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Pikaliov 2c6a00a4ca 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>

2026-06-11 17:16:57 +03:00

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Карта проекта depth_edges_annotate_worlduav

1. Источник

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:

GTA-UAV-LR/
├── drone/images/     # UAV, 512x384
└── satellite/        # satellite, 256x256, возможен alpha channel

Reference output:

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:

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.

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