AI 模型和平台
Ai2 发布 OlmoEarth V1.3 与全球10M卫星嵌入
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2026年10月22日的Allen Institute for AI 发布 OlmoEarth v1.3，一个开放的卫星嵌入模型，辅以10米分辨率的预计算全行星年度嵌入，以及一个基于浏览器的OlmoEarth嵌入探测器，不需要登录。
释放包括奥尔莫地球v1.3 基础模型，预计算全球嵌入128-和64-维版本的"探索者"，以及一份技术报告，均在OlmoEarth v1和v1.2所使用的"奥尔莫Earth Artifact License"下。 此前，使用OlmoEarth意味着在OlmoEarth Studio的微调，这需要GPU访问和局部图像存储；而嵌入模型将卫星数据压缩为可随时使用的特性，可重复使用，跨越许多任务。
根据它的模型卡，OlmoEarth-v1 3-Base是ViT-Base的基础模型，拥有1.28亿个参数，用于遥感超过哨兵1号，哨兵2号以及Landsat图像或图像时间序列。 它还包括一个1,430万参数的Perceiver和一个10万参数的线性学生将768维的符号投射到128维；卡片说，这个模型，包括学生在内，产生了奥尔莫地球的全球年化嵌入。
模型如何嵌入
OlmoEarth v1.3读取了一年的Sentinel-1雷达和Sentinel-2和Landsat多光谱时间序列，为地球上每10米像素产生一个128维的矢量；前64维也作为较小的嵌入体单独工作。 一个视觉变压器读取卫星数据，一个注意池将输出符集合成每块土地一个矢量，一个线性层将矢量减少到128个数字。 技术报告描述了Matryoshka式蒸馏，其学生是在同一前传中与骨干联合训练的单线层，报告称使得蒸馏基本自由。
报告指出，以20米的编码器分辨率运行视变器和注意力池，输出10米，比以10米的分辨率运行变器便宜7.7倍：每扇窗口308对2,378GMAC. 训练使用与OlmoEarth v1.2 — Sentinel-1, Sentinel-2, Landsat-8观测相同的数据和配方，其地图目标包括WorldCover,WorldCreet,SRTM,OpenStreetMap等，亚当W优化器的批量尺寸为512，共667200步。
基准测试结果和 Planteur 数据集
Ai2报告在覆盖六大洲的27项评价中对照AlphaEarth基金会和Tesera v2评价v1.3嵌入；128维嵌入得分最高的是27分中的20分，64维版本领先于27分中的18分。 技术报告指出，128维嵌入法在8个AEF数据集的7个上都超过了基线(例外的是埃塞俄比亚，Tessera v2优于OlmoEarth v1.3)，在PASTIS作物类型的数据集上略高于Tessera v2,58.6至58.3 mIoU.
随着发行，Ai2 推出 PLANTEUR，是一种作物型的基准测试，用于热带耕作，由法国海外的标签建造：马提尼克，留尼汪，法属圭亚那，瓜德罗普和马约特。 当分类人员在另一岛接受训练时，它在一个岛上的一岛式设置性能，而Ai2 称的v1.3嵌入式最优一般化，以放置分类人员，这是从未见过的。 报告记录了128维嵌入该一岛外措施的24.9 mIoU,Tessera v2和AlphaEarth Foundations的19.1。
Ai2还形容v1.3是一种强大的微调模型，它与v1.2在研究和合作伙伴标签上的性能相匹配；报告给予v1.3在所有被评价的模型和任务中的平均微调等级为4.34，落后于OlmoEarth v1.2 Base在4.02，领先于OlmoEarth v1 Base在4.74.
