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| Autori principali: | , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2604.07092 |
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| _version_ | 1866913018197049344 |
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| author | Madadikhaljan, Mojgan Prexl, Jonathan Wittmann, Isabelle Albrecht, Conrad M Schmitt, Michael |
| author_facet | Madadikhaljan, Mojgan Prexl, Jonathan Wittmann, Isabelle Albrecht, Conrad M Schmitt, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07092 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data Madadikhaljan, Mojgan Prexl, Jonathan Wittmann, Isabelle Albrecht, Conrad M Schmitt, Michael Computer Vision and Pattern Recognition 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. |
| title | Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.07092 |