PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models (2412.04204v2)

Abstract: Geospatial Foundation Models (GFMs) have emerged as powerful tools for extracting representations from Earth observation data, but their evaluation remains inconsistent and narrow. Existing works often evaluate on suboptimal downstream datasets and tasks, that are often too easy or too narrow, limiting the usefulness of the evaluations to assess the real-world applicability of GFMs. Additionally, there is a distinct lack of diversity in current evaluation protocols, which fail to account for the multiplicity of image resolutions, sensor types, and temporalities, which further complicates the assessment of GFM performance. In particular, most existing benchmarks are geographically biased towards North America and Europe, questioning the global applicability of GFMs. To overcome these challenges, we introduce PANGAEA, a standardized evaluation protocol that covers a diverse set of datasets, tasks, resolutions, sensor modalities, and temporalities. It establishes a robust and widely applicable benchmark for GFMs. We evaluate the most popular GFMs openly available on this benchmark and analyze their performance across several domains. In particular, we compare these models to supervised baselines (e.g. UNet and vanilla ViT), and assess their effectiveness when faced with limited labeled data. Our findings highlight the limitations of GFMs, under different scenarios, showing that they do not consistently outperform supervised models. PANGAEA is designed to be highly extensible, allowing for the seamless inclusion of new datasets, models, and tasks in future research. By releasing the evaluation code and benchmark, we aim to enable other researchers to replicate our experiments and build upon our work, fostering a more principled evaluation protocol for large pre-trained geospatial models. The code is available at https://github.com/VMarsocci/pangaea-bench.

• The paper presents PANGAEA, a comprehensive benchmark designed to evaluate Geospatial Foundation Models (GFMs) across diverse application domains, sensor modalities, and geographical regions, including underrepresented areas.
• Benchmark evaluations reveal insights into current GFMs, showing the importance of resolution and spectral richness while highlighting challenges in multimodal data fusion and inconsistent outperformance of baselines in domain-specific tasks under label scarcity.
• PANGAEA offers theoretical implications by suggesting architectural needs for handling multi-temporal/multi-sensor data and practical value by providing a resource for practitioners and promoting a transparent ecosystem for GFM deployment.

Evaluation Strategies and Implications for Geospatial Foundation Models: A Critical Review of PANGAEA

PANGAEA arrives as a comprehensive evaluation benchmark designed to address gaps in assessing the effectiveness of Geospatial Foundation Models (GFMs). GFMs represent a salient advancement in processing vast and complex Earth observation datasets, offering a range of utilities from segmentation to change detection tasks. However, the absence of a standardized evaluation protocol has limited the ability to fully leverage these models, particularly in diverse real-world scenarios. This work presents a robust benchmark, demonstrating the crucial dimensions such as generalization, performance with limited labels, and domain-specific efficacy of GFMs.

Core Contributions of PANGAEA

PANGAEA brings forth a multifaceted evaluation framework that comprises multiple application domains, sensor modalities, and geographical diversity. It accentuates the need for extensive testing across various scenarios, offering a holistic view of models' performances. A vital aspect of this paper lies in curating a range of datasets characterized by spatial, spectral, and temporal diversification, which provides a challenging field for benchmarking GFMs effectively.

• Diversity in Evaluation: One of the primary criticisms in the existing evaluation setups for GFMs is their restricted scope and geographical bias. PANGAEA mitigates this by including datasets from underrepresented regions and multiple task domains, such as marine environments and complex urban tasks. This enriches our understanding of how well GFMs generalize across varied contexts, especially outside the commonly emphasized North American and European regions.
• Multi-Modal and Temporal Incorporations: The benchmark stresses integrating multi-modal data, which is pertinent given the rapid technological diversification in sensor capabilities. Moreover, PANGAEA emphasizes testing models on multi-temporal data, although challenges persist in fully capturing the temporal dynamics due to limitations in available model architectures.

Insights from Benchmark Evaluations

The results drawn from PANGAEA's extensive evaluations provide critical perspectives on current GFMs and their application abilities:

• Resolution and Spectral Richness: The investigations signify that pretrained models on high-resolution dataset configurations outperform on tasks requiring spatial specificity, aligning with findings where models like Scale-MAE excel in high-resolution tasks. In contrast, GFMs that exploit spectral richness show robust performance in domain-specific applications, reflecting adaptability in agriculture or marine datasets.
• Unimodal vs. Multimodal Performance: The research highlights the persistent challenges of efficiently leveraging multimodal data, a critical factor for progressing in GFMs' generalization capabilities. Trials with unimodal settings frequently surpassed multimodal ones, implying an architectural or methodological gap in current designs for sensor data fusion.
• Label Scarcity and GFM Potentials: The experiments under limited label scenarios reveal that GFMs possess inherent advantages over fully supervised models, particularly when pre-trained on large, diverse datasets, which parallels the pre-training benefits seen in other AI domains like NLP. However, the domain-specific pretrained models have yet to systematically outpace straightforward task-specific baselines like UNet in many settings.

Theoretical and Practical Implications

From a theoretical perspective, PANGAEA prompts a reevaluation of the foundation model paradigms for geospatial applications. It underscores the necessity for multi-temporal and multi-sensor integration at an architectural level, positing that future iterations of GFMs must better encapsulate the complex environmental dynamics captured in EO data.

Practically, PANGAEA serves as an invaluable resource for practitioners aiming to deploy GFMs across various sectors. Increased reproducibility facilitated by open-source benchmarking of models provides a significant step towards a transparent ecosystem, promoting wider acceptance and adaptation of these advanced models within the geospatial community.

Future Directions

The results and methodologies presented in the PANGAEA benchmark call for further research to enhance GFMs' architectural capabilities in handling multi-temporal and multimodal inputs. There is a pressing need for improvements in domain-specific adaptability, particularly leveraging transfer learning techniques to bridge modality and scale gaps between pre-training and application datasets.

In conclusion, by providing rigorous and diverse benchmarking protocols, PANGAEA sets a new standard for evaluating GFMs, providing critical insights into their current limitations and guiding future advancements in geospatial AI. The foundation laid by this paper is instrumental for subsequent research geared towards developing resilient and versatile geospatial technologies.