NASA 与 IBM 开源月球测绘工具
英文标题：🔶 NASA and IBM open source lunar mapping tools
NASA 与 IBM 发布了开源的 NASA-IBM Lunar Foundation Model，用于分析月球表面的多模态、多分辨率观测数据，现已在 Hugging Face 上提供下载。该模型整合了来自四项任务、九台仪器的 30 多个空间对齐数据层，旨在帮助研究人员发现月球冰沉积、分析不规则月海斑块并识别和分类陨石坑。两家公司称，这一模型可减少人工筛查地图和图像的工作，并为全球科研社区提供可继续构建的开放平台。IBM 表示，模型的硬件需求取决于具体任务，其微调实验大多使用 Nvidia A100 GPU 完成。
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中文
AI 与机器学习
NASA 与 IBM 开源月球测绘工具
它既能帮你为未来的邪恶巢穴挑选完美地点，也能帮助科学家发现有关地球卫星的新线索。
IBM 与 NASA 再次合作，发布了一款月球开源 AI 模型，可用于推动对地球天然卫星的新发现。
NASA-IBM 月球基础模型（NASA-IBM Lunar Foundation Model）使用两家机构研究人员整理的大规模月球观测数据集进行训练，目前已在 Hugging Face 上提供。
该模型被称为首个整合多种模态（数据格式）、不同观测角度及不同空间尺度观测结果的 AI 模型。这两家机构表示，科学家可借助该模型分析地理特征，而不必人工逐一筛查地图和图像，也无需依赖低分辨率机器学习模型。
具体而言，NASA 和 IBM 希望研究人员能够借此发现此前未识别的月球冰沉积，分析被称为“不规则月海斑块”（Irregular Mare Patches）的火山特征，以及识别和分类陨石坑。
月球冰意味着可能存在水和氧气，这些资源或可用于未来载人任务。它存在于永久阴影区，而这些区域属于最难观测的地带。NASA-IBM 模型结合多模态和多分辨率观测数据，以更准确地预测月球表面可能存在冰的位置。
除模型外，IBM 和 NASA 的科学家还利用来自四项任务中九台仪器的数据，编制了一个由 30 多个空间对齐图层构成的开源月球数据集。该数据集汇集了数以万计的图像和地图，展示月球表面的多种地球物理属性。
“NASA 花费数十年时间构建了极其丰富的月球科学记录，但收集数据只是工作的一部分，”这家航天机构的首席科学数据官 Kevin Murphy 表示。“我们还必须让科学家更容易探索和使用这些数据。”
IBM 欧洲研究总监 Juan Bernabe-Moreno 称：“NASA-IBM 月球基础模型为科学家提供了大规模探索月球的基础，将跨仪器观测结果连接起来，揭示孤立分析时难以发现的模式，并提供一个可供全球研究社区继续构建的开放平台。”
这并不是两家机构首次合作开展此类项目。2023 年，双方发布了 Prithvi，这是一款开源基础 AI 模型，可帮助科学家分析卫星图像。一年后，双方发布了一款旨在 准确预测天气模式的 AI 气候模型，扩展了 Prithvi 模型家族。
去年，双方又推出了一款名为 Surya 的 AI 模型，用于预测可能扰乱卫星和航天器的剧烈太阳耀斑活动。该模型同样属于 Prithvi 家族，月球基础模型亦是如此。
NASA 和 IBM 尚未正式披露这一最新发布模型的具体参数量或确切尺寸。由于该模型已开源并可下载，我们询问了使用它所需的计算资源。
“硬件需求将取决于应用场景、输入数据的大小和数量，以及用户是在运行预测，还是进一步训练模型。作为经验法则，我们大多数微调实验都是使用 Nvidia A100 GPU 进行的，”IBM 一位发言人告诉我们。
“较小规模的实验和推理工作负载或许可以在性能较低的硬件上运行，但具体要求会因任务而异。”
或许 Reg 的读者能够利用这一新基础模型取得一些发现？例如，寻找失控火箭级留下的陨石坑，或者搜寻小绿人的存在证据？®
English / 原文
AI AND ML
NASA and IBM open source lunar mapping tools
Can help you pick the perfect spot for your future evil lair, or boffins make discoveries about Earth's satellite
IBM and NASA have got together again and released an open source AI model of the Moon that could be used to make new discoveries about Earth’s natural satellite.
The NASA‑IBM Lunar Foundation Model has been trained on an extensive lunar observation dataset curated by researchers at the two organizations, and is available now on Hugging Face.
It is claimed as the first AI model to integrate observations captured in a range of modalities (data formats), and at different viewing angles and spatial scales. Instead of sifting through maps and images by hand or using low resolution machine learning models, scientists can use this to analyze geographic features, the pair say.
In particular, NASA and IBM hope researchers will be able to discover previously unidentified lunar ice deposits, analyze volcanic features called Irregular Mare Patches, and identify and classify craters.
Lunar ice indicates the presence of water and oxygen, which may be useful for future manned missions. It is found in permanently shadowed regions, which are among the most difficult areas to observe. The NASA-IBM model combines multimodal and multi-resolution observations to better predict where ice may be present on the lunar surface.
Alongside the model, IBM and NASA scientists compiled an open-source lunar dataset from over 30 spatially-aligned layers, using data from nine instruments across four missions. It combines tens of thousands of images and maps showing various geophysical properties of the lunar surface.
“NASA has spent decades building an extraordinary scientific record of the Moon, but collecting data is only part of the job,” said the space agency’s chief science data officer, Kevin Murphy. “We also have to make data easier for scientists to explore and use.”
“The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on,” claimed IBM director of research for Europe, Juan Bernabe-Moreno.
This isn’t the first such project the two organizations have worked on together. In 2023, the pair released Prithvi, an open-source foundation AI model to help scientists analyze satellite imagery. A year later, they released an AI climate model designed to accurately predict weather patterns, extending the Prithvi family of models.
Last year, it was an AI model named Surya, developed to predict the kind of violent solar flare-ups that might disrupt satellites and spacecraft. This was also part of the Prithvi family, as is the Lunar Foundation Model.
NASA and IBM have not officially disclosed a specific parameter count or exact model size for this latest release. As it is open-source and available to download, we asked what resources someone would need to use it.
“Hardware needs will depend on the application, the size and number of inputs, and whether they’re running predictions or further training the model. As a rule of thumb, most of our fine-tuning experiments were conducted using Nvidia A100 GPUs,” an IBM spokesperson told us.
“Smaller-scale experiments and inference workloads may be possible on more modest hardware, although the exact requirements will vary depending on the task.”
Perhaps Reg readers will be able to make some discoveries using the new foundation model? Finding the craters made by rogue rocket stages, for example, or looking for evidence of little green men?®