我们开发了一种高分辨率深度学习框架，用于识别标准卫星遥感通常无法探测的细尺度生态要素（如树篱和林丛）。该框架生成的精确矢量数据为在保障粮食安全的前提下应对气候与生物多样性危机提供了新路径。森林不仅是树木的集合体，更是关键系统——其具备固碳、净水功能，并支撑着人类赖以生存的生物多样性。

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.

我们开发了一种高分辨率深度学习框架，用于识别标准卫星遥感通常无法探测的细尺度生态特征，例如树篱和林丛。这一精确的矢量数据为在耕地上应对气候与生物多样性危机提供了新路径，同时不损害粮食安全。森林远不止是树木的简单聚集；它们是关键系统，可固碳、净化水源，并支撑人类赖以生存的生物多样性。在全球努力缓解气候危机并遏制生物多样性丧失的背景下，扩大森林栖息地已成为一项全球优先事项。难点在于土地利用：随着人口增长，粮食需求持续上升，大规模造林不可避免地与满足该需求所必需的农业用地产生竞争。这种矛盾带来一个核心挑战：如何在不危及粮食安全或引发“泄漏”（即某一区域的保护行动无意中将环境退化转移至其他区域）的前提下，应对气候变化并遏制生物多样性丧失？穿插于农田之间的细尺度木本特征——如树篱与防风林——提供了一种潜在解决方案。它们可在不挤占作物种植面积的前提下提升碳储量与生物多样性，却常因尺寸过小而被国家级森林清查视为“不可见”。将高分辨率栅格地图转化为可用的矢量数据集，需跨越空间拓扑、语义解析与计算规模三方面的技术障碍。 首先，农业景观具有复杂的空间拓扑结构。各类要素极少孤立存在；例如，一条树篱可能毗邻农田，也可能紧贴石墙延伸，致使标准单层模型难以准确表达这些重叠要素。此外，处理如此大规模的地图需将其划分为S2-cell瓦片（一种将球形地球投影为平面方格的网格系统），但该操作常导致要素在瓦片边界处被人为截断。 其次，存在语义价值问题。“木本”像素本身无法区分森林核心区、连通廊道或孤立林丛。为使矢量化数据集切实服务于保护实践，我们必须找到一种方法，依据要素实际生态功能对其形状进行程序化分类。 最后，我们面临计算规模难题。高分辨率数据集体量庞大，常规栅格转矢量操作在计算上不可行。对英格兰全境（面积逾13万平方公里）数百万个独立木本要素进行处理，需精细的数据管理策略，以避免压垮传统系统。 为弥合像素信息与规划应用之间的鸿沟，我们构建了一套高分辨率深度学习框架，专为精准映射农业用地复杂斑块格局中的各类要素而设计。以此训练完成的模型为基础，我们构建了一条完整处理流程，以系统性解决上述空间、语义与规模三大核心挑战。 为应对乡村环境中多层拓扑结构（例如石墙正位于树篱冠层下方），我们基于亚米级影像与1米分辨率LiDAR数据，开发了双层标注系统。该系统使模型能在同一空间内识别两类信息：（1）地面边界（如耕地或水体）；（2）地表以上要素（如覆盖其上的树木与墙体）。 为修复瓦片边界处的人为截断问题，我们研发了一种可扩展算法，实现跨瓦片几何体自动融合，确保每个要素在几何层面完整无缺。 尽管矢量化数据集的发布已是重要进展，我们正持续推进数据优化工作。当前正探索高精度检测技术在多样化基于自然解决方案中的更广泛应用，例如支持农林复合系统（silvopasture）与农林业（agrisilviculture）中细尺度木本特征的量化评估。该技术亦可用于识别“泄漏”事件，确保项目边界内碳储量与生物多样性的局部提升，不会被边界外的损失所抵消。 此类方法为在耕地上规模化推进生态修复、协同应对气候与生物多样性危机，同时保障全球粮食安全，提供了关键路径。通过将该数据开放共享，我们期望赋能农民、科学家与政策制定者，共同守护那些虽微小却对地球产生深远影响的生态要素。 欲深入了解我们的AI与可持续发展工作，请访问Google Earth AI与Google Earth Engine。

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. 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. 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. 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.