Google 时速 Deepmind用卫星数据和AI绘制出全球森林地图

Google 时速 Deepmind用卫星数据和AI绘制出全球森林地图

关键点

- Google Deepmind和世界资源研究所绘制了2020年土地覆盖全球卫星地图，将分辨率为10米的地区分为6个类别，包括原始森林、种植园和树木作物。

- 该系统利用AlphaEarth基金会和多个图像识别阶段，按照生长和收成周期区分森林类型，而不仅仅是外观。

- 地图总体准确度达到90.2%，并在同样的测试数据上超过了欧洲联盟委员会的地图，但只确定了大约一半的树木作物和人造林以及60%的种植园。

Google Deepmind和世界资源研究所开发了"ForTy"，这是一张AI地图，它区分了全世界原始森林，木材种植园和树木作物农场。

卫星地图通常显示树木是在特定区域生长而未识别出森林还是作物类型。 ForTy，简称"森林类型学"，更进一步，在2020年以10米分辨率将世界陆地面积划入6个分级。 这些森林包括原始森林、自然再生森林、人造林、种植林、油棕榈、橡胶或可可等树木作物和其他土地。 作者说"ForTy"是第一张全球同类地图。 早期的数据集，包括欧洲联盟委员会的数据集，只覆盖了世界的部分地区。

欧盟的毁林规则需要一个可靠的2020年参考点

该项目的主要驱动力是欧盟的"无森林产品条例"(EUDR)，该条例禁止2020年12月31日后被清理或退化的与森林相关的商品，包括原生森林被转化为种植园后出产的商品。 油棕榈和可可农场算作农业用地，因此评估供应链风险的公司需要知道一个场地在2020年是如何被使用的。

ForTy给了他们2020年的参考点，还可以为其他依赖于同一类数据的举措提供素材，包括热带永远森林基金和联合国到2030年保护世界30%土地的目标。

建立这个参考点很困难，因为森林的类型取决于它的历史，而不只是它看起来的样子，DeepMind 称。 尽管卫星图像显示地面裸露，但最近清理的种植园在定义上仍被视为森林。

ForTy使用三个模式阶段来完善其分类

为了区分这些类别，ForTy运行在Google DeepMind的阿尔法地基模型上，将多颗卫星的数据压缩成每个像素的紧凑描述。 一些图像识别模型以这些描述为基础，首先是从经核实的例子中吸取教训。

第一个模型的预测，加上外部关于树高、建筑物和森林损失的数据，成为第二个模型的扩大培训数据。 最后一步是分析数年的数据以平稳的输出。 Google的Gemini语言模型也帮助将更老的培训数据与欧盟的定义相适应。

在这些修正回合中，专家增加了约43 000个例子，以解决错误，例如欧洲被归类为树种的葡萄园、中美洲被标记为人造林的天然松林以及非洲和印度尼西亚漏掉的油棕榈等。

即使作了这些修改，对卫星来说，仍然无法作出一些区分。 卫星图像很少显示如何管理土地，因此研究人员使用40年的自转期将种植园与其他人造林分开，这种截取并不适合每个地区。

ForTy对种植园和树木作物的可靠性较低

为了检查地图，2名专家对8 190个随机选址的每个地点进行了审查，发现在90.2%的案件中是正确的。 作者警告说，这一总体得分从容易识别的"别地"类获得很大的提升。

不同森林类型的结果差异很大。 地图正确确定了89%的原始森林，专家在实地证实，但只发现了大约一半的树木作物和被种植的森林以及60%的种植园。 翻开镜头，大约四分之一到三分之一的被规划为种植园或树木作物的地区被错误地划分出来，而三分之一被标注为被种植森林的地区的地图是错误的。

这些差距冲击了欧盟发展成果评估最重要的界限，评价中的不确定性也可能使结果看起来略好一些。 专家往往对管理区不放心，小组将那些不确定的案件排除在评估之外。

根据同样的测试数据，ForTy仍然优于欧盟委员会专门为EUDR绘制的地图，在森林分类和非森林分类方面得分为95.5%，在森林类型方面得分为93.6%，在90.9%，在87.8%。

