--- license: cc-by-4.0 language: - en pretty_name: "GSTA: Geo-SpatioTemporal Atlas" task_categories: - question-answering - visual-question-answering - text-generation tags: - geospatial - spatiotemporal - geoai - benchmark - llm - mllm - remote-sensing - urban-computing - navigation - dataset-index size_categories: - n<1K --- # ๐ŸŒ GSTA: Geo-SpatioTemporal Atlas ### A curated benchmark atlas for geospatial-temporal LLM & MLLM research

Hugging Face Dataset License CC-BY-4.0 Benchmarks Scope Geo x Spatial x Temporal

GSTA is a lightweight, metadata-only registry of benchmarks for geospatial, spatial, temporal, urban, mobility, map, navigation, remote-sensing, and Earth-observation research with language models.

Quick Start ยท At a Glance ยท Schema ยท Contribute

--- > [!IMPORTANT] > GSTA does **not** redistribute benchmark samples. Each row is a curated metadata entry pointing to the original paper, code, and data sources when stable public URLs were verified. Please always check the original benchmark license, access policy, and citation requirements before downloading or redistributing linked resources. ## โœจ Why GSTA? Geospatial-temporal evaluation is scattered across urban computing, remote sensing, map reasoning, navigation, GIS, mobility, Earth observation, and multimodal scene understanding. GSTA provides a compact atlas for researchers who need to answer questions like: - **Which benchmarks evaluate geospatial reasoning in LLMs or MLLMs?** - **Which datasets are text-only, multimodal, map-based, remote-sensing, or tool-oriented?** - **What papers, code repositories, public data URLs, metrics, and caveats should I check before choosing an evaluation suite?** - **How does benchmark coverage differ across domains, modalities, and years?** ## ๐Ÿ“ฆ Dataset at a Glance
Live benchmark count
benchmark entries		 16
metadata columns		 Paper-linked
publication metadata		 LLM + MLLM
model interfaces
Maps
navigation & routing		 Urban
mobility & city tasks		 Remote Sensing
Earth observation		 Spatiotemporal
reasoning & planning
### Current interface coverage | Model interface | Typical inputs | |---|---| | `MLLM` | images, map snapshots, remote-sensing imagery, video, visual scenes | | `LLM` | text, tables, coordinates, trajectories, structured spatiotemporal records | | `LLM; MLLM` | mixed text/tool/visual settings | ### Included benchmark inventory `GeoBench` ยท `CityBench` ยท `UrBench` ยท `MapEval` ยท `GEOBench-VLM` ยท `XLRS-Bench` ยท `STARK` ยท `TurnBack` ยท `TripCraft` ยท `GeoBenchX` ยท `GeoAnalystBench` ยท `USTBench` ยท `FRIEDA` ยท `GTR-Bench` ยท `GeoMMBench` ยท `VIR-Bench` ยท `UrbanFeel` ยท `TimeSpot` ยท `ERGeoBench` ยท `EarthSpatialBench` ยท `CityLens` ยท `GeoAgentBench` ยท `GPSBench` ยท `UrbanGeoEval` ยท `Compositional GeoQA` ยท `TP-RAG` ## ๐Ÿš€ Quick Start ```python from datasets import load_dataset # The dataset is a metadata table; each row describes one benchmark. ds = load_dataset("zhangdw/GSTA", split="train") print(ds.column_names) print(ds[0]) ``` Prefer pandas for fast inspection? ```python import pandas as pd url = "https://huggingface.co/datasets/zhangdw/GSTA/resolve/main/gsta_benchmarks.csv" df = pd.read_csv(url) # Example: find multimodal map / remote-sensing benchmarks view = df[df["model_type"].str.contains("MLLM", na=False)] print(view[["benchmark", "domain", "task_type", "metrics"]].head()) ``` ## ๐Ÿ—‚๏ธ Dataset File | File | Description | |---|---| | `gsta_benchmarks.csv` | One benchmark per row. This is the canonical metadata table for the current public snapshot. | ## ๐Ÿงญ Example Discovery Workflows
Find benchmarks by model interface ```python llm_only = df[df["model_type"].eq("LLM")] multimodal = df[df["model_type"].str.contains("MLLM", na=False)] ```
Find navigation, map, or mobility benchmarks ```python mask = df["domain"].str.contains("map|navigation|mobility|urban", case=False, na=False) print(df.loc[mask, ["benchmark", "domain", "task_type", "paper_url"]]) ```