培训费用和环境影响
Ai2将OlmoEarth v1.3描述为它最廉价的OlmoEarth模型进行训练，需要53%的v1.2和18%的v1计算时间，碳排放量分别占50%和22%. Ai2估计，复制 OlmoEarth v1.3的预训基地将花费大约2 720美元，租用H100时间进行最后运行。
报告环境影响表列出 OlmoEarth v1.3 基础预训时间为539 H100 GPU小时，161 kWh小时，0.07 tCO2eq，相比之下，v1.2 Base为1 012 GPU小时，0.14 tCO2eq为2 989 GPU小时，v1 Base为0.32 tCO2eq. 嵌入物本身的计算要大得多：报告估计，全球嵌入量的一年约为14 000个H100小时，约为培训前成本的26倍。
全球归档、探索者和许可证发放
探索者背后的档案以10米分辨率嵌入，每年大约150TB，覆盖陆地和邻近沿海水域。 发布，2025年，2024年，2023年是全球性的，而2017年到2022年覆盖了部分国家，覆盖范围在增加。 报告将2017年至2025年的完整数据集置于一个瓣形以上的井上，覆盖范围从南纬59.5度到北纬83.6度，不包括南极洲，并称Searchlight推断移除了发布嵌入物中的板块文物。
嵌入器作为Zarr商店在Source Corporation上托管，可直接从对象存储中读取任何Zarr客户端，不需要API密钥或瓷砖服务器。 开放出处表发布作为GeoParquet列出哨兵1号，哨兵2号以及Landsat的场景，每20公里的瓦片和年份后面；2025年，报告记录了约45万windows,2100万马赛克的条目，73万场景。
探索者使嵌入物成为行星的假彩图。 用户可以在几年之间翻转，拉起基础的Sentinel-2图像，点击一个位置以找到类似的地方，从少数标签点训练一个小分类器，并将结果导出为GeoTIFF，不需要账户。 Ai2的公告说明了秘鲁Madre de Dios地区非法金矿开采的方法：该帖子称拉潘帕周边的开采在2017年和2018年达到顶峰，秘鲁的Mercurio行动于2019年2月关闭了大部分金矿开采，2017–2020年和2020–2025年的嵌入比较显示了旧矿区的早期扩散和持续变化。 2026年8月，在检察官发现采矿进入坦博帕塔后，秘鲁派遣海军巡逻队返回后备部队的新增哨所。
对于需要特定时间窗口的工作，OlmoEarth Studio在用户选择的日期、方式和分辨率上运行相同的模型，以及一个教程显示如何在用户自己的计算上生成嵌入。 技术报告指出，OlmoEarth Artifact许可证限制用于军事、国防和采掘业应用；该型号的型号卡新增按照Ai2的负责任使用准则打算使用。
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AI Models & Platforms
Ai2 Releases OlmoEarth V1.3 With Global 10M Satellite Embeddings
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The Allen Institute for AI released OlmoEarth v1.3 on October 22, 2026, an open satellite embedding model accompanied by precomputed annual embeddings for the whole planet at 10-meter resolution and a browser-based OlmoEarth Embeddings Explorer that requires no sign-in.
The release includes the OlmoEarth v1.3 Base model, precomputed global embeddings in 128- and 64-dimensional versions, the Explorer, and a technical report, all under the OlmoEarth Artifact License used for OlmoEarth v1 and v1.2. Previously, using OlmoEarth meant fine-tuning in OlmoEarth Studio, which required GPU access and local imagery storage; embedding models instead compress satellite data into ready-to-use features reusable across many tasks.
According to its model card, OlmoEarth-v1_3-Base is a ViT-Base foundation model with 128 million parameters for remote sensing over Sentinel-1, Sentinel-2, and Landsat images or image time series. It also includes a 14.3-million-parameter Perceiver and a 0.1-million-parameter linear student projecting 768-dimensional tokens to 128 dimensions; the card states that this model, student included, generated OlmoEarth’s global annualized embeddings.
How the Model Produces Embeddings
OlmoEarth v1.3 reads a year of Sentinel-1 radar and Sentinel-2 and Landsat multispectral time series and produces one 128-dimensional vector for every 10-meter pixel on Earth; the first 64 dimensions also work on their own as a smaller embedding. A vision transformer reads the satellite data, an Attention Pooler pools the output tokens into one vector per patch of land, and a linear layer reduces that vector to 128 numbers. The technical report describes a Matryoshka-style distillation whose student is a single linear layer trained jointly with the backbone in the same forward pass, which the report says makes the distillation essentially free.
The report states that running the vision transformer and Attention Pooler at a 20-meter encoder resolution with 10-meter output is 7.7 times cheaper than running the transformer at 10-meter resolution: 308 versus 2,378 GMACs per window. Training used the same data and recipe as OlmoEarth v1.2 — Sentinel-1, Sentinel-2, and Landsat-8 observations with map targets such as WorldCover, WorldCereal, SRTM, and OpenStreetMap — with the AdamW optimizer at a batch size of 512 for 667,200 steps.
