Datasets:

vimageiitb
/

GeoDisaster

---
license: other
language:
- en
pretty_name: GeoDisaster Agentic 2921 (Multi-Agent + Single-Agent Tool-Use Chains)
tags:
- geospatial
- remote-sensing
- disaster-response
- earth-observation
- tool-use
- agentic
- multi-agent
- single-agent
- supervised-fine-tuning
- reasoning-chains
task_categories:
- question-answering
- image-text-to-text
size_categories:
- 1K<n<10K
configs:
- config_name: evaluation_inputs
  data_files:
  - split: test
    path: data/core/geodisaster_2921_test_inputs.jsonl
- config_name: multi_agent
  data_files:
  - split: train
    path: data/multi_agent/train_conversations.jsonl
  - split: test
    path: data/multi_agent/test_conversations.jsonl
- config_name: single_agent
  data_files:
  - split: train
    path: data/single_agent/train_conversations.jsonl
  - split: test
    path: data/single_agent/test_reference_conversations.jsonl
- config_name: single_agent_sft
  data_files:
  - split: train
    path: data/single_agent/train_turns.jsonl
  - split: test
    path: data/single_agent/test_turns.jsonl
- config_name: multi_agent_sft_orc
  data_files:
  - split: train
    path: data/multi_agent/sft_train/orc_train.jsonl
  - split: test
    path: data/multi_agent/sft_test/orc_eval.jsonl
- config_name: multi_agent_sft_ga
  data_files:
  - split: train
    path: data/multi_agent/sft_train/ga_train.jsonl
  - split: test
    path: data/multi_agent/sft_test/ga_eval.jsonl
- config_name: multi_agent_sft_pa
  data_files:
  - split: train
    path: data/multi_agent/sft_train/pa_train.jsonl
  - split: test
    path: data/multi_agent/sft_test/pa_eval.jsonl
- config_name: multi_agent_sft_vra
  data_files:
  - split: train
    path: data/multi_agent/sft_train/vra_train.jsonl
  - split: test
    path: data/multi_agent/sft_test/vra_eval.jsonl
- config_name: core
  data_files:
  - split: train
    path: data/core/geodisaster_2921_train_source.jsonl
  - split: test
    path: data/core/geodisaster_2921_test_source.jsonl
---
---

# 🌍 GeoDisaster

**Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence**

🎉 **Accepted at BMVC 2026**

📄 **[Paper](https://arxiv.org/pdf/2606.17246)** · 💻 **[Code & Documentation](https://github.com/VIMAGE-IITB/GeoDisaster)** · 🤗 **[Hugging Face Dataset](https://huggingface.co/datasets/vimageiitb/GeoDisaster/)**

*Grounded, multi-step disaster geo-intelligence from real Earth-observation and GIS evidence.*

---

## ✨ Benchmark at a glance

| **2,921** verified tasks | **43** question types | **5** disaster families | **2** agent settings |
|:---:|:---:|:---:|:---:|
| **2,629** train · **292** test | **55,648** turn-level SFT rows | **18** geospatial tools | Optical · SAR · Raster · Vector · Graph |

**GeoDisaster** evaluates whether tool-using agents can transform heterogeneous Earth-observation (EO) and Geographic Information System (GIS) inputs into **structured, evidence-grounded disaster decisions**. Each task connects a real geospatial question to declared input artifacts, an executable reference toolchain, and a verifiable answer target.

The dataset provides matched records for **single-agent** reasoning and **orchestrated multi-agent** execution. The same **2,921 underlying tasks** are available as reference task specifications, full conversations, and turn-level training examples; these are different representations of the same benchmark, *not* additional independent tasks.

> **For evaluation:** use the gold-free `evaluation_inputs` configuration / `data/core/geodisaster_2921_test_inputs.jsonl`. Reference answers and test trajectories must remain on the evaluator side. See [Evaluation without answer leakage](#-evaluation-without-answer-leakage).

## 🌋 Five disaster task families

| Task family | Dataset key | What it evaluates |
|---|---|---|
| 🌲 **Deforestation Monitoring** | `deforestation` | Forest-loss detection, multi-year attribution, and change measurement from EO layers |
| 🌪️ **Multi-Hazard Analysis** | `multi_hazard` | Hazard statistics, cross-layer comparison, spatial exposure, and region prioritization |
| 🏚️ **Building-Damage Assessment** | `xbd_damage` | Damage assessment and inventory from building footprints and disaster evidence |
| 🛣️ **Flood-Safe Routing** | `routing` | Network reasoning, hazard-aware route planning, and route comparison |
| 🌊 **Sentinel-1 SAR Flood Monitoring** | `sen1_flood` | SAR-driven flood mapping, flood measurement, and structured flood reporting |

**Evidence modalities:** optical and SAR imagery; forest-loss masks; hazard and flood rasters; building-damage polygons; vector geometries; road networks; exposure layers; and structured geospatial artifacts.

