Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization

• URL: http://arxiv.org/abs/2510.20247v1
• Date: Thu, 23 Oct 2025 06:07:07 GMT
• Title: Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization
• Authors: Shuhan Hu, Yiru Li, Yuanyuan Li, Yingying Zhu,
• Abstract summary: Cross-view object geo-localization enables high-precision object localization through cross-view matching.<n>Existing methods rely on keypoint-based positional encoding, which captures only 2D coordinates while neglecting object shape information.<n>We propose a mask-based positional encoding scheme that leverages segmentation masks to capture both spatial coordinates and object silhouettes.<n>We present EDGeo, an end-to-end framework for robust cross-view object geo-localization.
• Score: 8.559240391514063
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view object geo-localization enables high-precision object localization through cross-view matching, with critical applications in autonomous driving, urban management, and disaster response. However, existing methods rely on keypoint-based positional encoding, which captures only 2D coordinates while neglecting object shape information, resulting in sensitivity to annotation shifts and limited cross-view matching capability. To address these limitations, we propose a mask-based positional encoding scheme that leverages segmentation masks to capture both spatial coordinates and object silhouettes, thereby upgrading the model from "location-aware" to "object-aware." Furthermore, to tackle the challenge of large-span objects (e.g., elongated buildings) in satellite imagery, we design a context enhancement module. This module employs horizontal and vertical strip convolutional kernels to extract long-range contextual features, enhancing feature discrimination among strip-like objects. Integrating MPE and CEM, we present EDGeo, an end-to-end framework for robust cross-view object geo-localization. Extensive experiments on two public datasets (CVOGL and VIGOR-Building) demonstrate that our method achieves state-of-the-art performance, with a 3.39% improvement in localization accuracy under challenging ground-to-satellite scenarios. This work provides a robust positional encoding paradigm and a contextual modeling framework for advancing cross-view geo-localization research.

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