---
pretty_name: GABench Data
language:
- en
task_categories:
- text-generation
tags:
- geospatial
- gis
- geoagentbench
- agent-benchmark
- tool-use
- spatial-analysis
- benchmark
- data-artifacts
license: other
size_categories:
- n<1K
---
# πΊοΈ GABench Data
### Hub-hosted GIS artifacts for GABench / GeoAgentBench evaluation
GABench Data stores the large benchmark table, GIS input layers, and reference outputs used by the GABench / GeoAgentBench evaluation code.
Quick Start Β·
At a Glance Β·
Files Β·
Benchmark Table Β·
Citation
---
> [!IMPORTANT]
> This dataset is an **artifact mirror**, not a standalone benchmark implementation. The Hugging Face repository stores the files that were previously tracked with Git LFS; the benchmark code and evaluation logic live in the companion GitHub repository.
## β¨ Why This Dataset?
GABench / GeoAgentBench evaluates agents on realistic GIS analysis tasks that combine natural-language instructions, geospatial data layers, toolchain planning, and generated map or table outputs. The data artifacts are too large and heterogeneous for a lightweight code checkout, so this Hub repository keeps the file tree separately while preserving the relative paths expected by the evaluation code.
It is designed for questions like:
- **How do I restore the GABench `benchmark/` and `dataset/` folders without pulling large Git LFS blobs through Git?**
- **Which GIS domains, input layers, toolchain lengths, and reference outputs are covered by the benchmark table?**
- **Can I inspect the task descriptions and expected output files before running the evaluator?**
- **Can the code repository stay small while the benchmark artifacts remain versioned and downloadable?**
## π¦ Dataset at a Glance
53
benchmark tasks |
6
GIS domains |
190
artifact files |
2.60 GB
Hub storage |
188
dataset/ files |
50
reference outputs |
3-17
toolchain steps |
1
benchmark table |
### Domain coverage
| Domain | Tasks |
|---|---:|
| Raster Spatial Analysis | 16 |
| Geostatistical Analysis | 13 |
| Hydrological Analysis | 11 |
| Vector Spatial Analysis | 7 |
| 3D Modeling and Analysis | 4 |
| Spatial Data Management | 2 |
| **Total** | **53** |
### File-format coverage
| File family | Formats | Count |
|---|---|---:|
| Raster and gridded data | `.tif`, `.nc` | 34 |
| Vector GIS data | `.geojson`, `.shp`, `.dbf`, `.shx`, `.prj`, `.gpkg` | 95 |
| Tables and spreadsheets | `.csv`, `.xlsx` | 9 |
| Reference visual outputs | `.png` | 49 |
| Graph or serialized objects | `.graphml`, `.pkl` | 2 |
| Manifest | `.txt` | 1 |
## π Quick Start
Download the benchmark artifacts into a local checkout of the code repository:
```bash
uvx --from huggingface_hub hf download zhangdw/GABench \
--repo-type dataset \
--local-dir . \
--include 'benchmark/**' \
--include 'dataset/**' \
--include 'metadata/**'
```
After download, the original relative paths expected by the benchmark code are restored:
```text
benchmark/benchmark.csv
dataset/
dataset/result/
metadata/lfs-files.txt
```
Read the benchmark table directly from the Hub:
```python
import pandas as pd
url = "https://huggingface.co/datasets/zhangdw/GABench/resolve/main/benchmark/benchmark.csv"
df = pd.read_csv(url, encoding="utf-8-sig")
print(len(df))
print(df[["ID", "Domain", "Task Description", "Toolchain Length", "Result"]].head())
```
Download only the reference outputs:
```bash
uvx --from huggingface_hub hf download zhangdw/GABench \
--repo-type dataset \
--local-dir gabench-results \
--include 'dataset/result/**'
```
## ποΈ Files
| Path | Description |
|---|---|
| `benchmark/benchmark.csv` | Canonical benchmark task table with 53 GIS analysis tasks. |
| `dataset/` | GIS input layers plus reference outputs used by the benchmark. |
| `dataset/result/` | Expected result artifacts, mostly `.png` maps plus one `.csv` output. |
| `metadata/lfs-files.txt` | Manifest of files migrated from the Git LFS-backed source tree. |
## 𧱠Benchmark Table
`benchmark/benchmark.csv` is the primary task index. The logical columns are:
| Column | Description |
|---|---|
| `ID` | Stable task identifier. |
| `Domain` | GIS task family, such as raster, vector, hydrological, or geostatistical analysis. |
| `Task Description` | Natural-language instruction for the agent or evaluator. |
| `Data Description` | Input files and field-level hints needed by the task. |
| `Drawing Style` | Visualization requirements for map-style outputs. |
| `Toolchain Length` | Number of expected toolchain steps. |
| `Toolchain JSON` | Structured representation of the target GIS workflow. |
| `Result` | Expected output filename under `dataset/result/` or related output path. |
| `Layers` | Referenced GIS layers. |
> [!NOTE]
> The source CSV keeps several trailing empty columns. They are preserved here to avoid changing the upstream-compatible table layout.
