用神经压缩技术高效处理海量地球观测数据，降低传输存储负担。
Lossy Neural Compression for Geospatial Analytics: A Review
- 基于深度学习的有损压缩方法，适配遥感与气候模型数据特性。
- 解决高分辨率卫星影像与气候模拟数据的海量传输与存储难题。
- 适合遥感、气象、地理信息等领域的研究人员和工程师参考。
过去几十年，地球观测（EO）数据量呈爆炸式增长。卫星影像对地表和大气的前所未有的覆盖范围，产生了需传输至地面站、存入数据中心并分发给终端用户的海量数据。现代地球系统模型（ESMs）也面临类似挑战，以高时空分辨率运行，每日生成数十拍字节（petabytes）的数据。近年来，神经压缩（NC）技术在深度学习与信息论基础上兴起，因其能有效处理大量无标签数据，使EO数据与ESM输出成为理想应用对象。本文综述了神经压缩在地理空间数据中的最新进展，介绍了其基本概念，涵盖图像与视频压缩领域的开创性工作，重点聚焦有损压缩。分析了EO与ESM数据的独特属性，与“自然图像”对比，阐明其带来的额外挑战与机遇。回顾了神经压缩在各类地球观测模态中的应用，并探讨了迄今有限的气候模型压缩研究。自监督学习（SSL）与基础模型（FM）的发展，推动了从大规模无标签数据中高效提取表示的方法。本文将这些进展与地理空间神经压缩相联系，揭示两者的相似性，并阐述压缩特征表示在机器间通信中的潜力。基于本综述的洞察，提出了面向未来地球观测与气候模拟应用的研究方向。
原文摘要 · Abstract (English)
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 satellite imagery has resulted in large volumes of data that must be transmitted to ground stations, stored in data centers, and distributed to end users. Modern Earth System Models (ESMs) face similar challenges, operating at high spatial and temporal resolutions, producing petabytes of data per simulated day. Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates due to their abundance of unlabeled data. In this review, we outline recent developments in NC applied to geospatial data. We introduce the fundamental concepts of NC including seminal works in its traditional applications to image and video compression domains with focus on lossy compression. We discuss the unique characteristics of EO and ESM data, contrasting them with "natural images", and explain the additional challenges and opportunities they present. Moreover, we review current applications of NC across various EO modalities and explore the limited efforts in ESM compression to date. The advent of self-supervised learning (SSL) and foundation models (FM) has advanced methods to efficiently distill representations from vast unlabeled data. We connect these developments to NC for EO, highlighting the similarities between the two fields and elaborate on the potential of transferring compressed feature representations for machine--to--machine communication. Based on insights drawn from this review, we devise future directions relevant to applications in EO and ESM.
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