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GEO-Bench: Toward Foundation Models for Earth Monitoring

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arxiv 2306.03831 v2 pith:B7L2N5AG submitted 2023-06-06 cs.LG cs.CV

classification cs.LGcs.CV
keywords modelstasksbenchmarkearthfoundationmonitoringprogressbeen
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation

    cs.CV 2026-03 conditional novelty 6.0 of 10

    CKA-based pre-fine-tuning layer pruning selects redundant ViT depth on unlabeled EO task data, cutting up to ~79% parameters while retaining most task performance and speeding both train and inference.

  2. The View From Space: Navigating Instrumentation Differences with EOFMs

    cs.CV 2025-10 conditional novelty 6.0 of 10

    EOFM embeddings are strongly partitioned by sensor architecture, so matching spectral bands is not enough to make cross-sensor embedding search reliable.

  3. GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A new 21-step geographic reasoning benchmark built from 1.46 million street-view images shows current multimodal LLMs handle simple visual questions well but rarely localize precisely.

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