内容来源：https://research.google/blog/from-pixels-to-planning-earth-ai-for-nature-restoration/

内容总结：

谷歌发布高精度AI数据集，助力英国农业用地精细生态测绘

2026年6月16日，谷歌研究团队宣布取得一项重要突破：他们开发出一套高分辨率深度学习框架，能够精准识别农业用地上传统卫星监测难以发现的微小生态特征，如树篱、小树林和石墙，并将其转化为可直接应用于生态保护与碳核算的矢量数据集。

当前，全球面临气候变化与生物多样性丧失的双重危机，扩大森林栖息地成为国际共识。然而，人口增长带来的粮食需求与森林扩张之间存在用地矛盾。研究表明，散布在农田间的树篱、防风林等线性木本植被，可在不挤占耕地的前提下实现碳汇与生物多样性保护，但这些特征因尺寸过小，长期被国家森林资源调查所忽略。

为解决这一难题，谷歌与牛津大学利弗休姆自然恢复中心合作，此前已发布英国首张高分辨率“农田景观2020”栅格地图。本次发布的矢量数据集则更进一步，将像素化的图像转化为可量化的树篱、石墙和小树林清单，使土地所有者与保护者能够精确测量并扩展这些微小生态单元。

技术层面，研究团队克服了三大挑战：复杂空间拓扑（如树篱与石墙的重叠）、语义分类（区分森林核心、生态廊道与孤立树丛），以及海量数据处理。他们采用基于3亿张全球卫星图像预训练的遥感基础视觉Transformer模型，结合亚米级影像与1米分辨率激光雷达数据，构建了双层标注系统，分别识别地表边界和地上植被。同时，通过波尔斯比-波普尔紧密度评分算法，自动将检测到的植被分为直径30米以上的林地、小型树丛和线性木本植被（紧密度低于0.5），从而准确区分了具有生态连通功能的树篱与孤立树木。

在计算层面，团队利用谷歌地球引擎并行处理了覆盖英国全境超过13万平方公里的数百万个图块，将原本计算量极大的栅格转矢量操作变为可行。目前，该数据集已向公众开放。

谷歌表示，未来该项技术还将探索应用于林牧复合系统和农林复合系统，并帮助识别生态保护项目中的“泄漏”效应——即某地保护成果被邻近区域的环境退化所抵消。研究团队强调，通过使这些“隐形”的微小生态特征变得可见、可量化，将为在保障粮食安全的同时应对气候与生物多样性危机提供关键路径。

中文翻译：

2026年6月16日
米开朗基罗·康塞尔瓦（Michelangelo Conserva），谷歌研究院研究科学家；夏洛特·斯坦顿（Charlotte Stanton），谷歌研究院高级项目经理

我们开发了一套高分辨率深度学习框架，用于揭示标准卫星探测通常无法识别的精细生态特征，例如树篱和小树林。这种精确的矢量数据提供了一条新路径，可在不危及粮食安全的前提下，应对农业用地上的气候和生物多样性危机。

森林不仅仅是树木的集群，它们是关键的生态系统，能够固碳、过滤水源，并支撑人类赖以生存的生物多样性。随着全球努力缓解气候危机并遏制生物多样性丧失，扩大森林栖息地已成为一项全球优先事项。

困难在于土地利用。随着人口增长，粮食需求不断增加，而扩大大规模森林面积不可避免地会与满足这一需求所需的农业用地产生竞争。这种矛盾带来一个关键挑战：我们如何在解决气候变化和遏制生物多样性丧失的同时，不危及粮食安全或引发“泄漏效应”——即一个区域的保护行动无意中将环境退化转移到另一个区域？

农田中交织的精细木本特征，例如树篱和防护林带，提供了一种潜在的解决方案。它们可以在不挤占作物用地的情况下增强碳储存和生物多样性，但由于规模太小，标准卫星探测往往无法识别，因此常常被国家森林资源清查所“忽视”。

为使这些隐藏资产变得可见，我们此前与牛津大学莱弗休姆自然恢复中心合作发布了“农田景观2020”（Farmscapes 2020）：这是首张大规模、高分辨率地图，用于识别英格兰地区被忽视的特征，如树篱和线性林地。尽管最初的栅格（基于像素）格式是探测方面的一个进步，但景观恢复和碳核算的实际应用需要的不仅仅是像素。今天，我们发布了一个矢量化数据集，将这些地图转化为树篱、石墙和小树林的可操作清单。这一新资源使土地所有者和环保人士能够在整个英国范围内测量并扩展这些精细特征。

从高分辨率栅格地图转向可操作的矢量数据集，需要克服空间拓扑、语义和计算规模交叉领域的技术难题。

首先，农业景观呈现出复杂的空间拓扑。特征很少孤立存在；例如，一条树篱可能毗邻田地，或直接沿石墙延伸，这意味着标准的单层模型难以表现这些重叠元素。此外，处理如此大规模的地图需要将其分割成S2单元图块（一种将球形地球展平为地图上方形格网的坐标系），这常常导致特征在图块边界处被人为切断。