## π§ Example Workflows
Inspect task distribution by domain
```python
import pandas as pd
df = pd.read_csv("benchmark/benchmark.csv", encoding="utf-8-sig")
print(df.groupby("Domain").size().sort_values(ascending=False))
```
Find tasks that require longer toolchains
```python
long_tasks = df[df["Toolchain Length"].astype(int) >= 10]
print(long_tasks[["ID", "Domain", "Toolchain Length", "Result"]])
```
List referenced result files
```python
expected = sorted(df["Result"].dropna().unique())
for name in expected[:20]:
print(name)
```
## π Use With Code
This dataset is intended to be paired with the code repository rather than loaded as a conventional train/test split.
| Component | Location |
|---|---|
| Upstream project | `GeoX-Lab/GABench` |
| Companion code fork | `zhangdw156/GABench` |
| Code branch | `feat/leo-260609` |
| Data artifact repository | `zhangdw/GABench` on Hugging Face |
The GitHub repository keeps the benchmark and evaluation code; this Hugging Face dataset stores the benchmark table, GIS inputs, and reference outputs.
## β
Intended Use
This repository is useful for:
- restoring the GABench / GeoAgentBench data tree for evaluation runs;
- inspecting benchmark tasks, domains, required layers, toolchains, and outputs;
- separating large GIS artifacts from a lightweight code checkout;
- reproducing the artifact state used by the `feat/leo-260609` companion branch.
It is not meant to replace the official benchmark code, define a new evaluation protocol, or serve as a general-purpose geospatial training corpus.
## βοΈ License
The upstream repository did not expose a clear license file at the time this dataset card was created, so the Hub metadata is marked as `other` rather than inferred. Before redistributing, modifying, or using these files in downstream releases, check the original GABench / GeoAgentBench project and the terms of any source geospatial datasets referenced by individual tasks.
## π οΈ Maintenance Notes
- The path layout intentionally mirrors the source tree expected by the benchmark code.
- `metadata/lfs-files.txt` records the files that were migrated out of Git LFS.
- README-only edits should not imply a new data version unless the benchmark table, input artifacts, or reference outputs change.
- If upstream GABench / GeoAgentBench changes its task table or GIS files, update the manifest and this card together.
## π Citation
If you use this artifact mirror, cite the original GeoAgentBench paper for benchmark design and evaluation methodology, and cite this Hub dataset for data provenance.
```bibtex
@misc{yu2026geoagentbenchdynamicexecutionbenchmark,
title = {GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis},
author = {Bo Yu and Cheng Yang and Dongyang Hou and Chengfu Liu and Jiayao Liu and Chi Wang and Zhiming Zhang and Haifeng Li and Wentao Yang},
year = {2026},
eprint = {2604.13888},
archivePrefix = {arXiv},
primaryClass = {cs.AI},
url = {https://arxiv.org/abs/2604.13888}
}
```
You may also cite this Hugging Face artifact mirror when data provenance matters:
```bibtex
@misc{gabench_data2026,
title = {GABench Data},
author = {Dawei Zhang},
year = {2026},
howpublished = {Hugging Face Dataset},
url = {https://huggingface.co/datasets/zhangdw/GABench}
}
```
---
A path-compatible Hugging Face artifact mirror for GIS-agent benchmark evaluation.