## 📦 What is included?

| Representation | Purpose | Contents |
|---|---|---|
| **Core benchmark** | Evaluation and reference | Task/question specification, available artifact handles, canonical ground-truth toolchain, structured expected answer |
| **Full conversations** | Trajectory analysis and supervised examples | Complete agent episodes: question → tool calls / observations → terminal answer, for single- and multi-agent settings |
| **Turn-level SFT** | Training and role-specific analysis | One assistant target per row, with the required context and metadata |

| Release statistics | Count |
|---|---:|
| Unique benchmark tasks | **2,921** |
| Training tasks (per full agent form) | **2,629** |
| Test tasks (per full agent form) | **292** |
| Single-agent turn-level rows | **20,453** |
| Multi-agent role-level rows | **35,195** |
| **Total turn-level rows** | **55,648** |
| Terminal targets across both full agent forms | **5,842** |

Training and test partitions are disjoint by **task ID, source sample, declared path, normalized routing content, and complete model-visible single-agent trajectory**. Within a split, exact repeated episodes are weighted by inverse complete-trajectory multiplicity via `training_weight`.

## 🚀 Quick start

Load the released configurations with the Hugging Face `datasets` library:

```python
from datasets import load_dataset
REPO = "vimageiitb/GeoDisaster"
core        = load_dataset(REPO, "core")
eval_inputs = load_dataset(REPO, "evaluation_inputs")
single      = load_dataset(REPO, "single_agent")
multi       = load_dataset(REPO, "multi_agent")
single_sft  = load_dataset(REPO, "single_agent_sft")
ga_sft      = load_dataset(REPO, "multi_agent_sft_ga")
```

Other specialist role-SFT files are available in the dataset repository under `data/multi_agent/sft_train/` and `data/multi_agent/sft_test/`. You can also read any released `.jsonl` file directly, one record per line.

**Choosing a view:** use `core` for reference specifications, `single_agent` / `multi_agent` for full episodes, SFT views for assistant-turn supervision, and **`evaluation_inputs` for agent-visible held-out tasks**. Never give reference-bearing test files to the evaluated agent.
## 🗂️ Repository layout
```text
data/data/
├── core/
│   ├── geodisaster_2921_{train,test}_source.jsonl   # evaluator/reference tasks
│   ├── geodisaster_2921_test_inputs.jsonl          # gold-free public test inputs
│   └── scoring_policy_43.json                     # active structured-answer policy
├── single_agent/
│   ├── {train_conversations,test_reference_conversations}.jsonl
│   ├── {train_turns,test_turns}.jsonl
│   └── single_agent_system_prompt.md
└── multi_agent/
    ├── {train,test}_conversations.jsonl
    ├── sft_train/{orc,ga,pa,vra}_{train,eval}.jsonl
    ├── sft_test/{orc,ga,pa,vra}_{train,eval}.jsonl
    └── tool_ownership_and_prompt_context.md
source_assets/                                  # bundled permissible EO/GIS inputs
release_tools/
├── fetch_sen1floods11_subset.py                # pinned SAR input installer
└── build_isolated_test_bundle.py               # held-out evaluation bundle
user-guide-final.md                             # authoritative benchmark/runtime guide
ATTRIBUTIONS.md                                # upstream licensing and provenance
SHA256SUMS                                     # distributable archive checksums
```
<details>
<summary><b>🔍 Record IDs, shared fields, and dataset schema</b></summary>
### Record identity
Every representation shares three identity fields:
- `id`: `<family>/<sample>/<question_type>` — the unique linking key across benchmark forms.
- `sample_id`: source sample identifier.
- `question_type`: one of the 43 question types.
For example, `deforestation/000086/multi_year_loss_attribution` identifies the same task across its core specification, conversations, and SFT turns. Source input files use repository-relative paths such as `source_assets/deforestation/000086/masks/lossyear.tif`. Non-distributed teacher replay outputs, if referenced, use portable `generated-artifact://` logical locators.
### Multi-agent conversation fields
Files: `data/multi_agent/{train,test}_conversations.jsonl`
| Field | Type | Description |
|---|---|---|
| `raw_question` | string | Plain question used for active orchestrator/specialist prompts |
| `question` | string | Stored evaluation/display context: question, input handles, and answer schema; active prompts use `raw_question` with sanitized handle cards |
| `initial_artifacts` | object | Declared input handle cards with path, label, existence, and optionality |
| `available_initial_handles` | list | Handles available to the task |
| `plan` | object | ORC plan with goal and `{id, agent, subtask, needs}` steps |
| `multiagentic_gt_chain` | list | Full USER ↔ ORC ↔ specialist trajectory, actions, and observations |
| `executions` | list | Per-step `{action, tool_output}` execution log |
| `expected_final_answer` | object | Audit copy of the reference answer; the core record is authoritative |
| `final_answer_ref` | string | Terminal transport token (`ANSWER_JSON:answer_1`) |
| `resolved_final_answer` | object | Schema-valid answer projected from worker evidence |
| `resolved_answer_sha256` | string | Canonical-JSON checksum linking derived records |
| `resolved_answer_provenance` | string | Export provenance: full evidence or validated compact-trace cache |
| `answer_support` | object | Supporting evidence metadata |
| `pure_toolchain_sim_status` | string | Toolchain replay status |
### Other released record types
| Path | Main fields |
|---|---|
| `data/core/*_source.jsonl` | `question`, `initial_artifacts`, `available_initial_handles`, `gt_chain`, `expected_final_answer`, `source_replay`, `path_summary`, `split_seed` |
| `data/single_agent/{train_conversations,test_reference_conversations}.jsonl` | `messages` (`role`, `content`), `tool_sequence`, `final_answer_ref`, `resolved_answer_sha256` |
| `data/single_agent/{train_turns,test_turns}.jsonl` | `messages`, `kind`, `assistant_action`, `assistant_turn_index` |
| `data/multi_agent/sft_{train,test}/*.jsonl` | `messages`, `role`, `step_id`, `turn_no`, `kind` |
Each role-SFT row contains the exact system-prompt snapshot associated with its target. The data package also includes the standalone single-agent prompt and the multi-agent role/tool prompt context. **Executable inference-time prompt builders are authoritative in the [code repository](https://github.com/VIMAGE-IITB/GeoDisaster).**
</details>
## 🛡️ Evaluation without answer leakage
**Only `data/core/geodisaster_2921_test_inputs.jsonl` should be exposed as test records to an evaluated agent.** The canonical reference data, `test_source`, multi-agent test conversations, and single-agent test-reference conversations contain evaluator-only targets.

