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

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{$\mathbf{\text{G}}$}eography-knowledge \underline{$\mathbf{\text{E}}$}nhanced \underline{$\mathbf{\text{A}}$}nalog \underline{$\mathbf{\text{R}}$}ecognition ($\mathbf{\text{GEAR}}$) 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{$\mathbf{\text{M}}$}orphology-integrated \underline{$\mathbf{\text{S}}$}iamese \underline{$\mathbf{\text{G}}$}raph \underline{$\mathbf{\text{N}}$}etwork ($\mathbf{\text{MSG-Net}}$) 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.