This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter models incorporate temporal and location embeddings for enhanced performance across various geospatial tasks. Through extensive benchmarking with GEO-Bench, the 600M version outperforms the previous Prithvi-EO model by 8\% across a range of tasks. It also outperforms six other geospatial foundation models when benchmarked on remote sensing tasks from different domains and resolutions (i.e. from 0.1m to 15m). The results demonstrate the versatility of the model in both classical earth observation and high-resolution applications. Early involvement of end-users and subject matter experts (SMEs) are among the key factors that contributed to the project's success. In particular, SME involvement allowed for constant feedback on model and dataset design, as well as successful customization for diverse SME-led applications in disaster response, land use and crop mapping, and ecosystem dynamics monitoring. Prithvi-EO-2.0 is available on Hugging Face and IBM terratorch, with additional resources on GitHub. The project exemplifies the Trusted Open Science approach embraced by all involved organizations.

翻译：本技术报告介绍了Prithvi-EO-2.0，这是一个新的地理空间基础模型，相较于其前代Prithvi-EO-1.0有显著改进。该新模型包含3亿和6亿参数版本，在30米分辨率下，使用来自NASA Harmonized Landsat与Sentinel-2数据档案的420万全球时序样本进行训练，并融入了时序和位置嵌入，以提升在各种地理空间任务上的性能。通过使用GEO-Bench进行广泛基准测试，6亿参数版本在一系列任务上的表现优于之前的Prithvi-EO模型8%。在来自不同领域和分辨率（即从0.1米到15米）的遥感任务上进行基准测试时，它也优于其他六个地理空间基础模型。结果证明了该模型在经典地球观测和高分辨率应用中的多功能性。终端用户和领域专家的早期参与是促成项目成功的关键因素之一。特别是，领域专家的参与使得能够对模型和数据集设计提供持续反馈，并成功为灾害响应、土地利用与作物测绘以及生态系统动态监测等不同专家主导的应用进行了定制化。Prithvi-EO-2.0已在Hugging Face和IBM terratorch上提供，GitHub上还有额外资源。该项目体现了所有参与组织所秉持的“可信开放科学”理念。