--- 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 **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). ## ๐ŸŒ‹ Five disaster task families | Task family | Dataset key | What it evaluates | |---|---|---| | ๐ŸŒฒ **Deforestation Monitoring** | `deforestation` | Forest-loss detection, multi-year attribution, and change measurement from EO layers | | ๐ŸŒช๏ธ **Multi-Hazard Analysis** | `multi_hazard` | Hazard statistics, cross-layer comparison, spatial exposure, and region prioritization | | ๐Ÿš๏ธ **Building-Damage Assessment** | `xbd_damage` | Damage assessment and inventory from building footprints and disaster evidence | | ๐Ÿ›ฃ๏ธ **Flood-Safe Routing** | `routing` | Network reasoning, hazard-aware route planning, and route comparison | | ๐ŸŒŠ **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. ## ๐Ÿ“ฆ 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 โ†’ tool calls / observations โ†’ 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`. ## ๐Ÿš€ 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. ## ๐Ÿ—‚๏ธ Repository layout ```text data/data/ โ”œโ”€โ”€ core/ โ”‚ โ”œโ”€โ”€ geodisaster_2921_{train,test}_source.jsonl # evaluator/reference tasks โ”‚ โ”œโ”€โ”€ geodisaster_2921_test_inputs.jsonl # gold-free public test inputs โ”‚ โ””โ”€โ”€ scoring_policy_43.json # active structured-answer policy โ”œโ”€โ”€ single_agent/ โ”‚ โ”œโ”€โ”€ {train_conversations,test_reference_conversations}.jsonl โ”‚ โ”œโ”€โ”€ {train_turns,test_turns}.jsonl โ”‚ โ””โ”€โ”€ single_agent_system_prompt.md โ””โ”€โ”€ multi_agent/ โ”œโ”€โ”€ {train,test}_conversations.jsonl โ”œโ”€โ”€ sft_train/{orc,ga,pa,vra}_{train,eval}.jsonl โ”œโ”€โ”€ sft_test/{orc,ga,pa,vra}_{train,eval}.jsonl โ””โ”€โ”€ tool_ownership_and_prompt_context.md source_assets/ # bundled permissible EO/GIS inputs release_tools/ โ”œโ”€โ”€ fetch_sen1floods11_subset.py # pinned SAR input installer โ””โ”€โ”€ 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 ```
๐Ÿ” Record IDs, shared fields, and dataset schema ### Record identity Every representation shares three identity fields: - `id`: `//` โ€” 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 โ†” ORC โ†” 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).**
## ๐Ÿ›ก๏ธ 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. ## ๐Ÿง‘โ€๐Ÿคโ€๐Ÿง‘ 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** โ€” Orchestrator | Planning and delegation; owns no geospatial tools | | **GA** โ€” Geospatial Agent | `ReadData`, `PrepareScene`, `MeasureArea`, `ExtractObjects`, `MapFlood`, `RenderMap` | | **PA** โ€” Planning/Analysis Agent | `ComputeStats`, `CompareSources`, `DetectChange`, `ClassifyChange`, `ScoreHazard`, `EstimateExposure`, `RankRegions`, `PlanRoutes`, `CompareRoutes` | | **VRA** โ€” 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**.
๐Ÿงช Example task and trajectory records **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": "โ€ฆ identify the year with the largest forest loss โ€ฆ"}, "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 โ€ฆ AVAILABLE INPUT HANDLES โ€ฆ"}, {"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.
## ๐Ÿ›ฐ๏ธ 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. ## โš–๏ธ 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. ## ๐Ÿ“– 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* โ€” **accepted at BMVC 2026**. **Authors:** Maram Hasan, Aman Verma, Savitra Roy, Hariseetharam Gunduboina, Daksh Jain, Muhammad Haris Khan, Subhasis Chaudhuri, and Biplab Banerjee. **[๐Ÿ“„ Read PDF](https://arxiv.org/pdf/2606.17246)** ยท **[๐Ÿ”Ž arXiv abstract](https://arxiv.org/abs/2606.17246)** ยท **[๐Ÿ’ป 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} } ```