GeoBench: Unified Geospatial AI Benchmarks

• GeoBench is a collection of benchmark suites designed to evaluate performance in geospatial and geometric AI tasks.
• It covers diverse applications from geoscience language understanding and monocular geometry to remote sensing and geolocalization.
• The benchmarks emphasize rigorous evaluation protocols, reproducibility, and open-source contributions to advance real-world geospatial AI research.

GeoBench refers to a constellation of modern benchmark suites and protocols in the geospatial and geometric AI domains. Across recent years, multiple distinct benchmarks—often sharing similar naming—have become de facto standards for evaluating model performance in geoscience language understanding, monocular geometry estimation, geometric image editing, remote sensing foundation models, vision-language geospatial reasoning, geolocalization, and cross-band generalization. While their focal tasks and technical scope are diverse, all GeoBench-derived benchmarks emphasize rigorous, multi-domain evaluation, reproducibility, cross-modality challenge, and open-source community contribution.

1. Nomenclature and Conceptual Scope

The term “GeoBench” is polysemous within AI literature, referring to both individual standalone benchmarks and an evolving suite of evaluation protocols:

• In geoscience language understanding, GeoBench is an exam-style QA benchmark for assessing LLM performance on geology, geography, and environmental science content (Deng et al., 2023).
• In monocular geometry estimation, GeoBench serves as a comprehensive, controlled platform for depth, surface normal, and 3D correspondence tasks (Ge et al., 18 Jun 2024).
• In geometric image editing, GeoBench denotes a zero-shot evaluation set for 2D/3D object warping and structural completion in images (Zhu et al., 31 Jul 2025).
• In remote sensing, “GEO-Bench” encompasses Earth monitoring tasks (classification, segmentation) for foundation model assessment (Lacoste et al., 2023), subsequently extended by GEO-Bench-2 and GeoCrossBench to support multispectral, multi-temporal, and cross-satellite generalization (Simumba et al., 19 Nov 2025, Tamazyan et al., 4 Nov 2025).
• GeoBench also denominates testbeds for geospatial agentic reasoning (Krechetova et al., 23 Mar 2025), geolocalization (Wang et al., 19 Nov 2025), cross-view localization (Zheng et al., 17 Nov 2025), and gridded climate analysis (Jiang et al., 15 May 2025).

These shared naming conventions reflect converging goals: to unify evaluation, stress-test robust generalization, and drive progress on real-world challenges in geospatial machine intelligence.

2. Key Benchmark Instantiations and Their Formal Structure

Benchmark/Task	Domain Focus	Core Metrics
GeoBench (QA, K2) (Deng et al., 2023)	Geoscience LLM: exam QA, essays	Accuracy, Perplexity, Human Eval
GeoBench (Mono. Geometry) (Ge et al., 18 Jun 2024)	Monocular depth, surface-normals, 3D consistency	AbsRel, RMSE, mIoU, Angle Error
GeoBench (Geo-edit) (Zhu et al., 31 Jul 2025)	Geometric image editing (2D/3D)	FID, KD, SUBC, BC, WE, MD
GEO-Bench (Lacoste et al., 2023)	EO ImgCls+Segm. (S2, L8, aerial RGB...)	Accuracy, F1, mIoU, IQM
GEO-Bench-2 (Simumba et al., 19 Nov 2025)	GeoFM: cls, seg, regression, OD, inst-segm	Jaccard, mAP, F1, RMSE, IQM
GeoBenchX (Krechetova et al., 23 Mar 2025)	Tool-calling, multi-step geospatial tasks	Matching Rate, Precision, F1
GeoVista GeoBench (Wang et al., 19 Nov 2025)	Image geolocalization (panorama, photo, sat)	Acc (country/state/city), Haversine dist.
GeoX-Bench (Zheng et al., 17 Nov 2025)	Cross-view geo-localization/pose (pan–sat)	Recall@K, MAE orientation, mIoU
GeoGrid-Bench (Jiang et al., 15 May 2025)	Gridded climate variable VLM QA	Accuracy, MSE, F1
GEOBench-VLM (Danish et al., 28 Nov 2024)	Geospatial VLM: counting, segm., change, SAR	MCQ Accuracy, mIoU, Prec@IoU
GeoCrossBench (Tamazyan et al., 4 Nov 2025)	Cross-satellite/band generalization	Acc, mIoU (ZBO, SS protocols)

Each benchmark comprises rigorously constructed datasets, formalized evaluation protocols with standardized metrics reflecting the peculiarities and challenges of the domain (e.g., mIoU for segmentation, Jaccard/accuracy for classification, precise MCQ accuracy for QA, recall@K for retrieval). Open licensing, codebase release, and careful curation (e.g., geographical stratification, band-specific ablation) are common features.

3. Methodological Design and Evaluation Protocols

Data Composition and Sampling

• Many GeoBench variants calibrate for geographic diversity, sensor variety (optical/MSI/SAR, 0.1–30 m GSD), class cardinality (binary to 43-way), and scene complexity.
• Climate, remote sensing, and geoscience instantiations emphasize coverage across weather/crop types, urban/rural scenes, time periods, and modalities (panorama/photo/satellite).
• Zero-shot or strictly controlled recipe evaluation dominates, minimizing overfitting to specific test distributions (Ge et al., 18 Jun 2024, Zhu et al., 31 Jul 2025).