For a filesystem-isolated evaluation, create a separate root containing just the **292 held-out test tasks** and their permitted inputs:

```bash
python3 release_tools/build_isolated_test_bundle.py \
  --output /path/outside/repository/eval_bundle
export GEODISASTER_DATASET_ROOT=/path/outside/repository/eval_bundle
export GEODISASTER_STRICT_DATA_ROOT=1
```

The bundle builder rejects private path declarations; omits `gt/`, `ground_truth/`, `hidden/`, and trajectory products when a declared input is a directory; and writes a SHA-256 manifest. Strict runtime mode prevents fallback searches through the development repository. **Keep gold scoring in a separate process or mount; do not mount the full dataset/development repository into the agent sandbox.**

The active structured-answer scoring policy is `data/core/scoring_policy_43.json` (`geodisaster_structured_answer_v2`). Older v1 policy text inside `correction_provenance.publication_contract` is historical publication provenance, **not** the active evaluation policy. Values in `correction_provenance.evidence` are opaque build labels, not runtime paths or agent inputs.

## 🧑‍🤝‍🧑 Agent settings and tool ownership

The released trajectories cover two settings:

- **Single-agent:** one agent plans, invokes tools, and finishes the task.
- **Multi-agent:** an orchestrator (**ORC**) assigns work to **GA**, **PA**, and **VRA** specialists.

| Role | Tools / responsibility |
|---|---|
| **ORC** — Orchestrator | Planning and delegation; owns no geospatial tools |
| **GA** — Geospatial Agent | `ReadData`, `PrepareScene`, `MeasureArea`, `ExtractObjects`, `MapFlood`, `RenderMap` |
| **PA** — Planning/Analysis Agent | `ComputeStats`, `CompareSources`, `DetectChange`, `ClassifyChange`, `ScoreHazard`, `EstimateExposure`, `RankRegions`, `PlanRoutes`, `CompareRoutes` |
| **VRA** — Visual Reasoning Agent | `AssessDamage`, `DescribeFlood`, `CompareScenes` |

**Terminal-answer contract:** all **5,842** full-episode terminal targets use the reference-only token `ANSWER_JSON:answer_1`. The runtime creates `answer_1` only from a schema-valid projection of worker evidence, resolves it at termination, and passes the resulting JSON to the evaluator. The token is **transport, not a second answer format**; agents do not emit the large resolved JSON themselves. Unknown or missing references fail explicitly, with no ground-truth fallback in live execution.

- Multi-agent ORC ends with `Terminate`; the single-agent baseline ends with `FinalAnswer`. Both carry the same answer reference.
- `TaskComplete` closes a specialist subgoal; it does **not** transmit the terminal answer.
- Single-agent training support covers SFT and deterministic/live evaluation; **GRPO/RCEA training is supported only for the four multi-agent roles**.

