Space2Ground 2.0：结合街景与卫星影像的农业监测多源数据集及框架
Space2Ground 2.0: A Multi-Source Dataset and Framework for Agricultural Monitoring through Fusion of Street-Level and Satellite Imagery
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中文总结 AI 辅助
该研究提出Space2Ground 2.0多源框架，整合卫星与街景影像构建农业监测基准数据集，实验证实街景影像可提升作物分类效果，为多模态农业监测提供了可复现方法。
中文摘要 AI 辅助
精准且可扩展的地块级农业监测仍面临挑战，仅卫星对地观测仅能提供农业地块的俯瞰视角，而光学观测还受云导致的时间间隔影响。本文提出Space2Ground 2.0，这是一个整合Sentinel-1 SAR、Sentinel-2多光谱时间序列与车载相机采集并通过Mapillary平台共享的带地理标签街景影像的多源框架。一个高度自动化的处理流程执行语义过滤、影像质量评估、基于视角的地块关联及数据集优化，将大量众包影像转化为地块关联、可直接分析的数据。该流程在2022生长季的塞浦路斯应用后，从超90万张初始影像中生成了46050张标注街景影像的精选数据集，关联了8581个农业地块的卫星信息。通过使用单源及多源观测的地块级作物分类实验评估了该数据集的实用价值，结果显示街景影像提供了互补的精细尺度视觉信息，与卫星时间序列结合后可提升分类效果。总体而言，Space2Ground 2.0提供了一个公开可用的基准数据集及可复现的多模态农业监测方法，具备视觉验证、减少对昂贵实地检查的依赖及数据驱动农业政策实施等应用潜力。
英文摘要
Accurate and scalable parcel-level agricultural monitoring remains challenging because satellite Earth Observation alone provides only an overhead perspective of agricultural parcels, while optical observations are further affected by cloud-induced temporal gaps. This paper presents Space2Ground 2.0, a multi-source framework integrating Sentinel-1 SAR and Sentinel-2 multispectral time series with geo-tagged street-level imagery acquired using vehicle-mounted cameras and shared through the Mapillary platform. A largely automated processing pipeline performs semantic filtering, image quality assessment, viewpoint-based parcel association, and dataset refinement, transforming large volumes of crowdsourced imagery into parcel-linked, analysis-ready data. Applied over Cyprus during the 2022 growing season, the pipeline produced a curated dataset of 46,050 annotated street-level images, selected from an initial collection exceeding 900,000 images and linked with satellite information for 8,581 agricultural parcels. The practical value of the dataset was assessed through parcel-level crop classification experiments using both single- and multi-source observations. The results demonstrate that street-level imagery provides complementary fine-scale visual information that enhances classification when integrated with satellite time series. Overall, Space2Ground 2.0 provides an openly available benchmark dataset and a reproducible methodology for multimodal agricultural monitoring, with potential applications in visual verification, reduced reliance on costly field inspections, and data-driven agricultural policy implementation.
发表机构
- Operational Unit BEYOND Centre, IAASARS, National Observatory of Athens（雅典国家天文台IAASARS研究所BEYOND中心业务单元）
- DHI Water & Environment（DHI水环境公司）
- Artificial Intelligence Group, Wageningen University & Research（瓦赫宁根大学及研究中心人工智能组）
机构由 AI 辅助整理，请以论文原文为准。
