用位置坐标建模地球观测数据，无需原始影像即可完成下游任务。
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
- 仅凭时空坐标重建卫星图像，构建连续时空神经场。
- 预训练后微调性能媲美从零训练或使用现有基础模型。
- 适合希望免数据预处理、快速适配新任务的研究者。
本文提出LIANet（Location Is All You Need Network），一种基于坐标的神经表示方法，将多时相星载地球观测（EO）数据建模为连续的时空神经场。给定空间和时间坐标，LIANet可重建对应卫星影像。预训练完成后，该神经表示可适配多种下游任务，如语义分割或像素级回归，且无需访问原始卫星数据。该方法旨在作为地理空间基础模型（GFMs）的用户友好替代方案，消除数据获取与预处理开销，实现仅依赖标签的微调。我们在不同规模目标区域上展示了LIANet的预训练效果，并验证其在下游任务中的微调性能优于从零训练，与现有GFMs相当。代码与数据集已公开于https://github.com/mojganmadadi/LIANet/tree/v1.0.1。
原文摘要 · Abstract (English)
In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne Earth observation (EO) data for a given region of interest as a continuous spatiotemporal neural field. Given only spatial and temporal coordinates, LIANet reconstructs the corresponding satellite imagery. Once pretrained, this neural representation can be adapted to various EO downstream tasks, such as semantic segmentation or pixel-wise regression, importantly, without requiring access to the original satellite data. LIANet intends to serve as a user-friendly alternative to Geospatial Foundation Models (GFMs) by eliminating the overhead of data access and preprocessing for end-users and enabling fine-tuning solely based on labels. We demonstrate the pretraining of LIANet across target areas of varying sizes and show that fine-tuning it for downstream tasks achieves competitive performance compared to training from scratch or using established GFMs. The source code and datasets are publicly available at https://github.com/mojganmadadi/LIANet/tree/v1.0.1.
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