<details>
<summary><b>🧪 Example task and trajectory records</b></summary>

**Core task specification** (abridged for readability):

```json
{
  "id": "deforestation/000086/multi_year_loss_attribution",
  "sample_id": "000086",
  "question_type": "multi_year_loss_attribution",
  "question": {"handle": "question_1", "text": "… identify the year with the largest forest loss …"},
  "gt_chain": [
    {"tool": "ReadData", "args": {"input": "forest_loss_history_1"}},
    {"tool": "ComputeStats", "args": {}}
  ],
  "expected_final_answer": {"year": 2016, "loss_ha": 201.039833}
}
```

This example's `initial_artifacts` include a declared `forest_loss_history_1` handle pointing to `source_assets/deforestation/000086/masks/lossyear.tif`.

**Single-agent conversation** (abridged; the stored record includes full messages):

```json
{
  "id": "deforestation/000086/multi_year_loss_attribution",
  "tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"],
  "messages": [
    {"role": "user", "content": "QUESTION … AVAILABLE INPUT HANDLES …"},
    {"role": "assistant", "content": "{\"action\":{\"tool\":\"ReadData\",\"args\":{\"input\":\"forest_loss_history_1\"}}"},
    {"role": "assistant", "content": "{\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}
  ]
}
```

The corresponding multi-agent example uses the same task `id`, and additionally includes `plan`, `multiagentic_gt_chain`, `executions`, `resolved_final_answer`, and reference-answer metadata.

</details>

## 🛰️ Source assets and Sen1Floods11 setup

Bundled files in `source_assets/` cover redistributable EO/GIS inputs, while the selected **Sen1Floods11** S1/JRC payload is intentionally **not included** in the thin public release.

To download the pinned **100-chip subset** from the official upstream bucket and verify the required files:

```bash
python3 release_tools/fetch_sen1floods11_subset.py
python3 release_tools/fetch_sen1floods11_subset.py --verify-only
```

**Important reproducibility details:**

- The **500 `sen1_flood` task records** describe a hydrated execution contract; `exists: true` does not mean every source file is already present in the downloaded archive.
- The installer fetches and verifies **200 upstream S1/JRC source paths** and produces **100 portable execution rasters** (300 hydrated files in total).
- `SHA256SUMS` covers the distributed thin archive but **excludes** locally hydrated files under `source_assets/sen1_flood/`; the installer verifies those separately using its pinned upstream manifest.
- Dataset input paths are repository-relative. Follow `user-guide-final.md` for task-specific requirements and correct execution semantics.
## ⚖️ Licensing and attribution
**This is a mixed-license dataset.** Its Hugging Face license field is therefore `other`, not a blanket CC-BY-4.0 declaration.
| Component | Applicable terms |
|---|---|
| Project-authored questions, annotations, and tool-use chains | **CC BY 4.0** |
| Redistributed `source_assets/` | Retain their respective upstream licenses and conditions |
| xBD / xView2-derived assets | **Non-commercial restrictions** from their original sources |
| Selected Sen1Floods11 subset | Retrieved from the official source; subject to upstream terms |

Read **`ATTRIBUTIONS.md`** for upstream attributions and component-specific licenses before reusing or redistributing source imagery and geospatial files.

## 📖 Documentation and paper

The dataset package includes **`user-guide-final.md`**, the authoritative guide covering the benchmark contract, all 43 question types, runtime/evaluation behavior, and release setup. For executable inference-time code and additional project documentation, see the [GeoDisaster GitHub repository](https://github.com/VIMAGE-IITB/GeoDisaster).

**Paper:** *GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence* — **accepted at BMVC 2026**.

**Authors:** Maram Hasan, Aman Verma, Savitra Roy, Hariseetharam Gunduboina, Daksh Jain, Muhammad Haris Khan, Subhasis Chaudhuri, and Biplab Banerjee.

**[📄 Read PDF](https://arxiv.org/pdf/2606.17246)** · **[🔎 arXiv abstract](https://arxiv.org/abs/2606.17246)** · **[💻 Code](https://github.com/VIMAGE-IITB/GeoDisaster)**

### Citation

If you use GeoDisaster tasks, reference outputs, or trajectories, please cite the accompanying paper:

```bibtex
@misc{hasan2026geodisaster,
  title         = {GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence},
  author        = {Maram Hasan and Aman Verma and Savitra Roy and Hariseetharam Gunduboina and Daksh Jain and Muhammad Haris Khan and Subhasis Chaudhuri and Biplab Banerjee},
  year          = {2026},
  eprint        = {2606.17246},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2606.17246},
  note          = {Accepted at BMVC 2026}
}
```

