PlanetScope-py

Professional Python Library for Satellite Imagery Analysis

A comprehensive Python library for PlanetScope satellite imagery discovery, analysis, and processing. Designed for researchers, GIS analysts, and Earth observation professionals.

v4.1.0

Latest Version

20+

Core Modules

MIT

Open Source

View on GitHub Install via PyPI

Ground Station

➤ planetscope-py

├─ ROI defined

├─ scenes: 247

└─ processing...

ACTIVE

API: 247/500

SCANNING

IMAGING

PROCESSING

Data Center

GPKG

Export Complete

2.4MB • 247 scenes

Project Overview

PlanetScope-py is a professional Python library developed to address critical gaps in satellite imagery analysis. It provides specialized tools for PlanetScope satellite data discovery, processing, and sophisticated spatial-temporal analysis.

Advanced Scene Discovery

Automated scene discovery through Planet API integration with flexible filtering

Spatial-Temporal Analysis

Multi-algorithm density calculations and temporal pattern analysis

Professional Integration

Cross-platform compatibility with QGIS, ArcGIS, and standard GIS formats

System Architecture Overview

Challenges Addressed

• Limited specialized tools for PlanetScope imagery analysis
• Complex spatial-temporal density calculations
• Fragmented workflows across different platforms
• Manual metadata processing and scene selection
• Lack of integrated visualization capabilities

Our Solution

• Comprehensive Python library with 20+ specialized modules
• Multi-algorithm spatial density analysis framework
• Unified workflow from discovery to analysis
• Automated metadata processing and filtering
• Professional visualization and export capabilities

Primary Use Cases

Scene Inventory & Metadata Analysis

Comprehensive scene discovery capabilities with flexible output options for research planning, coverage analysis, and metadata summarization.

Planet API query with advanced filtering

GeoPackage export with embedded metadata

QGIS integration for immediate visualization

Spatial-Temporal Density Analysis

Sophisticated analytical capabilities for understanding scene distribution patterns and temporal availability across study areas.

Multi-algorithm spatial density calculations

Grid-based temporal pattern analysis

Integrated spatial-temporal insights

System Architecture

Complete system architecture showing modular design with core infrastructure, API integration, and analysis engines

Core Infrastructure

• Authentication system (auth.py)
• Configuration management (config.py)
• Utility functions (utils.py)
• Exception handling (exceptions.py)

API Integration

• Planet API integration (query.py)
• Metadata processing (metadata.py)
• Asset management (asset_manager.py)
• Rate limiting & error recovery

Analysis Engines

• Spatial analysis (density_engine.py)
• Temporal analysis (temporal_analysis.py)
• Adaptive grid (adaptive_grid.py)
• Visualization (visualization.py)

Modular Design Benefits

Code Reusability

70% overlap between use cases with shared core functions, eliminating redundancy and ensuring consistency.

Unified API Design

Consistent interface across all modules with standardized authentication, configuration, and error handling.

Extensibility

Modular architecture allows easy addition of new analysis algorithms and output formats.

Professional Standards

Comprehensive testing, documentation, and quality assurance across all components.

Key Features & Capabilities

Scene Discovery & API Integration

• Automated Planet API queries with rate limiting
• Advanced filtering (cloud cover, date range, quality)
• Multiple authentication methods
• Robust error handling and recovery
• Batch processing capabilities

Spatial Analysis Engine

• Multi-algorithm density calculations
• Rasterization method for efficiency
• Vector overlay for precision
• Adaptive grid for memory optimization
• Performance-optimized methods

Temporal Analysis

• Grid-based temporal pattern analysis
• Acquisition frequency assessment
• Gap detection algorithms
• Temporal coverage metrics
• Integrated spatial-temporal insights

Data Export & Integration

• Professional GeoPackage export
• Multi-schema attribute support
• GeoTIFF export with QGIS styling
• Cross-platform compatibility
• Standardized metadata schemas

Visualization & Reporting

• Professional visualization suite
• Interactive mapping capabilities
• Statistical analysis and plotting
• Multi-panel summary reports
• Publication-ready outputs

Asset Management

• Quota monitoring and alerts
• Parallel downloads with retry logic
• ROI clipping support
• Data usage warnings
• Interactive download management