该模型还继承了其培训数据中的一个弱点，其注释者很少将道路或定居点附近的森林归类为原始森林，即使这些森林似乎没有受到影响。

瑞典树木多而天然林少

研究人员估计，世界有41亿公顷森林，包括近39亿公顷天然森林，其中约20亿公顷为原始森林。 被栽培的森林覆盖了约2.5亿公顷，而树木作物覆盖了近2亿公顷，大多在亚洲。

区域错误使情况复杂化。 该地图高估了亚洲的原始森林约2亿公顷，主要分布在俄罗斯的针叶林。 在非洲，它错失了大约9 400万公顷的森林，这可能是由于开阔的干燥森林和草原。

瑞典约三分之二的森林面积被种植，天然林只覆盖了全国的五分之一左右。 作者说，总的树木覆盖面积可能足够，但天然森林对生物多样性至关重要，其所占份额可能极低。 刚果民主共和国、印度尼西亚和巴西的天然森林份额最高。

ForTy仍然与被荫蔽的作物和开阔的林地相搏

除了这些区域错误之外，一些土地使用仍然难以分类。 在一些区域，地图仍然将天然树冠下种植的荫蔽可可或咖啡标为森林，有选择地砍伐或被烧毁的森林也造成了问题。

ForTy还没有为开阔的林地单独设置一个类别，EUDR对此进行了单独处理，尽管研究者表示未来的版本应该增加一个。 用户可以使用数据集对每个类的置信分数来作出自己的分类选择，这让用户可以设定自己的阈值。

地图，分级层和测试数据可自由使用CC BY 4.0许可，包括Google Earth Earth引擎。 Google DeepMind拥有GeeFlow和JEO寄存器中的代码发布。

Deepmind 推出 2025年7月基础的阿尔法地基模型是一类虚拟卫星。 它将光学卫星图像，雷达，激光扫描，和气候模拟结合成每个10相-10米区域的紧凑描述，每年的嵌入量覆盖自2017年以来的数年，可免费使用Google Earth Earth引擎。

ForTy是最早在这些嵌入物上建造的大型绘图项目之一，而Google 称是地球AI的一部分。 由于Google在2025年秋季将地球AI与Gemini连接起来，以帮助发现洪水、干旱和藻类开花速度快，因此这一连接将该项目置于更广泛的努力之中。

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Google Deepmind creates a global map of forests with satellite data and AI

Key Points

- Google Deepmind and the World Resources Institute created a global satellite map of 2020 land cover, classifying areas at 10-meter resolution into six categories, including primary forest, plantations, and tree crops.

- The system uses AlphaEarth Foundations and multiple image-recognition stages to distinguish forest types by growth and harvest cycles, not appearance alone.

- The map achieved 90.2% overall accuracy and outperformed European Commission maps on the same test data, but identified only about half of tree crops and planted forests and 60% of plantations.

Google Deepmind and the World Resources Institute have developed ForTy, an AI map that distinguishes primary forests, timber plantations, and tree crop farms worldwide.

Satellite maps usually show whether trees grow in a given area without identifying the forest or crop type. ForTy, short for Forest Typology, goes further by assigning the world's land area in 2020 to 6 classes at 10-meter resolution. These cover primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops such as oil palm, rubber, or cocoa, and other land. The authors say ForTy is the first global map of its kind. Earlier datasets, including one from the European Commission, cover only parts of the world.

EU deforestation rules need a reliable 2020 reference point

The main driver behind the project is the EU Regulation on Deforestation-free Products (EUDR), which prohibits goods linked to forests cleared or degraded after December 31, 2020, including goods produced after primary forest was converted to a plantation. Oil palm and cocoa farms count as agricultural land, so companies assessing supply chain risks need to know how a site was used in 2020.

ForTy gives them that 2020 reference point and could also feed into other initiatives that depend on the same kind of data, including the Tropical Forest Forever Facility (TFFF) and the UN goal of protecting 30 percent of the world's land by 2030.

Building that reference point is hard because a forest's type depends on its history, not just how it looks, DeepMind says. An old planted mixed forest can look like a natural one from space, and a recently cleared plantation still counts as forest by definition even though satellite images show bare ground.