Scoring, Aggregation, and Capabilities

• Task-level scores (accuracy, F1, mIoU, RMSE) are aggregated using interquartile mean (IQM), bootstrapping for uncertainty, and normalization against a baseline range (Lacoste et al., 2023, Simumba et al., 19 Nov 2025).
• Several frameworks (GEO-Bench-2) introduce “capability” groups: overlapping clusters of datasets sharing key axes (e.g., temporal, spectral, pixel-wise vs. detection), allowing users to interrogate model strengths on specialized subdomains.
• Explicit error taxonomies and ablations (e.g., freezing encoder, removing bands/timestamps) are reported to diagnose failure modes.

Protocol Examples

Formal metrics are mathematically specified, e.g.

• Mean IoU:

$$\mathrm{mIoU} = \frac{1}{C}\sum_{c=1}^C \frac{TP_c}{TP_c + FP_c + FN_c}$$

• Haversine metric for geolocalization:

$$d = 2 R_e \arcsin\left(\sqrt{\sin^2\left(\frac{\hat\phi-\phi}{2}\right) + \cos(\phi)\cos(\hat\phi)\sin^2\left(\frac{\hat\lambda-\lambda}{2}\right)}\right)$$

Across benchmarks, train/val/test splits are spatially disjoint and stratified, with emphasis on robust generalization and fair comparison.

4. Core Findings and Model Performance Insights

• General-purpose vision transformers (ConvNeXt, ViT, DINOv3) pretrained on natural images excel on RGB, high-res tasks, but Earth observation-specific models (Clay-V1, TerraMind, Prithvi) are superior on multispectral, multi-temporal, or SAR-enhanced tasks (Simumba et al., 19 Nov 2025).
• For specialized tasks such as program-to-geometry reasoning, even the largest LLMs fail to exceed 50 % accuracy at the highest abstraction level, revealing an unresolved challenge in symbolic-to-spatial geometric reasoning (Luo et al., 23 May 2025).
• Discriminative monocular geometry models, when trained on a small amount of high-quality synthetic data, consistently outperform more complex generative (diffusion-based) methods under equal training conditions (Ge et al., 18 Jun 2024).
• Domain-adapted LLMs (e.g., K2–7B) substantially outperform generic LLMs on geoscience technical QA and subjective tasks (Deng et al., 2023).

Notable error modalities include spatial reasoning failures, inability to reject unsolvable or ill-posed tasks, and significant drops in cross-sensor (zero-band-overlap) generalization, even in foundation models (Krechetova et al., 23 Mar 2025, Tamazyan et al., 4 Nov 2025).

5. Impact, Community Adoption, and Open Problems

GeoBench and its descendents have become essential for:

• Powering the development and benchmarking of foundation models in remote sensing, geoscience, and geometric vision.
• Diagnosing specific bottlenecks in cross-modal, spectral, and temporal generalization.
• Promoting transparent, reproducible, and open research via large-scale, permissively licensed datasets and codebases.

However, the aspiration toward a single universal geospatial foundation model remains unmet. All tested architectures exhibit domain/task specificity: e.g., natural–image pretraining confers advantages on high-res RGB in detection/classification, but EO–specific pretraining is optimal for multi-temporal/multispectral tasks (Simumba et al., 19 Nov 2025). Adding new spectral bands or generalizing to unseen sensors continues to degrade performance by up to 75%, highlighting the need for more robust architectures and flexible input models (Tamazyan et al., 4 Nov 2025).

Key challenges include standardized SAR-only capabilities, predictive uncertainty quantification, mitigating geographic bias, and establishing clear transfer scaling laws analogous to those found in natural language (Simumba et al., 19 Nov 2025).

6. Future Directions and Extensions

The trajectory of GeoBench-style benchmarks is characterized by:

• Extension to new modalities (e.g., SAR, elevation, video), tasks (temporal object tracking, agentic movement, geometric program synthesis), and reasoning paradigms (tool-based agents, code generation, web/internet augmentation).
• Introduction of hierarchical and compositional tasks (e.g., cross-view geo-localization via both imagery and spatial reasoning) (Zheng et al., 17 Nov 2025).
• Emphasis on annotation methods and evaluation protocols minimizing domain leakage and maximizing challenge, including unsolvable tasks, multi-turn agentic chains, and zero-shot splits (Krechetova et al., 23 Mar 2025, Zhu et al., 31 Jul 2025).
• Community-driven expansion of datasets, instruction corpora, and benchmarking capabilities, together with leaderboards and artifact sharing.

A plausible implication is that the scope of GeoBench and its successors will further broaden, occupying a unifying role across spectral, spatial, temporal, and symbolic domains for geospatial AI. Benchmarks will continue to shape not only technical progress but also the operational and methodological standards by which geospatial AI is measured.