OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation

Researchers introduced OVEarth-Bench, a benchmark for open-vocabulary Earth observation that expands category breadth and query diversity, finding that current methods perform limitedly, with MLLM-based methods achieving the strongest overall performance and EO-specific methods generally underperforming general models. The benchmark supports mask and box localization under a unified zero-shot protocol and is released at https://earth-insights.github.io/OVEarth-bench.

arXiv:2607.27278v1 Announce Type: new Abstract: Open-vocabulary Earth observation EO aims to localize geospatial concepts specified in natural language rather than a fixed label set. Existing benchmarks, however, usually cover narrow category vocabularies or limited query forms. To fill this gap, we introduce OVEarth-Bench, which extends existing evaluation in two directions: category breadth, through broad hierarchical category coverage with positive and negative expressions, and query diversity, through vocabulary, referring, and reasoning queries. The benchmark supports mask and box localization under a unified zero-shot protocol. We evaluate a broad set of general and EO-specific methods. The evaluation reveals that: 1 the performance of current methods remains limited, while broader category coverage yields more stable model rankings; 2 MLLM-based methods achieve the strongest overall performance; and 3 EO-specific methods generally underperform general models and rarely match the strongest methods. These findings provide guidance for future open-vocabulary EO method design and highlight the importance of developing more realistic, diverse, high-quality, and large-scale benchmarks for reliable evaluation. Our data and evaluation package are released at https://earth-insights.github.io/OVEarth-bench.