会议通知 | 2026年中国地理学会春季年会暨中国地理编辑出版年会
“十五五”时期是基本实现社会主义现代化夯实基础、全面发力的关键时期。地理学作为支撑可持续发展的基础性学科，亟需以创新驱动为核心，勇担服务生态文明建设、区域高质量发展及全球治理的重要使命。 在此背景下，为促进地理学高质量发展及其服务能力可持续提升，奠定未来五年的发展基调，**2026年中国地理学会春季年会暨中国地理编辑出版年会**将于**2026年4月24–26日**在**重庆市**隆重举行。本次会议规模预计达**2000人**。 会议旨在充分发挥科技期刊在学科发展中的“方向引领”与“服务支撑”双重作用，搭建高水平学术交流平台。我们诚挚邀请全国地理学及相关领域的科研教学工作者、编辑出版人员及研究生积极参会，共同传播最新研究成果、探索科技前沿，推动地理学理论、技术与方法的创新，促进学术出版与科研创新的深度融合，为地理学在“十五五”开局之年的高质量发展拼出开局之势、干出开局之为。
MGIM: Masked Geo-Inference for Land Parcels
Effective modeling of spatio-temporal contexts to support geographic reasoning is essential for advancing Geospatial Artificial Intelligence. Inspired by masked language models, this paper introduces the Masked Geographical Information Model (MGIM), a novel self-supervised framework for learning context-aware representations from multi-source spatio-temporal data. The framework’s core innovations include a parcel-scale method for multi-source data fusion and a custom self-supervised masking strategy for diverse geographic elements. This integrated modeling approach enables the model to capture complex spatio-temporal relationships and achieve consistently strong performance across diverse geographic reasoning tasks, such as trajectory inference, people flow inference, event identification, and land parcel function analysis. MGIM accurately reasons from spatio-temporal contexts and dynamically adjusts inferences according to contextual changes. The visualization of attention mechanisms further illustrates MGIM’s capacity to construct contextually-aware representations and task-specific attention patterns analogous to natural language processing models. This study presents a new paradigm for general-purpose spatio-temporal modeling in real-world geographic scenarios, offering significant theoretical and practical value, and promising an effective solution for building a geographic foundation model.