	---
	license: other
	language:
	- en
	pretty_name: GeoDisaster Agentic 2921 (Multi-Agent + Single-Agent Tool-Use Chains)
	tags:
	- geospatial
	- remote-sensing
	- disaster-response
	- earth-observation
	- tool-use
	- agentic
	- multi-agent
	- single-agent
	- supervised-fine-tuning
	- reasoning-chains
	task_categories:
	- question-answering
	- image-text-to-text
	size_categories:
	- 1K<n<10K
	configs:
	- config_name: evaluation_inputs
	data_files:
	- split: test
	path: data/core/geodisaster_2921_test_inputs.jsonl
	- config_name: multi_agent
	data_files:
	- split: train
	path: data/multi_agent/train_conversations.jsonl
	- split: test
	path: data/multi_agent/test_conversations.jsonl
	- config_name: single_agent
	data_files:
	- split: train
	path: data/single_agent/train_conversations.jsonl
	- split: test
	path: data/single_agent/test_reference_conversations.jsonl
	- config_name: single_agent_sft
	data_files:
	- split: train
	path: data/single_agent/train_turns.jsonl
	- split: test
	path: data/single_agent/test_turns.jsonl
	- config_name: multi_agent_sft_orc
	data_files:
	- split: train
	path: data/multi_agent/sft_train/orc_train.jsonl
	- split: test
	path: data/multi_agent/sft_test/orc_eval.jsonl
	- config_name: multi_agent_sft_ga
	data_files:
	- split: train
	path: data/multi_agent/sft_train/ga_train.jsonl
	- split: test
	path: data/multi_agent/sft_test/ga_eval.jsonl
	- config_name: multi_agent_sft_pa
	data_files:
	- split: train
	path: data/multi_agent/sft_train/pa_train.jsonl
	- split: test
	path: data/multi_agent/sft_test/pa_eval.jsonl
	- config_name: multi_agent_sft_vra
	data_files:
	- split: train
	path: data/multi_agent/sft_train/vra_train.jsonl
	- split: test
	path: data/multi_agent/sft_test/vra_eval.jsonl
	- config_name: core
	data_files:
	- split: train
	path: data/core/geodisaster_2921_train_source.jsonl
	- split: test
	path: data/core/geodisaster_2921_test_source.jsonl
	---
	---

	# 🌍 GeoDisaster

	Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence

	🎉 Accepted at BMVC 2026

	📄 [Paper](https://arxiv.org/pdf/2606.17246) · 💻 [Code & Documentation](https://github.com/VIMAGE-IITB/GeoDisaster) · 🤗 [Hugging Face Dataset](https://huggingface.co/datasets/vimageiitb/GeoDisaster/)

	Grounded, multi-step disaster geo-intelligence from real Earth-observation and GIS evidence.

	---

	## ✨ Benchmark at a glance

	\| 2,921 verified tasks \| 43 question types \| 5 disaster families \| 2 agent settings \|
	\|:---:\|:---:\|:---:\|:---:\|
	\| 2,629 train · 292 test \| 55,648 turn-level SFT rows \| 18 geospatial tools \| Optical · SAR · Raster · Vector · Graph \|

	GeoDisaster evaluates whether tool-using agents can transform heterogeneous Earth-observation (EO) and Geographic Information System (GIS) inputs into structured, evidence-grounded disaster decisions. Each task connects a real geospatial question to declared input artifacts, an executable reference toolchain, and a verifiable answer target.

	The dataset provides matched records for single-agent reasoning and orchestrated multi-agent execution. The same 2,921 underlying tasks are available as reference task specifications, full conversations, and turn-level training examples; these are different representations of the same benchmark, not additional independent tasks.

	> For evaluation: use the gold-free `evaluation_inputs` configuration / `data/core/geodisaster_2921_test_inputs.jsonl`. Reference answers and test trajectories must remain on the evaluator side. See [Evaluation without answer leakage](#-evaluation-without-answer-leakage).

	## 🌋 Five disaster task families

	\| Task family \| Dataset key \| What it evaluates \|
	\|---\|---\|---\|
	\| 🌲 Deforestation Monitoring \| `deforestation` \| Forest-loss detection, multi-year attribution, and change measurement from EO layers \|
	\| 🌪️ Multi-Hazard Analysis \| `multi_hazard` \| Hazard statistics, cross-layer comparison, spatial exposure, and region prioritization \|
	\| 🏚️ Building-Damage Assessment \| `xbd_damage` \| Damage assessment and inventory from building footprints and disaster evidence \|
	\| 🛣️ Flood-Safe Routing \| `routing` \| Network reasoning, hazard-aware route planning, and route comparison \|
	\| 🌊 Sentinel-1 SAR Flood Monitoring \| `sen1_flood` \| SAR-driven flood mapping, flood measurement, and structured flood reporting \|

	Evidence modalities: optical and SAR imagery; forest-loss masks; hazard and flood rasters; building-damage polygons; vector geometries; road networks; exposure layers; and structured geospatial artifacts.

