MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding

• URL: http://arxiv.org/abs/2512.17492v1
• Date: Fri, 19 Dec 2025 12:03:05 GMT
• Title: MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
• Authors: Oskar Kristoffersen, Alba R. Sánchez, Morten R. Hannemose, Anders B. Dahl, Dim P. Papadopoulos,
• Abstract summary: We introduce the Multi-Modal Landmark dataset (MMLANDMARKS), a benchmark composed of four modalities: 197k highresolution aerial images, 329k ground-view images, textual information, and geographic coordinates for 18,557 distinct landmarks in the United States.<n>The MMLANDMARKS dataset has a one-to-one correspondence across every modality, which enables training and benchmarking models for various geo-spatial tasks.
• Score: 4.493333639603517
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textual descriptions, and geographic coordinates). Current geo-spatial benchmarks have limited coverage across modalities, considerably restricting progress in the field, as current approaches cannot integrate all relevant modalities within a unified framework. We introduce the Multi-Modal Landmark dataset (MMLANDMARKS), a benchmark composed of four modalities: 197k highresolution aerial images, 329k ground-view images, textual information, and geographic coordinates for 18,557 distinct landmarks in the United States. The MMLANDMARKS dataset has a one-to-one correspondence across every modality, which enables training and benchmarking models for various geo-spatial tasks, including cross-view Ground-to-Satellite retrieval, ground and satellite geolocalization, Text-to-Image, and Text-to-GPS retrieval. We demonstrate broad generalization and competitive performance against off-the-shelf foundational models and specialized state-of-the-art models across different tasks by employing a simple CLIP-inspired baseline, illustrating the necessity for multimodal datasets to achieve broad geo-spatial understanding.

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