GEAR: Geography-knowledge Enhanced Analog Recognition Framework in Extreme Environments

2026-03-19

AI Summary

This study addresses the challenges of geographic knowledge scarcity and computational inefficiency in cross-domain terrain similarity retrieval between the Mariana Trench and the Tibetan Plateau. To this end, the authors propose a three-stage efficient identification framework comprising morphological skeleton-based coarse screening, physics-aware waveform and texture filtering, and fine-grained matching via a graph neural network integrated with geomorphometric features. Innovatively combining geographic prior knowledge with deep learning, they develop MSG-Net—a siamese graph network with integrated geomorphological representations—and release the first expert-annotated terrain similarity dataset focused on tectonic collision zones. MSG-Net achieves a 1.38 percentage point improvement in F1 score over the best baseline, and its learned features exhibit significant correlation with biological distributions, offering a novel tool for interdisciplinary research.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The Mariana Trench and the Qinghai-Tibet Plateau exhibit significant similarities in geological origins and microbial metabolic functions. Given that deep-sea biological sampling faces prohibitive costs, recognizing structurally homologous terrestrial analogs of the Mariana Trench on the Qinghai-Tibet Plateau is of great significance. Yet, no existing model adequately addresses cross-domain topographic similarity retrieval, either neglecting geographical knowledge or sacrificing computational efficiency. To address these challenges, we present \underline{\textbf{G}}eography-knowledge \underline{\textbf{E}}nhanced \underline{\textbf{A}}nalog \underline{\textbf{R}}ecognition (\textbf{GEAR}) Framework, a three-stage pipeline designed to efficiently retrieve analogs from 2.5 million square kilometers of the Qinghai-Tibet Plateau: (1) Skeleton guided Screening and Clipping: Recognition of candidate valleys and initial screening based on size and linear morphological criteria. (2) Physics aware Filtering: The Topographic Waveform Comparator (TWC) and Morphological Texture Module (MTM) evaluate the waveform and texture and filter out inconsistent candidate valleys. (3) Graph based Fine Recognition: We design a \underline{\textbf{M}}orphology-integrated \underline{\textbf{S}}iamese \underline{\textbf{G}}raph \underline{\textbf{N}}etwork (\textbf{MSG-Net}) based on geomorphological metrics. Correspondingly, we release an expert-annotated topographic similarity dataset targeting tectonic collision zones. Experiments demonstrate the effectiveness of every stage. Besides, MSG-Net achieved an F1-Score 1.38 percentage points higher than the SOTA baseline. Using features extracted by MSG-Net, we discovered a significant correlation with biological data, providing evidence for future biological analysis.

Problem

• cross-domain topographic similarity retrieval
• geographical knowledge
• extreme environments
• terrestrial analogs
• computational efficiency

Innovation

• GEAR framework
• topographic similarity retrieval
• Morphology-integrated Siamese Graph Network
• geographical knowledge integration
• cross-domain analog recognition