Replication data for: Towards Multimodal Geospatial Reasoning: A Foundation Model Approach for Disaster Detection from Social Media, News, and Weather Data

This dataset provides a time-stamped H3 hexagonal grid for benchmarking multimodal disaster detection models across two events: (1) the September 2024 floods in Austria and Czechia, and (2) the January 2025 Southern California wildfires. It integrates satellite-derived disaster footprints from DLR and NASA to indicate the presence of an active flood or wildfire. In addition, it includes aggregated statistics from Bluesky social media posts, GDELT news headlines, and daily weather observations from Meteostat. All related data collection scripts for social media, news, and weather are available on GitHub. This dataset dataset can be used directly or adapted as a benchmark for multimodal disaster detection across diverse data sources. It can also be extended to incorporate or evaluate additional modalities. Data schema Each record represents one H3 cell on a specific date and includes the following columns. H3 grid and metadata event: Disaster event identifier date: Observation date (UTC) h3_index: H3 cell index h3_polygon: Polygon of the H3 cell h3_resolution: H3 resolution level h3_centroid: Cell centroid centroid_location: Human-readable location address event_type: Type of disaster (e.g., flood, wildfire) Satellite-derived disaster presence ground_truth_count: Number of hazard-affected blobs ground_truth_presence: Binary indicator of disaster presence Aggregated Bluesky statistics bsky_posts_count bsky_posts_disaster_related_count ratio_bsky_posts_disaster_related Aggregated GDELT statistics gdelt_articles_count gdelt_articles_disaster_related_count ratio_gdelt_articles_disaster_related Weather data (Meteostat) tavg: Average temperature tmin: Minimum temperature tmax: Maximum temperature prcp: Precipitation wspd: Wind speed本数据集提供带时间戳的H3六边形网格（H3 hexagonal grid），用于在两类灾害事件中基准测试多模态灾害检测模型：其一为2024年9月奥地利与捷克洪灾，其二为2025年1月南加州山火。数据集整合了德国宇航中心（DLR）与美国国家航空航天局（NASA）获取的卫星灾害足迹数据，用以标识活跃洪灾或山火的发生情况。此外，数据集还包含来自Bluesky社交平台帖子、GDELT新闻头条的聚合统计数据，以及Meteostat提供的每日气象观测数据。 所有针对社交媒体、新闻与气象数据的相关采集脚本均已上传至GitHub。本数据集可直接使用，或作为跨多源数据的多模态灾害检测基准进行适配，也可扩展以纳入或评估更多模态数据。 ### 数据模式（Data schema） 每条记录对应特定日期的一个H3网格单元，包含以下字段： #### H3网格及元数据 - event：灾害事件标识符 - date：观测日期（协调世界时UTC） - h3_index：H3网格单元索引 - h3_polygon：H3网格单元的多边形几何信息 - h3_resolution：H3网格分辨率等级 - h3_centroid：H3网格单元的质心坐标 - centroid_location：质心对应可读地理位置 - event_type：灾害类型（例如洪灾、山火） #### 卫星反演灾害存在信息 - ground_truth_count：受灾害影响斑块数量 - ground_truth_presence：灾害存在与否的二元标签 #### Bluesky平台聚合统计数据 - bsky_posts_count：Bluesky平台相关帖子总数 - bsky_posts_disaster_related_count：与灾害相关的Bluesky帖子数量 - ratio_bsky_posts_disaster_related：Bluesky平台灾害相关帖子占比 #### GDELT聚合统计数据 - gdelt_articles_count：GDELT平台相关新闻文章总数 - gdelt_articles_disaster_related_count：与灾害相关的GDELT新闻文章数量 - ratio_gdelt_articles_disaster_related：GDELT平台灾害相关文章占比 #### 气象数据（Meteostat） - tavg：平均气温 - tmin：最低气温 - tmax：最高气温 - prcp：降水量 - wspd：风速