标题： 面向可持续发展目标的地理空间基础模型 显示英文标题

标题： Geospatial Foundation Models to Enable Progress on Sustainable Development Goals

摘要： 基础模型（FMs）是大规模的预训练人工智能系统，它们彻底改变了自然语言处理和计算机视觉，并正在推进地理空间分析和地球观测（EO）。它们承诺在任务间提供更好的泛化能力、可扩展性以及高效适应少量标注数据的能力。 然而，尽管地理空间基础模型迅速普及，但它们在现实世界中的实用性和与全球可持续发展目标的一致性仍未得到充分探索。 我们介绍了SustainFM，这是一个以17个可持续发展目标为基础的综合基准框架，涵盖了从资产财富预测到环境危害检测等极其多样化任务。 本研究对地理空间基础模型进行了严谨的跨学科评估，并提供了关于它们在实现可持续发展目标中作用的关键见解。 我们的研究结果表明：（1）虽然基础模型并非在所有情况下都优于传统方法，但在各种任务和数据集上，它们通常表现更优。（2）评估基础模型应超越准确性，包括可迁移性、泛化能力和能源效率作为其负责任使用的关键标准。（3）基础模型能够提供可扩展且基于SDG的解决方案，广泛适用于解决复杂的可持续发展挑战。 关键的是，我们提倡从以模型为中心的开发转向以影响为导向的部署，并强调诸如能源效率、对领域偏移的鲁棒性以及伦理考量等指标的重要性。 显示英文摘要

摘要： Foundation Models (FMs) are large-scale, pre-trained AI systems that have revolutionized natural language processing and computer vision, and are now advancing geospatial analysis and Earth Observation (EO). They promise improved generalization across tasks, scalability, and efficient adaptation with minimal labeled data. However, despite the rapid proliferation of geospatial FMs, their real-world utility and alignment with global sustainability goals remain underexplored. We introduce SustainFM, a comprehensive benchmarking framework grounded in the 17 Sustainable Development Goals with extremely diverse tasks ranging from asset wealth prediction to environmental hazard detection. This study provides a rigorous, interdisciplinary assessment of geospatial FMs and offers critical insights into their role in attaining sustainability goals. Our findings show: (1) While not universally superior, FMs often outperform traditional approaches across diverse tasks and datasets. (2) Evaluating FMs should go beyond accuracy to include transferability, generalization, and energy efficiency as key criteria for their responsible use. (3) FMs enable scalable, SDG-grounded solutions, offering broad utility for tackling complex sustainability challenges. Critically, we advocate for a paradigm shift from model-centric development to impact-driven deployment, and emphasize metrics such as energy efficiency, robustness to domain shifts, and ethical considerations.