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

• URL: http://arxiv.org/abs/2512.01788v1
• Date: Mon, 01 Dec 2025 15:27:33 GMT
• Title: Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks
• Authors: Christian Mollière, Iker Cumplido, Marco Zeulner, Lukas Liesenhoff, Matthias Schubert, Julia Gottfriedsen,
• Abstract summary: We evaluate the potential of task-specific learned compression algorithms to reduce data volumes.<n> Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery.<n>Traditional codecs remain competitive on smaller, single-channel thermal infrared datasets.
• Score: 2.048356018298881
• License: http://creativecommons.org/licenses/by/4.0/
• 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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