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

TL;DR

G-SEED is a hierarchical encoding framework that efficiently manages and queries large-scale, multimodal forest and grassland spatio-temporal data, improving integration, precision, and performance over existing methods.

Contribution

It introduces a unified, scalable encoding system based on GeoSOT for diverse forest and grassland data, enhancing data integration and query efficiency.

Findings

Outperforms Geohash and H3 in spatial precision and query speed.

Effectively encodes heterogeneous data including imagery, maps, and sensor records.

Demonstrates scalability and robustness on real-world dataset from China.

Abstract

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.