Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression remains fragmented, lacking publicly available pretrained models and misaligned with advances in compression for natural imagery. Image codecs overlook temporal redundancy, while video codecs rely on motion priors that fail to capture the radiometric evolution of largely static scenes. We introduce TerraCodec (TEC), a family of learned codecs tailored to EO. TEC includes efficient image-based variants adapted to multispectral inputs, as well as a Temporal Transformer model (TEC-TT) that leverages dependencies across time. To overcome the fixed-rate setting of today's neural codecs, we present Latent Repacking, a novel method for training flexible-rate transformer models that operate on varying rate-distortion settings. Trained on Sentinel-2 data, TerraCodec outperforms classical codecs, achieving 3-10x stronger compression at equivalent image quality. Beyond compression, TEC-TT enables zero-shot cloud inpainting, surpassing state-of-the-art methods on the AllClear benchmark. Our results establish bespoke, learned compression algorithms as a promising direction for Earth observation. Code and model weights will be released under a permissive license.

翻译：地球观测（EO）卫星产生海量的多光谱图像时间序列数据流，对存储和传输提出了紧迫挑战。然而，基于学习的EO压缩研究仍处于碎片化状态，缺乏公开可用的预训练模型，且与自然图像压缩领域的最新进展脱节。现有图像编解码器忽视了时间冗余性，而视频编解码器依赖的运动先验则难以捕捉基本静态场景的辐射度演化规律。本文提出TerraCodec（TEC）——一个专为EO数据设计的系列化学习型编解码器。TEC包含适用于多光谱输入的高效图像变体，以及利用时间依赖性的时序Transformer模型（TEC-TT）。为突破当前神经编解码器的固定码率限制，我们提出潜在重封装技术，这是一种训练灵活码率Transformer模型的新方法，可在不同率失真设置下运行。基于Sentinel-2数据训练的TerraCodec性能超越传统编解码器，在同等图像质量下实现3-10倍的压缩效能提升。除压缩任务外，TEC-TT还具备零样本云层修复能力，在AllClear基准测试中超越现有最优方法。我们的研究结果表明，定制化的学习型压缩算法是地球观测领域极具前景的发展方向。代码与模型权重将通过宽松许可协议公开发布。