Ai2 发布 OlmoEarth v1.3 最先进 AI-已准备好的卫星嵌入整个行星
Ai2，艾伦人工智能研究所，今天的发布是一颗名为OlmoEarth v1.3的卫星地图嵌入模型，并预估了全行星在各种分辨率上使用基于浏览器的映射工具进行年度嵌入。
该模型将一年的卫星观测压缩成一个数字指纹，用于最小分辨率下每10乘-10米的地面，其中64平方米和128米也可用。 这种指纹记录了其他软件可用于区分作物、森林、地雷和其他类型土地覆盖的规律。
直到现在，使用OlmoEarth的主要方法是到OlmoEarth工作室的微调，为每项任务训练一个模型，并运行在卫星数据上以制作地图。 这就需要获得大量的图形处理单元资源，以及下载和存储图像的能力。
使用今天的发布，用户可以使用基于浏览器的地图制作工具OlmoEarth Embeddings Explorer. 这意味着任何人都可以把OlmoEarth变成真正的工作，从跟踪随时间推移的变化到寻找类似的地方 — — 不需要签名。
嵌入是一种从数百万卫星图像中汲取的模型。 奥尔莫地球从数据读取了一年的雷达图像，包括哨兵1号，哨兵2号和Landsat. Sentinel和Landsat是提供对地球的补充观测的卫星飞行任务：Sentinel-1测量雷达反射，而Sentinel-2和Landsat则以可见和红外波长捕获光。
称公司与前几代奥尔莫地球模型相比，1.3是其最廉价的训练模式。 它用了53%的最新版本(v1.2)的计算时间和18%的第一个版本(v1)的计算时间，其中50%和22%的碳排放量。 它也仍然是一个强大的微调模型，使得v1.2的性能对研究和伙伴实验室很有价值。
这些模型适合现实世界的地方
这些卫星模型有助于压缩跨时间和空间的大量数据，这对了解地球的变化是有用的。
在秘鲁的马德雷德迪奥斯地区，拉潘帕周围的非法黄金开采在2017年和2018年达到顶峰，在坦博帕塔国家保护区的缓冲区内清理了1200多公顷的森林。 Amazon保护小组的MAAP监测方案报告了这项活动。 2019年2月，秘鲁的Mercurio行动关闭了大部分。 我们怎么知道这是怎么回事？
内嵌给这一个明确的答案。 从2016年到2020年，南方采矿带内部明显向东推进：34平方公里的重大变化。 从2020年到2024年的显示的同一比较，该面积下降到9平方公里。 关闭是显而易见的。
同样，农业分析员可以绘制油棕榈种植地点的地图，确定哪些地区种植园扩张可能取代森林，以便进行更仔细的审查。 这些问题可以确定环境影响对整个世界的影响。
Ai2 称对于许多测试一个想法对抗奥尔莫地球的球队来说意味着微调跑步，这是一个按小时顺序昂贵，艰巨的冒险。 在探险者中，第一次通过需要几秒钟。
从少数标签分析一个区域不需要GPU. 当一个任务需要微调做实验时，嵌入可以帮助决定它是否值得计算时间和模型的不足之处。
图片：数字艺术家/皮克斯巴伊
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Ai2 releases OlmoEarth v1.3 state-of-the-art AI-ready satellite embeddings for the whole planet
Ai2, the Allen Institute for Artificial Intelligence, today released a satellite map embedding model called OlmoEarth v1.3 and precomputed annual embeddings for the whole planet at various resolutions with a browser-based map-making tool.
The model compresses a year of satellite observations into a numerical fingerprint for each 10-by-10-meter patch of ground at the smallest resolution, with 64-square-meter and 128-meter also available. That fingerprint captures patterns that other software can use to distinguish crops, forests, mines and other types of land cover.
Until now, the primary way to use OlmoEarth was to fine-tune in OlmoEarth Studio, train a model for each task and run it over satellite data to make a map. That requires access to vast graphics processing unit resources and the ability to download and store imagery.
With today’s launch, users can use OlmoEarth Embeddings Explorer, a browser-based map-making tool. That means anyone can turn OlmoEarth into real work, from tracking change over time to finding similar places – no sign-in required.
An embedding represents what a model learned from millions of satellite images. OlmoEarth reads a year of radar images from data, including Sentinel-1, Sentinel-2 and Landsat. Sentinel and Landsat are satellite missions that provide complementary views of Earth: Sentinel-1 measures radar reflections, while Sentinel-2 and Landsat capture light in visible and infrared wavelengths.
The company said that, compared with previous generations of the OlmoEarth model, 1.3 is its cheapest model to train. It took 53% of the computing time of the latest version (v1.2) and 18% of the first (v1), with 50% and 22% of the carbon emissions. It also remains a strong fine-tuning model, making v1.2’s performance valuable for research and partner labs.
Where these models fit into the real world
These satellite models help compress large swaths of data across time and space, which is useful for understanding how the Earth changes.
In Peru’s Madre de Dios region, illegal gold mining around La Pampa peaked in 2017 and 2018, clearing more than 1,200 hectares of forest in the buffer zone of the Tambopata National Reserve. The Amazon Conservation Team’s MAAP monitoring program reports this activity. In February 2019, Peru’s Operation Mercurio shut most of it down. How do we know that happened?
Embeddings give this a clear answer. Comparing pixels within the data across years, no model training required, looking across 2016 to 2020 and there’s a clear eastward push within the southern mining belt: 34 square kilometers of significant change. The same comparison from 2020 to 2024 shows that area falls to 9 square kilometers. The shutdown is visible.
Equivalently, an agricultural analyst could map where oil palm is being grown and identify areas for closer examination where plantation expansion may have replaced forest. These questions could determine where the environmental impact is affecting the world at large.
Ai2 said for many teams testing an idea against OlmoEarth has meant a fine-tuning run, which is an expensive, arduous venture on the order of hours. In the explorer, a first pass takes seconds.
Analysis of a region from a handful of labels requires no GPU. When a task calls for fine-tuning that experiment, the embeddings can help decide whether it deserves the compute time and where the model falls short.
Image: TheDigitalArtist/Pixabay
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