Metadata-Version: 2.4
Name: Satalyze
Version: 0.1.1
Summary: A powerful pipeline for satellite traffic analysis using YOLO and SAHI.
License-Expression: AGPL-3.0-only
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: streamlit>=1.61.1
Requires-Dist: streamlit-folium>=0.27.4
Requires-Dist: folium>=0.2.0
Requires-Dist: numpy>=2.4.6
Requires-Dist: pandas>=3.0.3
Requires-Dist: Pillow>=12.2.0
Requires-Dist: earthengine-api>=1.7.38
Requires-Dist: requests>=2.25.0
Requires-Dist: opencv-python>=4.5.0
Requires-Dist: ultralytics>=8.4.53
Requires-Dist: sahi>=0.12.5
Requires-Dist: matplotlib>=3.4.0
Dynamic: license-file

# Satalyze

Welcome to Satalyze! This library provides an end-to-end automated pipeline for downloading cloud satellite imagery, processing it via computer vision, and generating vehicle tracking telemetry logs.

---

## Installation

You can install the library directly from your terminal:

```bash
pip install satalyze
```

---

## How to Use

There are **two ways** to interact with this pipeline depending on your workflow.

### 1. The Code Pipeline (For Developers)
Import the core orchestrator class directly into your scripts or Jupyter Notebooks to run automated batches:

```python
from satalyze import SatelliteTrafficPipeline

# Initialize the pipeline
traffic_pipeline = SatelliteTrafficPipeline(project_id="satalyze") # REPLACE 'satalyze' with your project ID.

# Run analysis on a target location (Example: King of Prussia Mall Parking Lot)
traffic_pipeline.run(
    center_coordinates=[-75.3947, 40.0890], # [Longitude, Latitude] # Location of your parking lot
    start_date="2018-01-01", # start date of when you want to see images (NAIP data runs every 2-3 years on average)
    end_date="2022-12-31", # End date, unless there is recent NAIP data most recent dates wil be from 2023, important to manage your time window
    limit=2 # how many images you want from this place and timeframe
)
```

### 2. The Interactive App UI (For Visual Navigation)
If you prefer a visual interface, launch the built-in Streamlit dashboard. It includes an interactive map with an integrated address search bar, date pickers, and visual counters:

```python

# Launch the visual dashboard interface
satalyze-start
```

---

## Features
* **Google Earth Engine Integration**: Automatic spatial patch downloads.
* **YOLO + SAHI Machine Learning Layers**: Sliced object detection for ultra-high-resolution targets.
* **Interactive Geocoder Map**: Search for addresses or click anywhere on the globe to grab raw coordinates.
* **Auto-Generating Analytics**: Renders live data tables and futuristic timeline graphics instantly.


## License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0). See the [LICENSE](LICENSE) file for details.