	## 📦 What is included?

	\| Representation \| Purpose \| Contents \|
	\|---\|---\|---\|
	\| Core benchmark \| Evaluation and reference \| Task/question specification, available artifact handles, canonical ground-truth toolchain, structured expected answer \|
	\| Full conversations \| Trajectory analysis and supervised examples \| Complete agent episodes: question → tool calls / observations → terminal answer, for single- and multi-agent settings \|
	\| Turn-level SFT \| Training and role-specific analysis \| One assistant target per row, with the required context and metadata \|

	\| Release statistics \| Count \|
	\|---\|---:\|
	\| Unique benchmark tasks \| 2,921 \|
	\| Training tasks (per full agent form) \| 2,629 \|
	\| Test tasks (per full agent form) \| 292 \|
	\| Single-agent turn-level rows \| 20,453 \|
	\| Multi-agent role-level rows \| 35,195 \|
	\| Total turn-level rows \| 55,648 \|
	\| Terminal targets across both full agent forms \| 5,842 \|

	Training and test partitions are disjoint by task ID, source sample, declared path, normalized routing content, and complete model-visible single-agent trajectory. Within a split, exact repeated episodes are weighted by inverse complete-trajectory multiplicity via `training_weight`.

	## 🚀 Quick start

	Load the released configurations with the Hugging Face `datasets` library:

	```python
	from datasets import load_dataset

	REPO = "vimageiitb/GeoDisaster"

	core = load_dataset(REPO, "core")
	eval_inputs = load_dataset(REPO, "evaluation_inputs")

	single = load_dataset(REPO, "single_agent")
	multi = load_dataset(REPO, "multi_agent")

	single_sft = load_dataset(REPO, "single_agent_sft")
	ga_sft = load_dataset(REPO, "multi_agent_sft_ga")
	```

	Other specialist role-SFT files are available in the dataset repository under `data/multi_agent/sft_train/` and `data/multi_agent/sft_test/`. You can also read any released `.jsonl` file directly, one record per line.

	Choosing a view: use `core` for reference specifications, `single_agent` / `multi_agent` for full episodes, SFT views for assistant-turn supervision, and `evaluation_inputs` for agent-visible held-out tasks. Never give reference-bearing test files to the evaluated agent.

	## 🗂️ Repository layout

	```text
	data/data/
	├── core/
	│ ├── geodisaster_2921_{train,test}_source.jsonl # evaluator/reference tasks
	│ ├── geodisaster_2921_test_inputs.jsonl # gold-free public test inputs
	│ └── scoring_policy_43.json # active structured-answer policy
	├── single_agent/
	│ ├── {train_conversations,test_reference_conversations}.jsonl
	│ ├── {train_turns,test_turns}.jsonl
	│ └── single_agent_system_prompt.md
	└── multi_agent/
	├── {train,test}_conversations.jsonl
	├── sft_train/{orc,ga,pa,vra}_{train,eval}.jsonl
	├── sft_test/{orc,ga,pa,vra}_{train,eval}.jsonl
	└── tool_ownership_and_prompt_context.md

	source_assets/ # bundled permissible EO/GIS inputs
	release_tools/
	├── fetch_sen1floods11_subset.py # pinned SAR input installer
	└── build_isolated_test_bundle.py # held-out evaluation bundle
	user-guide-final.md # authoritative benchmark/runtime guide
	ATTRIBUTIONS.md # upstream licensing and provenance
	SHA256SUMS # distributable archive checksums
	```

	<details>
	<summary><b>🔍 Record IDs, shared fields, and dataset schema</b></summary>

	### Record identity

	Every representation shares three identity fields:

	- `id`: `<family>/<sample>/<question_type>` — the unique linking key across benchmark forms.
	- `sample_id`: source sample identifier.
	- `question_type`: one of the 43 question types.

	For example, `deforestation/000086/multi_year_loss_attribution` identifies the same task across its core specification, conversations, and SFT turns. Source input files use repository-relative paths such as `source_assets/deforestation/000086/masks/lossyear.tif`. Non-distributed teacher replay outputs, if referenced, use portable `generated-artifact://` logical locators.

	### Multi-agent conversation fields

	Files: `data/multi_agent/{train,test}_conversations.jsonl`

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `raw_question` \| string \| Plain question used for active orchestrator/specialist prompts \|
	\| `question` \| string \| Stored evaluation/display context: question, input handles, and answer schema; active prompts use `raw_question` with sanitized handle cards \|
	\| `initial_artifacts` \| object \| Declared input handle cards with path, label, existence, and optionality \|
	\| `available_initial_handles` \| list \| Handles available to the task \|
	\| `plan` \| object \| ORC plan with goal and `{id, agent, subtask, needs}` steps \|
	\| `multiagentic_gt_chain` \| list \| Full USER ↔ ORC ↔ specialist trajectory, actions, and observations \|
	\| `executions` \| list \| Per-step `{action, tool_output}` execution log \|
	\| `expected_final_answer` \| object \| Audit copy of the reference answer; the core record is authoritative \|
	\| `final_answer_ref` \| string \| Terminal transport token (`ANSWER_JSON:answer_1`) \|
	\| `resolved_final_answer` \| object \| Schema-valid answer projected from worker evidence \|
	\| `resolved_answer_sha256` \| string \| Canonical-JSON checksum linking derived records \|
	\| `resolved_answer_provenance` \| string \| Export provenance: full evidence or validated compact-trace cache \|
	\| `answer_support` \| object \| Supporting evidence metadata \|
	\| `pure_toolchain_sim_status` \| string \| Toolchain replay status \|

