OSMGraphCLIP：从OpenStreetMap图学习全局位置表示
OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
AI总结：
提出OSMGraphCLIP模型，利用OpenStreetMap异构图结构学习全局位置嵌入，通过多尺度图编码器和对比学习对齐，在气候、生态、社会经济等下游任务中达到或超越卫星基线方法。
摘要：
我们提出了OSMGraphCLIP，一种CLIP风格的地理空间表示模型，从免费可用的OpenStreetMap（OSM）数据中学习全局位置嵌入。OSMGraphCLIP将地理环境表示为带类型的OSM特征的异构图，保留了道路、建筑物、土地利用区域和兴趣点之间的拓扑和语义关系。多尺度图编码器捕获细粒度的局部结构和更广泛的景观组成，并通过对比对齐目标监督球谐位置编码器。我们在涵盖气候、生态、社会经济指标、公共卫生、土地覆盖、生物多样性和野火预测等一系列下游地理空间回归和分类任务中评估了OSMGraphCLIP，并表明仅结构化OSM数据就支持跨领域的强全局位置表示。OSMGraphCLIP在大多数基准测试中达到或超过了基于卫星的基线，在社会经济和公共卫生任务中优势最为明显，因为OSM对建成环境的显式语义注释编码了卫星像素只能间接捕获的人类活动模式。在生态和环境任务中，尽管未使用地球观测数据，该模型仍与基于图像的方法保持紧密竞争。定性分析证实，学习到的嵌入连贯地组织了地理空间，仅从地图拓扑中恢复了生物群落边界、城市梯度和热带-温带区别。
英文摘要：
We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data. OSMGraphCLIP represents geographic environments as heterogeneous graphs of typed OSM features, preserving the topological and semantic relationships among roads, buildings, land-use regions, and points of interest. A multi-scale graph encoder captures both fine-grained local structure and broader landscape composition, and supervises a spherical-harmonics location encoder through a contrastive alignment objective. We evaluate OSMGraphCLIP across a diverse suite of downstream geospatial regression and classification tasks spanning climate, ecology, socioeconomic indicators, public health, land cover, biodiversity, and wildfire forecasting, and show that structured OSM data alone supports strong global location representations across domains. OSMGraphCLIP matches or exceeds satellite-based baselines on the majority of benchmarks, with the most pronounced advantage on socioeconomic and public-health tasks, where OSM's explicit semantic annotation of the built environment encodes patterns of human activity that satellite pixels can only capture indirectly. On ecological and environmental tasks, the model remains closely competitive with imagery-based methods despite using no Earth observation data. Qualitative analysis confirms that the learned embeddings organize geographic space coherently, recovering biome boundaries, urban gradients, and tropical--temperate distinctions from map topology alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
