fuse_proj: Initial operational package for 3 researchers (Pavlenko/Blizno/Moroz)

Multimodal fusion research on StripNet+GTA-UAV proxy:
- 3 independent fusion tracks: condition-aware (A), token/bottleneck (B), role-aware (C)
- Shared interfaces, protocol, dataset audit, baseline benchmarks
- Canonical version-chain references to vault (SPEC, ANALYSIS, TRIAGE)
- Personalized task plans and decision tables for each researcher
- 3 generated DOCX task assignment files with milestones and DoD checklist
- Full modality dropout diagnostics and missing-modality robustness requirements
- Data contract, benchmark registry, experiment tracking infrastructure

Operational documents:
- docs/00_project/: MERIDIAN context, protocol, repository reuse guide, experiment specification
- docs/01_tasks/: Master assignment + 3 individual researcher tracks + joint integration
- docs/02_references/: Core literature, version-chain bases, code maps
- docs/03_codebase_guides/: Existing code snapshots from vault
- scripts/: gen_task_plans.js (DOCX generation), placeholder infrastructure
- vendor_reference/: Snapshots of caption_test, depth_edges_annotate, existing SOFIA/SegModel code
- reports/, results/, experiments/: Shared output structure for all 3 researchers

3 DOCX files generated from gen_task_plans.js (Times New Roman 14pt, GOST format):
- План_заданий_Павленко_БВ.docx (Condition-Aware track, fusion API owner)
- План_заданий_Близно_МВ.docx (Token/Bottleneck track, benchmark owner)
- План_заданий_Мороз_ЕС.docx (Role-Aware track, data contract owner)

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

docs/03_codebase_guides/ANNOTATION_PROJECT_CODE_MAP.md

@@ -0,0 +1,109 @@
+# Карта проекта depth_edges_annotate_worlduav
+
+## 1. Источник
+
+```text
+C:\Users\Lisadminipc\Documents\code\depth_edges_annotate_worlduav
+```
+
+Снимки ключевых файлов находятся в `vendor_reference/depth_edges_annotate_worlduav/`.
+
+## 2. GTA-UAV annotation run
+
+Reference entry point: `scripts/run_gta_uav.py`.
+
+Исходная GTA-UAV структура:
+
+```text
+GTA-UAV-LR/
+├── drone/images/     # UAV, 512x384
+└── satellite/        # satellite, 256x256, возможен alpha channel
+```
+
+Reference output:
+
+```text
+GTA-UAV-LR-aug/
+├── depth/
+├── edge/
+├── segm/
+├── chm/
+├── safetensors/
+└── manifest.json
+```
+
+## 3. Используемые модальности
+
+| View | Geometry key | Segmentation key |
+|---|---|---|
+| Satellite | `chm` | `segm` |
+| UAV | `depth` | `segm` |
+
+`edge` генерируется pipeline, но не входит в primary fusion input этого проекта.
+
+## 4. Segmentation classes
+
+Canonical source: `scripts/seg_classes.py`.
+
+17 classes:
+
+```text
+0 background
+1 building
+2 road
+3 vegetation
+4 water
+5 sand and gravel ground
+6 rocky terrain
+7 farmland
+8 railway
+9 parking lot
+10 sidewalk
+11 bare soil and plowed field
+12 roof and rooftop
+13 sports field and playground
+14 muddy ground and wetland
+15 embankment and levee
+16 swimming pool
+```
+
+При переносе запрещено создавать новый порядок IDs.
+
+## 5. Storage
+
+Для обучения предпочтительны SafeTensors:
+
+| Key | Dtype | Shape | Range |
+|---|---|---|---|
+| `depth` | float16 | `[1,H,W]` | normalized `[0,1]` |
+| `chm` | float16 | `[1,H,W]` | normalized `[0,1]` |
+| `segm` | uint8 | `[1,H,W]` | IDs `[0,16]` |
+| `edge` | float16 | `[1,H,W]` | normalized `[0,1]` |
+
+PNG используется только для визуальной инспекции.
+
+## 6. Data validation перед training
+
+Для каждой view вычислить:
+
+- число найденных SafeTensors;
+- число отсутствующих файлов;
+- shape mismatch count;
+- NaN/Inf count;
+- min/max/mean/std geometry;
+- class histogram segmentation;
+- valid fraction;
+- долю constant maps;
+- совпадение stem с RGB.
+
+Результат сохранить в `reports/joint/DATA_VALIDATION.json` и кратко описать в `ENVIRONMENT_AUDIT.md`.
+
+## 7. Что не копировать в training code
+
+- Model inference implementation генераторов.
+- Веса DA3, segmentation и CHM generators.
+- Sequential model loading logic.
+- Visualization palette как model input.
+- Хардкодированные source/output paths.
+
+Новый проект читает готовые annotations. Генерация повторяется во внешнем проекте только при отсутствии или повреждении данных.