Performance Specifications

100km²

Maximum ROI Size

30m resolution

10 min

Processing Time

50km² density analysis

8GB

Memory Efficiency

Typical workflows

MIT

Open Source

Professional license

Development Methodology

Six-phase development approach with iterative testing and documentation

Development Phases

Foundation & Core Infrastructure

Authentication system, core utilities, configuration management, exception handling

Duration: 1-2 weeks

Planet API Integration

Scene discovery, metadata processing, rate limiting, error recovery

Duration: 1-2 weeks

Spatial Analysis Engine

Three algorithms, performance optimization, memory management, coordinate system fixes

Duration: 2-3 weeks

Temporal Analysis & Grid Compatibility

Temporal analysis engine, grid-based patterns, FAST & ACCURATE methods

Duration: 2-3 weeks

Visualization & Export Systems

Professional visualization, GeoTIFF export, QGIS integration, statistical analysis

Duration: 1-2 weeks

Asset Management & Advanced Features

Asset management, quota monitoring, async downloads, interactive features

Duration: 2-3 weeks

Development Strategy

Modular Architecture

70% code reusability between use cases with shared core functions and consistent API design.

Iterative Development

Six-phase approach with comprehensive testing and documentation at each stage.

Professional Standards

Continuous integration, quality assurance, and industry best practices throughout development.

User-Centered Design

Focus on accessibility, clear documentation, and seamless GIS integration workflows.

Technical Implementation Highlights

20+

Specialized Modules

Comprehensive coverage of satellite imagery analysis workflows

8-9 weeks

Development Timeline

Structured six-phase approach with parallel documentation

3 algorithms

Density Methods

Rasterization, vector overlay, and adaptive grid approaches

Technical Specifications

Installation

# Standard Installation

pip install planetscope-py

# Enhanced Installation

pip install planetscope-py[all]

Quick Start

from planetscope_py import PlanetScopeQuery

# Initialize query system

query = PlanetScopeQuery()

# Search for scenes

results = query.search_scenes(...)

Technology Stack

Python 3.10+

Planet API

NumPy

Pandas

Shapely

GeoPandas

Rasterio

Matplotlib

Folium

QGIS

GeoPackage

pytest

System Requirements

• Python 3.10 or later versions
• Windows, macOS, or Linux operating systems
• Minimum 8GB RAM for typical workflows
• Planet API key for satellite data access
• Internet connection for API queries

Core Dependencies

• requests>=2.32.4 (HTTP requests)
• shapely>=2.1.0 (Geometric operations)
• numpy>=2.2.0 (Numerical computing)
• pandas>=2.2.3 (Data manipulation)
• geopandas>=1.0.1 (Spatial data operations)
• rasterio>=1.4.3 (Raster processing)

Scene Discovery & Analysis Results

Comprehensive Scene Inventory

Successfully implemented automated scene discovery with comprehensive metadata extraction. The system efficiently processes large datasets and exports structured data for GIS analysis.

• Interactive metadata tables with 15+ attributes
• GeoPackage export with embedded spatial data
• QGIS integration for immediate visualization
• Automated filtering and scene selection

Scene Discovery Results - Metadata Analysis

Click to enlarge

Spatial-Temporal Density Analysis

Click to enlarge

Multi-Algorithm Spatial Analysis

Advanced spatial density calculations using three complementary algorithms: rasterization for efficiency, vector overlay for precision, and adaptive grid for optimization.

• Heat map visualization with coordinate correction
• Grid-based temporal pattern analysis
• Performance-optimized algorithms
• Statistical density summaries

System Architecture Results

Modular Design Implementation

Successfully implemented a modular architecture with 70% code reusability between use cases. The system provides unified API design with consistent authentication and configuration.

• 20+ specialized Python modules
• Cross-platform GIS integration
• Professional visualization capabilities
• Comprehensive error handling

System Architecture Implementation Results

Click to enlarge

Performance & Validation Results

100km²

Maximum ROI Size

30m resolution processing

10 min

Processing Time

50km² density analysis

8GB

Memory Efficiency

Optimized workflows

MIT

Open Source

Professional license