文献类型：期刊文献

英文题名：G-SEED: A Spatio-temporal Encoding Framework for Forest and Grassland Data Based on GeoSOT

作者：Ouyang, Xuan[1] Yu, Xinwen[1] Chen, Yan[1] Deng, Guang[1] Liu, Xuanxin[1]

第一作者：Ouyang, Xuan

机构：[1] Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250294992)

语种：英文

外文关键词：Big data - Computer software reusability - Data mining - Decision making - Encoding (symbols) - Forestry - Geographic information systems - Information management - Query processing - Remote sensing - Search engines - Signal encoding

摘要：In recent years, the rapid development of remote sensing, Unmanned Aerial Vehicles, and IoT technologies has led to an explosive growth in spatio-temporal forest and grassland data, which are increasingly multimodal, heterogeneous, and subject to continuous updates. However, existing Geographic Information Systems (GIS)-based systems struggle to integrate and manage of such large-scale and diverse data sources. To address these challenges, this paper proposes G-SEED (GeoSOT-based Scalable Encoding and Extraction for Forest and Grassland Spatio-temporal Data), a unified encoding and management framework based on the hierarchical GeoSOT (Geographical coordinate global Subdivision grid with One dimension integer on 2n tree) grid system. G-SEED integrates spatial, temporal, and type information into a composite code, enabling consistent encoding of both structured and unstructured data, including remote sensing imagery, vector maps, sensor records, documents, and multimedia content. The framework incorporates adaptive grid-level selection, center-cell-based indexing, and full-coverage grid arrays to optimize spatial querying and compression. Through extensive experiments on a real-world dataset from Shennongjia National Park (China), G-SEED demonstrates superior performance in spatial precision control, cross-source consistency, query efficiency, and compression compared to mainstream methods such as Geohash and H3. This study provides a scalable and reusable paradigm for the unified organization of forest and grassland big data, supporting dynamic monitoring and intelligent decision-making in these domains. Copyright ? 2025, The Authors. All rights reserved.