Benchmark Results and the PLANTEUR Dataset
Ai2 reports evaluating the v1.3 embeddings against AlphaEarth Foundations and Tessera v2 across 27 evaluations covering six continents; the 128-dimensional embeddings scored highest on 20 of 27, and the 64-dimensional version led on 18 of 27. The technical report states that the 128-dimensional embeddings outperformed both baselines on 7 of 8 AEF datasets (the exception was Ethiopia, where Tessera v2 outperformed OlmoEarth v1.3) and slightly outperformed Tessera v2 on the PASTIS crop-type dataset, 58.6 to 58.3 mIoU.
With the release, Ai2 introduced PLANTEUR, a crop-type benchmark for tropical farming built from labels in Overseas France: Martinique, Réunion, French Guiana, Guadeloupe, and Mayotte. Its leave-one-island-out setup tests performance on one island when classifiers are trained on the others, and Ai2 says the v1.3 embeddings generalize best to places a classifier has never seen. The report records 24.9 mIoU for the 128-dimensional embeddings on that leave-one-island-out measure, versus 19.1 for Tessera v2 and 15.8 for AlphaEarth Foundations.
Ai2 also describes v1.3 as a strong fine-tuning model that matches v1.2’s performance across research and partner labels; the report gives v1.3 a mean fine-tuning rank of 4.34 across all evaluated models and tasks, behind OlmoEarth v1.2 Base at 4.02 and ahead of OlmoEarth v1 Base at 4.74.
Training Cost and Environmental Impact
Ai2 describes OlmoEarth v1.3 as its cheapest OlmoEarth model to train, requiring 53% of the computing time of v1.2 and 18% of v1, with 50% and 22% of the carbon emissions, respectively. Ai2 estimates that reproducing pretraining of OlmoEarth v1.3 Base would cost roughly $2,720 in rented H100 time for the final run.
The report’s environmental impact table lists OlmoEarth v1.3 Base pretraining at 539 H100 GPU hours, 161 kWh, and 0.07 tCO2eq, compared with 1,012 GPU hours and 0.14 tCO2eq for v1.2 Base and 2,989 GPU hours and 0.32 tCO2eq for v1 Base. Computing the embeddings themselves is far larger: the report estimates one year of global embeddings at approximately 14,000 H100 hours, roughly 26 times the pretraining cost.
Global Archive, Explorer, and Licensing
The archive behind the Explorer holds embeddings at 10-meter resolution, roughly 150 TB per year, covering land and adjacent coastal waters. At launch, 2025, 2024, and 2023 are global, while 2017 through 2022 cover select countries, with more coverage in progress. The report puts the full 2017-to-2025 dataset at well over a petabyte, with coverage from 59.5 degrees south to 83.6 degrees north and excluding Antarctica, and states that Searchlight inference removed tiling artifacts in the released embeddings.
The embeddings are hosted on Source Cooperative as a Zarr store readable directly from object storage with any Zarr client, with no API key or tile server required. Open provenance tables released as GeoParquet list the Sentinel-1, Sentinel-2, and Landsat scenes behind each 20-kilometer tile and year; for 2025, the report records about 450,000 windows, 21 million mosaic entries, and 730,000 scenes.
The Explorer renders the embeddings as a false-color map of the planet. Users can flip between years, pull up the underlying Sentinel-2 imagery, click a location to find similar places, train a small classifier from a handful of labeled points, and export the result as a GeoTIFF, with no account required. Ai2’s announcement illustrates the approach with illegal gold mining in Peru’s Madre de Dios region: the post states that mining around La Pampa peaked in 2017 and 2018 and that Peru’s Operation Mercurio shut most of it down in February 2019, and that embedding comparisons between 2017–2020 and 2020–2025 show the earlier spread and continued change in the old mining zone. The post adds that Peru sent navy patrols back into the reserve in August 2026 after prosecutors found mining had crossed into Tambopata itself.
For work that needs a specific time window, OlmoEarth Studio runs the same model over user-chosen dates, modalities, and resolution, and a tutorial shows how to generate embeddings on a user’s own compute. The technical report states that the OlmoEarth Artifact License restricts use for military, defense-related, and extractive-industry applications; the model card adds that the model is intended for use in accordance with Ai2’s Responsible Use Guidelines.
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