	### Other released record types

	\| Path \| Main fields \|
	\|---\|---\|
	\| `data/core/*_source.jsonl` \| `question`, `initial_artifacts`, `available_initial_handles`, `gt_chain`, `expected_final_answer`, `source_replay`, `path_summary`, `split_seed` \|
	\| `data/single_agent/{train_conversations,test_reference_conversations}.jsonl` \| `messages` (`role`, `content`), `tool_sequence`, `final_answer_ref`, `resolved_answer_sha256` \|
	\| `data/single_agent/{train_turns,test_turns}.jsonl` \| `messages`, `kind`, `assistant_action`, `assistant_turn_index` \|
	\| `data/multi_agent/sft_{train,test}/*.jsonl` \| `messages`, `role`, `step_id`, `turn_no`, `kind` \|

	Each role-SFT row contains the exact system-prompt snapshot associated with its target. The data package also includes the standalone single-agent prompt and the multi-agent role/tool prompt context. Executable inference-time prompt builders are authoritative in the [code repository](https://github.com/VIMAGE-IITB/GeoDisaster).

	</details>

	## 🛡️ Evaluation without answer leakage

	Only `data/core/geodisaster_2921_test_inputs.jsonl` should be exposed as test records to an evaluated agent. The canonical reference data, `test_source`, multi-agent test conversations, and single-agent test-reference conversations contain evaluator-only targets.

	For a filesystem-isolated evaluation, create a separate root containing just the 292 held-out test tasks and their permitted inputs:

	```bash
	python3 release_tools/build_isolated_test_bundle.py \
	--output /path/outside/repository/eval_bundle

	export GEODISASTER_DATASET_ROOT=/path/outside/repository/eval_bundle
	export GEODISASTER_STRICT_DATA_ROOT=1
	```

	The bundle builder rejects private path declarations; omits `gt/`, `ground_truth/`, `hidden/`, and trajectory products when a declared input is a directory; and writes a SHA-256 manifest. Strict runtime mode prevents fallback searches through the development repository. Keep gold scoring in a separate process or mount; do not mount the full dataset/development repository into the agent sandbox.

	The active structured-answer scoring policy is `data/core/scoring_policy_43.json` (`geodisaster_structured_answer_v2`). Older v1 policy text inside `correction_provenance.publication_contract` is historical publication provenance, not the active evaluation policy. Values in `correction_provenance.evidence` are opaque build labels, not runtime paths or agent inputs.

	## 🧑‍🤝‍🧑 Agent settings and tool ownership

	The released trajectories cover two settings:

	- Single-agent: one agent plans, invokes tools, and finishes the task.
	- Multi-agent: an orchestrator (ORC) assigns work to GA, PA, and VRA specialists.

	\| Role \| Tools / responsibility \|
	\|---\|---\|
	\| ORC — Orchestrator \| Planning and delegation; owns no geospatial tools \|
	\| GA — Geospatial Agent \| `ReadData`, `PrepareScene`, `MeasureArea`, `ExtractObjects`, `MapFlood`, `RenderMap` \|
	\| PA — Planning/Analysis Agent \| `ComputeStats`, `CompareSources`, `DetectChange`, `ClassifyChange`, `ScoreHazard`, `EstimateExposure`, `RankRegions`, `PlanRoutes`, `CompareRoutes` \|
	\| VRA — Visual Reasoning Agent \| `AssessDamage`, `DescribeFlood`, `CompareScenes` \|

	Terminal-answer contract: all 5,842 full-episode terminal targets use the reference-only token `ANSWER_JSON:answer_1`. The runtime creates `answer_1` only from a schema-valid projection of worker evidence, resolves it at termination, and passes the resulting JSON to the evaluator. The token is transport, not a second answer format; agents do not emit the large resolved JSON themselves. Unknown or missing references fail explicitly, with no ground-truth fallback in live execution.

	- Multi-agent ORC ends with `Terminate`; the single-agent baseline ends with `FinalAnswer`. Both carry the same answer reference.
	- `TaskComplete` closes a specialist subgoal; it does not transmit the terminal answer.
	- Single-agent training support covers SFT and deterministic/live evaluation; GRPO/RCEA training is supported only for the four multi-agent roles.

