README.md

metadata

title: DDA Change Detection (Dev)
emoji: 🛰️
colorFrom: gray
colorTo: green
sdk: docker
app_port: 7860

DDA Change Detection — Development

Satellite / drone change detection web app for the DDA Scope of Work. This repository tracks dev-only features (APP_MODE=dda): image library, GeoTIFF comparison, async jobs, geo-referenced regions, reports, and PDF export.

New to the project? Start with DEV_SETUP.md — step-by-step install and run instructions for your machine.

Environment	URL
Dev Space (HF)	https://coderuday21-satdetect-dev.hf.space
Local	http://127.0.0.1:8000 after python run.py

Quick start

git clone https://github.com/Uday-at-Vedang/Change-Detection-DEV.git
cd Change-Detection-DEV
python -m venv venv
venv\Scripts\activate          # Windows
# source venv/bin/activate     # macOS/Linux
pip install -r requirements.txt
python run.py

1. Add GeoTIFFs to library_sources/2025/ (or other year folders).
2. Open Image Library → Refresh.
3. Use Change Detection to compare T1 vs T2.

Full details: DEV_SETUP.md · SOW plan: docs/IMPLEMENTATION_PLAN_DDA.md · HF deploy: DEPLOYMENT.md

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Satellite Change Detection — Standalone Web App

Standalone web application for satellite image change detection with database storage and a clean, modern UI. Opens directly on the detection page — no sign-in required.

Features

• DDA dev UI — Library, comparison, jobs, reports, PDF (when APP_MODE=dda)
• Direct access — upload and run detection immediately (no login)
• Database — SQLite (or set DATABASE_URL for PostgreSQL); stores detection runs
• Change detection — AdaptFormer deep learning + hybrid / difference methods
• GeoTIFF library — Year-folder library, 5 GB upload limit, lat/lng on regions
• Object classification — Changed regions labeled (Water, Vegetation, Building, Road, etc.)
• History & reports — Past runs, PDF export, email notifications

Setup (legacy summary)

See DEV_SETUP.md for the complete guide. Minimal steps:

1. Create a virtual environment (recommended)

   cd change_detection_webapp
   python -m venv venv
   source venv/bin/activate   # Windows: venv\Scripts\activate
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Run the app

   python run.py
   # or: uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
   

4. Open http://localhost:8000 in your browser.

First run

• The SQLite DB and data/ (overlay images) are created automatically on first use.
• DDA mode: place images in library_sources/YYYY/ and use the Library tab.
• Legacy mode: upload Before/After PNGs on the detection page.
• AdaptFormer model downloads from Hugging Face on first detection (~500 MB).

Configuration

• DDA mode: APP_MODE=dda (default when using run.py). Copy .env.example → .env for optional overrides.
• Database: set DATABASE_URL (e.g. postgresql://user:pass@host/db) to use another DB; otherwise SQLite under data/satellite_app.db is used.
• JWT: set SECRET_KEY via env in production.
• Email: EMAIL_API_URL or SMTP_USER / SMTP_PASS — see .env.example.

Project layout

change_detection_webapp/
├── app/
│   ├── main.py              # FastAPI app, routes
│   ├── dda/                 # DDA dev modules (library, jobs, reports)
│   └── detection_engine.py  # Change detection pipeline
├── static/js/dda/           # DDA frontend
├── templates/index_dda.html # DDA dev UI
├── library_sources/         # Local GeoTIFF library (year folders)
├── docs/IMPLEMENTATION_PLAN_DDA.md
├── DEV_SETUP.md             # Colleague setup guide
├── requirements.txt
└── run.py                   # Local launcher (APP_MODE=dda)

API (DDA highlights)

• GET /health — app mode and version
• GET /api/dda/local/images — library image list
• POST /api/dda/jobs — queue async detection
• GET /api/dda/reports/{id}/pdf — PDF download
• GET /dda/reports/{id} — browser report page
• POST /api/detect — legacy multipart upload detect
• GET /api/history — detection run history

Hugging Face: Space stuck on “Restarting”

1. Open your Space → Settings → under Build, click Clear build cache → Save. Then trigger a rebuild (push a commit or click Restart).
2. Check Logs (Build logs + App logs) for Python errors or “Killed” (out of memory).
3. In Settings → Hardware, try a slightly larger CPU/memory if available.