improve performance over standalone optimization.

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Christian Mollière, Iker Cumplido, Marco Zeulner, Lukas Liesenhoff, Matthias Schubert, Julia Gottfriedsen. 2025-12-01. Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks. https://arxiv.org/abs/2512.01788

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