ForTy uses three model stages to refine its classifications

To distinguish these categories, ForTy runs on Google DeepMind's AlphaEarth Foundations model, which compresses data from multiple satellites into a compact description of each pixel. Several image recognition models build on those descriptions, starting with one that learns from verified examples.

That first model's predictions, combined with outside data on tree heights, buildings, and forest loss, become expanded training data for a second model. A final step analyzes several years of data together to smooth outliers. Google's Gemini language model also helped adapt older training data to the EU's definitions.

During those correction rounds, experts added about 43,000 examples to address errors such as vineyards in Europe classified as tree crops, natural pine forests in Central America labeled as planted forests, and oil palms missed in Africa and Indonesia.

Even with those corrections, some distinctions remain out of reach for satellites alone. Satellite images rarely show how land is managed, so the researchers use a 40-year rotation period to separate plantations from other planted forests, and that cutoff does not fit every region.

ForTy is less reliable for plantations and tree crops

To check the map, 2 experts reviewed each of 8,190 randomly selected sites, finding it correct in 90.2 percent of cases. The authors warn that this overall score gets a large boost from the easy-to-identify "other land" class.

Results vary sharply by forest type. The map correctly identified 89 percent of primary forests that experts confirmed on the ground but found only about half of tree crops and planted forests and 60 percent of plantations. Flipping the lens, roughly a quarter to a third of areas mapped as plantations or tree crops turned out to be misclassified, and the map was wrong for a third of areas labeled as planted forest.

These gaps hit the boundaries that matter most for the EUDR, and uncertainty in the evaluation may also make the results look slightly better than they are. Experts were most often unsure about managed areas, and the team excluded those uncertain cases from its assessment.

ForTy still outperforms European Commission maps made specifically for the EUDR on the same test data, scoring 95.5 percent compared with 93.6 percent for forest versus nonforest classification, and 90.9 percent compared with 87.8 percent for forest types.

The model also inherited a weakness from its training data, whose annotators rarely classified forests near roads or settlements as primary forest, even when the forests appeared untouched.

Sweden has plenty of trees but little natural forest

The researchers estimate that the world has about 4.1 billion hectares of forest, including nearly 3.9 billion hectares of natural forest, of which about 2 billion hectares are primary forest. Planted forests cover about 250 million hectares, and tree crops cover nearly 200 million hectares, mostly in Asia.

Regional errors complicate that picture. The map overestimates primary forest in Asia by about 200 million hectares, largely in Russia's conifer forests. In Africa, it instead misses about 94 million hectares of forest, likely because of open dry forests and savannas.

About two-thirds of Sweden's forest area is planted, and natural forest covers only around one-fifth of the country. The authors say overall tree cover there may be adequate, but the share of natural forest, which matters for biodiversity, could be critically low. The Democratic Republic of the Congo, Indonesia, and Brazil have the highest shares of natural forest.

ForTy still struggles with shaded crops and open woodlands

Beyond these regional errors, some land uses remain difficult to classify. In some regions, the map still labels shaded cocoa or coffee grown beneath a natural canopy as forest, and selectively logged or burned forests also pose problems.

ForTy does not yet have a separate class for open woodlands, which the EUDR treats separately, though the researchers say future versions should add one. Users can make their own classification choices using the dataset's confidence scores for each class, which let them set their own thresholds.

The map, classification layer, and test data are freely available under a CC BY 4.0 license, including in Google Earth Engine. Google DeepMind has released the code in the GeeFlow and JEO repositories.

Deepmind introduced the underlying AlphaEarth Foundations model in July 2025 as a kind of virtual satellite. It combines optical satellite images, radar, laser scans, and climate simulations into a compact description of each 10-by-10-meter area, with annual embeddings covering years since 2017 freely available in Google Earth Engine.

ForTy is one of the first large mapping projects built on those embeddings and, Google says, is part of Earth AI. That connection places the project within a broader effort, as Google connected Earth AI to Gemini in fall 2025 to help detect floods, droughts, and algal blooms faster.

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