TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis

• URL: http://arxiv.org/abs/2506.20380v1
• Date: Wed, 25 Jun 2025 12:46:26 GMT
• Title: TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
• Authors: Zhengpeng Feng, Sadiq Jaffer, Jovana Knezevic, Silja Sormunen, Robin Young, Madeline Lisaius, Markus Immitzer, James Ball, Clement Atzberger, David A. Coomes, Anil Madhavapeddy, Andrew Blake, Srinivasan Keshav,
• Abstract summary: We present TESSERA, a novel Remote Sensing Foundation Model (RSFM) that uses Self-Supervised Learning (SSL) to generate global, robust representations at 10m scale from pixel-level satellite time series data.<n>Our results show that TESSERA outperforms both traditional RS baselines and the leading geospatial foundation models in diverse downstream tasks.
• Score: 0.24199968850337347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Satellite remote sensing (RS) enables a wide array of downstream Earth observation (EO) applications, including climate modeling, carbon accounting, and strategies for conservation and sustainable land use. We present TESSERA, a novel Remote Sensing Foundation Model (RSFM) that uses Self-Supervised Learning (SSL) to generate global, robust representations at 10m scale from pixel-level satellite time series data. TESSERA combines information from only optical and SAR data streams using two parallel Transformer-based encoders: one dedicated to Sentinel-1 SAR polarizations and another to Sentinel-2 MSI data (10 selected spectral bands) to create representations that are then fused using a multilayer perceptron (MLP), resulting in a global representation map covering the years 2017 to 2024. Our precomputed representations set a new state-of-the-art performance benchmark and our open-source approach democratizes access to high-performance, high-resolution representations. We benchmark the performance of TESSERA in five diverse tasks, comparing our work with state-of-the-art task-specific models and other foundation models. Our results show that TESSERA outperforms both traditional RS baselines and the leading geospatial foundation models in these diverse downstream tasks.

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