{"ID":2834561,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01788","arxiv_id":"2512.01788","title":"Learned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks","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.","short_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 traditio...","url_abs":"https://arxiv.org/abs/2512.01788","url_pdf":"https://arxiv.org/pdf/2512.01788v1","authors":"[\"Christian Mollière\",\"Iker Cumplido\",\"Marco Zeulner\",\"Lukas Liesenhoff\",\"Matthias Schubert\",\"Julia Gottfriedsen\"]","published":"2025-12-01T15:27:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}