其次，是语义价值的问题。一个简单的“木本”像素无法区分森林核心区域、连通廊道或孤立小树林。为使矢量化数据集对保护工作有用，我们必须找到一种方法，根据实际生态功能对形状进行程序化分类。

最后，我们面临计算规模的问题。高分辨率数据集的庞大数据量使得标准的栅格转矢量操作在计算上难以承受。处理整个英格兰（面积超过13万平方公里）范围内数百万个独立的木本特征，需要谨慎的数据处理以避免压垮传统系统。

为弥合像素与规划之间的鸿沟，我们开发了一个高分辨率深度学习框架，旨在明确绘制农业用地复杂拼图中的特征。

训练人工智能识别英国乡村的特定特征，如经修剪的树篱，需要深厚的专业知识，但我们只有相对较少的标注数据（约247平方公里）。为克服这一困难，我们使用了基于超过3亿张全球卫星图像预训练的“遥感基础模型”（RSF）视觉变换器（ViT）主干网络。RSF是谷歌地球人工智能（Google Earth AI）的一部分，该模型和数据集集合旨在将行星数据转化为可操作的洞察。通过从这一扎实的空间纹理基础出发，我们对模型进行了微调，使其能够以更高精度识别英国景观的特定细节。

以这一训练好的模型为基础，我们设计了一个流程来解决核心的空间、语义和规模挑战。

为处理乡村的分层拓扑——例如石墙可能直接位于树篱冠层下方——我们开发了一个双层标注系统，使用亚米级影像和1米激光雷达数据。这使得我们的模型能够在同一空间看到两样东西：（1）地面边界（如农田或水体），以及（2）地上特征（如位于其上的树木和墙壁）。为修复图块边界处的人工切断问题，我们开发了一个可扩展算法，用于合并跨单元几何形状，确保每个特征在几何上完整。

随后我们解决了语义挑战。AI模型能够轻松检测到绿色植被，但自然无法区分一小片树丛和一条细长的树篱。为将模型生成的原始数字轮廓转化为有用的生态清单，我们应用了一种名为“波尔斯比-波珀紧凑度评分”（Polsby–Popper compactness score）的数学测试。通过分析每个检测结果的物理足迹，我们对乡村的几何形状进行了程序化分类。我们将林地定义为直径至少30米的连续冠层，木本斑块定义为小树丛或孤树，而线性木本特征——如树篱和狭长廊道——则根据其拉伸的足迹来界定，严格以小于0.5的紧凑度评分为标准。这种几何智能使我们能够程序化地分离出对野生动物迁移至关重要的狭长廊道。

最后，为解决计算瓶颈并将这一分析扩展到全国范围，我们利用了谷歌地球引擎（Google Earth Engine）。通过并行处理数千个独立的S2单元，我们绕过了传统的计算限制，能够同时为数百万个独立特征生成矢量几何。这些进步共同使我们能够将一张原始地图转化为一个实用的自然恢复工具。

尽管矢量化数据集的发布是向前迈出的重要一步，但我们已在进一步优化数据。

我们正在研究高精度探测在多种基于自然的解决方案中的更广泛用途，例如支持对混林牧业和农林复合系统中精细木本特征的量化。这项技术还有助于识别“泄漏”事件，确保碳和生物多样性的局部收益不会被项目边界之外的损失所抵消。这些方法为在农业用地上扩大恢复规模、应对气候和生物多样性危机而不危及全球粮食安全提供了一条关键路径。

