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:
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:
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:
# 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 file for details.
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