Intermediate Examples
=====================

Build on basic concepts with more sophisticated Earth Engine workflows and analysis techniques.

.. toctree::
   :maxdepth: 2
   :caption: Intermediate Examples:

   time-series-analysis
   image-collection-filtering
   vector-operations

Overview
--------

These intermediate examples demonstrate:

* **Time Series Analysis**: Working with temporal data and change detection
* **Collection Filtering**: Advanced techniques for managing large datasets
* **Vector Operations**: Spatial analysis with geometric data
* **Multi-temporal Processing**: Analyzing changes over time
* **Quality Assessment**: Filtering and processing based on data quality

Prerequisites
-------------

Before working with intermediate examples:

* Complete all basic examples
* Understanding of remote sensing concepts
* Familiarity with Earth Engine data structures
* Knowledge of statistical analysis concepts
* Basic understanding of spatial analysis

Key Concepts
------------

**Temporal Analysis**
  * Time series creation and analysis
  * Seasonal decomposition
  * Trend detection and change analysis
  * Multi-year comparisons

**Advanced Filtering**
  * Metadata-based filtering
  * Quality assessment integration
  * Custom filter functions
  * Multi-criteria filtering

**Spatial Operations**
  * Vector-raster interactions
  * Geometric operations
  * Zonal statistics
  * Spatial relationship analysis

Example Progression
-------------------

**Start Here**: :doc:`time-series-analysis`
  * Learn temporal data processing
  * Understand collection reduction
  * Master time series visualization
  * Practice change detection

**Then**: :doc:`image-collection-filtering`
  * Advanced collection management
  * Quality-based filtering
  * Multi-sensor integration
  * Efficient data processing

**Finally**: :doc:`vector-operations`
  * Spatial analysis workflows
  * Vector-raster integration
  * Geographic processing
  * Regional statistics

Learning Objectives
-------------------

By completing these examples, you will:

✅ **Master Time Series Analysis**
* Process multi-temporal datasets
* Detect changes and trends
* Create temporal composites
* Analyze seasonal patterns

✅ **Advanced Data Management**
* Filter large collections efficiently
* Implement quality assessment
* Handle multi-sensor data
* Optimize processing workflows

✅ **Spatial Analysis Skills**
* Work with vector data
* Perform geometric operations
* Calculate zonal statistics
* Integrate multiple data types

Common Patterns
---------------

**Error Handling for Large Operations**

.. code-block:: python

   import ee
   
   def safe_large_operation(collection, operation_func):
       """Safely perform operations on large collections."""
       try:
           # Check collection size
           size = collection.size().getInfo()
           if size > 1000:
               print(f"Warning: Large collection ({size} images)")
           
           # Perform operation
           result = operation_func(collection)
           return result
           
       except ee.EEException as e:
           print(f"Earth Engine error: {e}")
           return None
       except Exception as e:
           print(f"General error: {e}")
           return None

**Progress Monitoring**

.. code-block:: python

   def monitor_processing_progress(operation_name, total_steps):
       """Monitor progress of long-running operations."""
       print(f"Starting {operation_name}...")
       
       def progress_callback(current_step):
           percent = (current_step / total_steps) * 100
           print(f"\r{operation_name}: {current_step}/{total_steps} ({percent:.1f}%)", end="")
       
       return progress_callback

**Batch Processing Pattern**

.. code-block:: python

   def process_in_batches(collection, process_func, batch_size=100):
       """Process large collections in manageable batches."""
       total_size = collection.size().getInfo()
       results = []
       
       for i in range(0, total_size, batch_size):
           print(f"Processing batch {i//batch_size + 1}/{(total_size//batch_size) + 1}")
           
           batch = collection.limit(batch_size, i)
           batch_result = process_func(batch)
           results.append(batch_result)
       
       return results

Performance Tips
----------------

**Optimize Collection Filtering**
* Apply spatial filters before temporal filters
* Use metadata filters to reduce data volume
* Cache frequently used collections

**Efficient Computation**
* Use appropriate scale parameters
* Limit analysis regions when possible
* Leverage Earth Engine's parallel processing

**Memory Management**
* Process data in chunks for large analyses
* Use server-side operations when possible
* Clear unused variables in long scripts

Troubleshooting Common Issues
-----------------------------

**Memory Errors**

.. code-block:: python

   # Reduce memory usage
   def reduce_memory_usage(image):
       """Reduce memory usage for large images."""
       return image.select(['B4', 'B3', 'B2'])  # Select only needed bands
   
   # Process smaller regions
   def process_by_tiles(large_region, tile_size=0.1):
       """Process large regions by breaking into tiles."""
       # Implementation for tiled processing
       pass

**Timeout Issues**

.. code-block:: python

   import time
   
   def retry_operation(operation_func, max_retries=3, delay=5):
       """Retry operations that might timeout."""
       for attempt in range(max_retries):
           try:
               return operation_func()
           except Exception as e:
               if attempt < max_retries - 1:
                   print(f"Retry {attempt + 1} after {delay} seconds...")
                   time.sleep(delay)
               else:
                   raise e

**Collection Size Limits**

.. code-block:: python

   def check_collection_limits(collection):
       """Check if collection exceeds processing limits."""
       size = collection.size().getInfo()
       
       if size > 5000:
           print(f"Warning: Very large collection ({size} images)")
           print("Consider additional filtering or batch processing")
           return False
       
       return True

Next Steps
----------

After completing intermediate examples:

1. :doc:`../advanced/index` - Tackle complex workflows
2. Apply concepts to your own research projects
3. Explore Earth Engine's advanced features
4. Consider performance optimization techniques

.. note::
   Intermediate examples may require more processing time and computational resources than basic examples. Start with small test areas and time periods.

.. tip::
   Keep the Earth Engine documentation handy as you work through these examples. The complexity increases significantly from basic examples.