	<details>
	<summary><b>🧪 Example task and trajectory records</b></summary>

	Core task specification (abridged for readability):

	```json
	{
	"id": "deforestation/000086/multi_year_loss_attribution",
	"sample_id": "000086",
	"question_type": "multi_year_loss_attribution",
	"question": {"handle": "question_1", "text": "… identify the year with the largest forest loss …"},
	"gt_chain": [
	{"tool": "ReadData", "args": {"input": "forest_loss_history_1"}},
	{"tool": "ComputeStats", "args": {}}
	],
	"expected_final_answer": {"year": 2016, "loss_ha": 201.039833}
	}
	```

	This example's `initial_artifacts` include a declared `forest_loss_history_1` handle pointing to `source_assets/deforestation/000086/masks/lossyear.tif`.

	Single-agent conversation (abridged; the stored record includes full messages):

	```json
	{
	"id": "deforestation/000086/multi_year_loss_attribution",
	"tool_sequence": ["ReadData", "PrepareScene", "ComputeStats", "DetectChange"],
	"messages": [
	{"role": "user", "content": "QUESTION … AVAILABLE INPUT HANDLES …"},
	{"role": "assistant", "content": "{\"action\":{\"tool\":\"ReadData\",\"args\":{\"input\":\"forest_loss_history_1\"}}"},
	{"role": "assistant", "content": "{\"action\":{\"tool\":\"FinalAnswer\",\"answer_ref\":\"ANSWER_JSON:answer_1\"}}"}
	]
	}
	```

	The corresponding multi-agent example uses the same task `id`, and additionally includes `plan`, `multiagentic_gt_chain`, `executions`, `resolved_final_answer`, and reference-answer metadata.

	</details>

	## 🛰️ Source assets and Sen1Floods11 setup

	Bundled files in `source_assets/` cover redistributable EO/GIS inputs, while the selected Sen1Floods11 S1/JRC payload is intentionally not included in the thin public release.

	To download the pinned 100-chip subset from the official upstream bucket and verify the required files:

	```bash
	python3 release_tools/fetch_sen1floods11_subset.py
	python3 release_tools/fetch_sen1floods11_subset.py --verify-only
	```

	Important reproducibility details:

	- The 500 `sen1_flood` task records describe a hydrated execution contract; `exists: true` does not mean every source file is already present in the downloaded archive.
	- The installer fetches and verifies 200 upstream S1/JRC source paths and produces 100 portable execution rasters (300 hydrated files in total).
	- `SHA256SUMS` covers the distributed thin archive but excludes locally hydrated files under `source_assets/sen1_flood/`; the installer verifies those separately using its pinned upstream manifest.
	- Dataset input paths are repository-relative. Follow `user-guide-final.md` for task-specific requirements and correct execution semantics.

	## ⚖️ Licensing and attribution

	This is a mixed-license dataset. Its Hugging Face license field is therefore `other`, not a blanket CC-BY-4.0 declaration.

	\| Component \| Applicable terms \|
	\|---\|---\|
	\| Project-authored questions, annotations, and tool-use chains \| CC BY 4.0 \|
	\| Redistributed `source_assets/` \| Retain their respective upstream licenses and conditions \|
	\| xBD / xView2-derived assets \| Non-commercial restrictions from their original sources \|
	\| Selected Sen1Floods11 subset \| Retrieved from the official source; subject to upstream terms \|

	Read `ATTRIBUTIONS.md` for upstream attributions and component-specific licenses before reusing or redistributing source imagery and geospatial files.

	## 📖 Documentation and paper

	The dataset package includes `user-guide-final.md`, the authoritative guide covering the benchmark contract, all 43 question types, runtime/evaluation behavior, and release setup. For executable inference-time code and additional project documentation, see the [GeoDisaster GitHub repository](https://github.com/VIMAGE-IITB/GeoDisaster).

	Paper: GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence — accepted at BMVC 2026.

	Authors: Maram Hasan, Aman Verma, Savitra Roy, Hariseetharam Gunduboina, Daksh Jain, Muhammad Haris Khan, Subhasis Chaudhuri, and Biplab Banerjee.

	[📄 Read PDF](https://arxiv.org/pdf/2606.17246) · [🔎 arXiv abstract](https://arxiv.org/abs/2606.17246) · [💻 Code](https://github.com/VIMAGE-IITB/GeoDisaster)

	### Citation

	If you use GeoDisaster tasks, reference outputs, or trajectories, please cite the accompanying paper:

	```bibtex
	@misc{hasan2026geodisaster,
	title = {GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence},
	author = {Maram Hasan and Aman Verma and Savitra Roy and Hariseetharam Gunduboina and Daksh Jain and Muhammad Haris Khan and Subhasis Chaudhuri and Biplab Banerjee},
	year = {2026},
	eprint = {2606.17246},
	archivePrefix = {arXiv},
	primaryClass = {cs.CV},
	url = {https://arxiv.org/abs/2606.17246},
	note = {Accepted at BMVC 2026}
	}
	```