Audit rows that need manual URL follow-up ```python needs_code = df["code_url"].isna() | df["code_url"].eq("") needs_data = df["data_url"].isna() | df["data_url"].eq("") print(df.loc[needs_code | needs_data, ["benchmark", "code_url", "data_url", "url_status", "notes"]]) ```
## ๐Ÿงฑ Schema | Column | Description | |---|---| | `benchmark` | Benchmark name or acronym; should be unique within GSTA. | | `abstract` | One-sentence summary of what the benchmark evaluates. | | `paper_title` | Title of the paper that introduced or released the benchmark. | | `publication_year` | Publication year of the benchmark paper. | | `venue` | Venue and track if needed. | | `research_group` | Main organization, lab, or research group behind the benchmark paper, based on public author affiliations and official project/repository ownership. | | `paper_url` | Canonical paper URL. | | `code_url` | Official code repository URL, if verified. | | `data_url` | Official public data or access URL(s), if verified; semicolon-separated for multiple URLs. | | `url_status` | URL verification, access, and release-status note, especially for rows with blank `code_url` cells or non-direct data access. | | `domain` | Domain or scenario covered by the benchmark. | | `task_type` | Semicolon-separated short task labels. | | `model_type` | `LLM`, `MLLM`, or `LLM; MLLM`. | | `sample_count` | Core benchmark scale. | | `metrics` | Main evaluation metrics. | | `notes` | Caveats, release status, count notes, or registry-specific comments. | ## ๐Ÿงฉ Model Type Convention | Label | Meaning | |---|---| | `LLM` | Text-based language models, including standard LLMs and reasoning-oriented LLMs/LRMs. Coordinates, trajectories, tables, sensor values, and time series still count as `LLM` if serialized as text or structured text. | | `MLLM` | Multimodal large language models with non-text inputs such as images, remote-sensing imagery, map images, videos, visual scenes, or raster data. | | `LLM; MLLM` | Benchmarks that evaluate both text-based and multimodal model interfaces. | ## โœ… Intended Use GSTA is designed for: - literature review and benchmark discovery; - selecting evaluation suites for geospatial-temporal LLM/MLLM research; - tracking benchmark coverage across domains, modalities, metrics, and venues; - maintaining lab-internal or community-facing benchmark inventories; - identifying gaps in current geospatial-temporal model evaluation. ## ๐Ÿค Contributing a Benchmark Community contributions are welcome. A high-quality new entry should include: - [ ] a stable benchmark name; - [ ] paper title, year, venue, and canonical paper URL; - [ ] verified official code and data links when available; - [ ] concise domain and task labels; - [ ] model interface label: `LLM`, `MLLM`, or `LLM; MLLM`; - [ ] sample count and primary metrics; - [ ] notes for caveats, missing public releases, version differences, or count discrepancies. > [!TIP] > If a benchmark has multiple released subsets or versions, prefer a conservative row-level summary and document the version-specific details in `notes`. ## ๐Ÿ› ๏ธ Maintenance Notes This public snapshot is maintained as a live CSV registry. Some rows intentionally leave `code_url` empty when a stable public code repository was not verified, and some `data_url` entries point to official access instructions rather than direct downloads. `url_status` records the current follow-up state for those cases. GSTA favors traceability over completeness: uncertain URLs should remain blank until verified against the original release channel. ## ๐Ÿ“š Citation If you use GSTA, please cite this dataset and the original benchmark papers you rely on. ```bibtex @misc{gsta2026, title = {GSTA: Geo-SpatioTemporal Atlas}, author = {Dawei Zhang}, year = {2026}, howpublished = {Hugging Face Dataset}, url = {https://huggingface.co/datasets/zhangdw/GSTA} } ``` ---
GSTA aims to make geospatial-temporal evaluation easier to discover, compare, and maintain.