通过开放和共享这些数据，我们希望赋能农民、科学家和政策制定者，保护那些能对地球产生大规模影响的小尺度特征。

欲了解更多关于我们人工智能和可持续发展工作的信息，请访问谷歌地球人工智能和谷歌地球引擎。

英文来源：

June 16, 2026
Michelangelo Conserva, Research Scientist, and Charlotte Stanton, Senior Program Manager, Google Research
We developed a high-resolution deep learning framework to reveal fine-scale ecological features, like hedgerows and copses, that are typically invisible to standard satellite detection. This precise vector data offers a new pathway to address the climate and biodiversity crises on working lands without compromising food security.
Forests are more than just clusters of trees; they are critical systems that sequester carbon, filter water, and support the biodiversity on which humanity depends. As the world strives to mitigate the climate crisis and halt biodiversity loss, increasing forest habitat is a global priority.
The difficulty lies in land use. With a growing population, the demand for food is increasing, and expanding large-scale forests inevitably competes with the agricultural land needed to meet that demand. This tension creates a key challenge: how do we address climate change and halt biodiversity loss without compromising food security or causing "leakage", where conservation in one area inadvertently shifts environmental degradation to another?
Fine-scale woody features, such as hedgerows and shelterbelts woven among our farms, offer a potential solution. They can enhance carbon storage and biodiversity without displacing crops, yet they are often “invisible” to national forest inventories because they are too small for standard satellite detection.
To make these hidden assets visible, we previously released Farmscapes 2020: the first large-scale, high-resolution map to identify overlooked features like hedgerows and linear woodlands across England, in collaboration with the Leverhulme Centre for Nature Recovery at the University of Oxford. While the initial raster (pixel-based) format was a step forward in detection, real-world applications for landscape restoration and carbon accounting require more than pixels. Today, we’re releasing a vectorized dataset that transforms these maps into an actionable inventory of hedgerows, stone walls, and copses. This new resource empowers landowners and conservationists to measure and expand these fine-scale features throughout the UK.
Moving from a high-resolution raster map to an actionable vector dataset required overcoming technical hurdles at the intersection of spatial topology, semantics, and computational scale.
First, agricultural landscapes present complex spatial topologies. Features are rarely isolated; for example, a hedgerow might border a field or run directly alongside a stone wall, meaning standard single-layer models struggle to represent these overlapping elements. Additionally, processing such a large map requires breaking it into S2-cell tiles (a grid system that flattens our round planet into flat squares on a map), which often results in features being artificially sliced at the tile borders.
Second, there is the question of semantic value. A simple "woody" pixel doesn't distinguish between a forest core, a connective corridor, or an isolated copse. To make the vectorized dataset useful for conservation, we had to find a way to programmatically classify these shapes based on their actual ecological function.
Finally, we faced the problem of computational scale. The sheer size of the high-resolution dataset made standard raster-to-vector operations computationally prohibitive. Processing millions of individual woody features across the entirety of England (an area of over 130,000 km²) required careful data handling to avoid overwhelming traditional systems.
To bridge the gap between pixels and planning, we developed a high-resolution deep-learning framework designed to explicitly map features across the complex patchwork of agricultural land.
Training an AI to recognize specific features of the British countryside like a managed hedgerow requires deep expertise, but we only had a relatively small set of annotated data (~247 km²). To overcome this, we used Remote Sensing Foundations’ (RSF) Vision-Transformer (ViT) Backbone pre-trained on more than 300 million global satellite images. RSF is part of Google Earth AI, our collection of geospatial models and datasets to transform planetary data into actionable insights. By starting with this robust foundation of spatial textures, we fine-tuned the model to recognize the specific nuances of the British landscape with much higher precision.
With this trained model as our foundation, we designed a pipeline to resolve our core spatial, semantic, and scaling challenges.
To handle the layered topology of the countryside, where a stone wall might sit directly beneath the canopy of a hedgerow, we developed a dual-layer labeling system using submeter imagery and 1-meter LiDAR data. This allowed our model to see two things in the same space: (1) the ground-level boundaries (like farmed land or water) and (2) the above-ground features (like the trees and walls that sit on top of them). To fix the artificial slices at tile borders, we developed a scalable algorithm that merges geometries across cells, ensuring every feature is geometrically complete.
We then addressed the semantic challenge. An AI model can easily detect greenery, but it doesn't naturally know the difference between a small cluster of trees and a long, thin hedgerow. To turn the model's raw digital outlines into a useful ecological inventory, we applied a mathematical test called the Polsby–Popper compactness score. By analyzing the physical footprint of each detection, we programmatically categorized the countryside's geometry. We defined woodlands as substantial, contiguous canopies with at least a 30-meter diameter, woody patches as small copses or individual trees, and linear woody features — such as hedgerows and elongated corridors — by their stretched footprints, strictly defined by a compactness score of less than 0.5. This geometric intelligence allowed us to programmatically isolate the long, thin corridors that are so vital for wildlife movement.
Finally, to address the computational bottleneck and scale this analysis nationwide, we leveraged Google Earth Engine. By processing thousands of independent S2 cells in parallel, we bypassed traditional computational limits, allowing us to generate vector geometries for millions of individual features simultaneously. Together, these advancements allow us to turn a raw map into a functional tool for nature recovery.
While the release of the vectorized dataset is an important step forward, we are already working to further refine the data.
We’re investigating the broader utility of high-precision detection for diverse nature-based solutions, such as supporting the quantification of fine-scale woody features in silvopasture and agrisilviculture systems. This technology could also help identify “leakage” events, ensuring that local gains in carbon and biodiversity are not offset by losses just beyond a project’s boundary. These approaches offer a critical pathway to scale restoration across working lands and address the climate and biodiversity crises without compromising global food security.
By making this data open and accessible, we hope to empower farmers, scientists, and policymakers to protect the small-scale features that make a large-scale difference for our planet.
Learn more about our AI and sustainability efforts by checking out Google Earth AI and Google Earth Engine.