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Module: extraction.js

Comprehensive Analysis of extraction.js

Module Name

  • extraction.js

Primary Objectives

  • The primary purpose of this module is to process satellite imagery to identify and extract shorelines. It involves various geospatial operations such as image partitioning, noise removal, and smoothing to derive accurate shorelines from raster data.

Critical Functions

  1. identifyWaterFeature
    • Partitions the raster image using a water index and a threshold function, then reduces the output raster to a vector corresponding to the water body.
  2. removeShorelineNoise
    • Removes noise from the shoreline by keeping only the polygons that have the most intersections with transects.
  3. gaussianKernel
    • Creates a Gaussian kernel controlled by size, mean, and sigma.
  4. linearGaussianFilter
    • Convolves over the list of coordinates producing local averages weighted by the Gaussian kernel to smooth the shoreline.
  5. thresholdingAlgorithm
    • Finds the appropriate thresholds for a given histogram and count of classes.
  6. selectThreshold
    • Selects the threshold function according to the input threshold.
  7. extractShoreline
    • Extracts the raw shoreline of the given image by identifying the water feature and converting it into linestrings.
  8. deriveShoreline
    • Fully derives the shoreline using the satellite image, producing a single smoothed linestring without noisy features.
  9. deriveShorelines
    • Acquires and extracts shorelines given an array of dates.
  10. expandCoastline
    • Expands the given coordinates by a specified distance.
  11. rectifyJunctions
    • Normalizes the junctions of segments.

Key Variables

  • internalBandName: Used to store the name of the water index band.
  • threshold: Stores the threshold value for water feature identification.
  • firstImage: Used to store the first image for registration purposes in deriveShoreline.

Interdependencies

  • Earth Engine (ee): The module heavily relies on Google Earth Engine (GEE) for geospatial operations.
  • Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module.
  • Metadata Module: Used for handling metadata.
  • Utils Module: Provides utility functions like getDate.
  • Material-UI Icons: Imported but not used in the provided code.

Core vs. Auxiliary Operations

  • Core Operations:
    • identifyWaterFeature
    • removeShorelineNoise
    • extractShoreline
    • deriveShoreline
    • deriveShorelines
  • Auxiliary Operations:
    • gaussianKernel
    • linearGaussianFilter
    • thresholdingAlgorithm
    • selectThreshold
    • expandCoastline
    • rectifyJunctions

Operational Sequence

  1. Image Acquisition: Images are acquired based on dates and satellite information.
  2. Water Feature Identification: The water feature is identified using the identifyWaterFeature function.
  3. Shoreline Extraction: The raw shoreline is extracted using extractShoreline.
  4. Noise Removal: Noise is removed from the shoreline using removeShorelineNoise.
  5. Smoothing: The shoreline is smoothed using linearGaussianFilter.
  6. Shoreline Derivation: The final shoreline is derived using deriveShoreline and deriveShorelines.

Performance Aspects

  • Scalability: The module uses Earth Engine, which is designed to handle large-scale geospatial data efficiently.
  • Efficiency: Functions like reduceToVectors and reduceRegion are computationally intensive and should be optimized for performance.

Reusability

  • The module is highly reusable for any geospatial analysis involving shoreline extraction from satellite imagery. Functions are modular and can be adapted for different types of geospatial data and thresholds.

Usage

  • The module is used to process satellite images to extract and analyze shorelines. It can be integrated into larger geospatial analysis pipelines for environmental monitoring, coastal management, and other applications.

Assumptions

  • Image Quality: Assumes that the input satellite images are of sufficient quality for analysis.
  • Threshold Functions: Assumes that the provided threshold functions are appropriate for the given images.
  • Earth Engine Availability: Assumes that Google Earth Engine services are available and accessible.
  • Geospatial Accuracy: Assumes that the geospatial data (coordinates, geometries) are accurate and correctly formatted.

This analysis provides a detailed overview of the extraction.js module, highlighting its primary objectives, critical functions, and operational flow. The module is designed for efficient and accurate shoreline extraction using satellite imagery, leveraging the capabilities of Google Earth Engine.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Request| B[deriveShorelines]
    B -->|Calls| C[deriveShoreline]
    C -->|Calls| D[extractShoreline]
    D -->|Calls| E[identifyWaterFeature]
    D -->|Calls| F[removeShorelineNoise]
    D -->|Calls| G[linearGaussianFilter]
    E -->|Uses| H[selectThreshold]
    H -->|Uses| I[thresholdingAlgorithm]
    G -->|Uses| J[gaussianKernel]
    C -->|Returns| K[Shoreline Data]
    B -->|Returns| L[Shorelines Collection]
    A -->|Receives| L

Module: extraction.js

Comprehensive Analysis of extraction.js

Module Name

  • extraction.js

Primary Objectives

  • The primary purpose of this module is to process satellite imagery to identify and extract shoreline features. It involves various geospatial operations such as image partitioning, noise removal, and smoothing to derive accurate shorelines from raster data.

Critical Functions

  1. identifyWaterFeature
    • Partitions the raster image using a water index and a threshold function, then reduces the output raster to a vector corresponding to the water body.
  2. removeShorelineNoise
    • Removes noise from the shoreline by keeping only the polygons that have the most intersections with transects.
  3. gaussianKernel
    • Creates a Gaussian kernel controlled by size, mean, and sigma.
  4. linearGaussianFilter
    • Convolves over the list of coordinates producing local averages weighted by the Gaussian kernel, resulting in a smoothed list.
  5. thresholdingAlgorithm
    • Finds the appropriate thresholds for a given histogram and count of classes.
  6. selectThreshold
    • Selects the threshold function according to the input threshold.
  7. extractShoreline
    • Extracts the raw shoreline of the given image by identifying the water feature and converting it into linestrings.
  8. deriveShoreline
    • Fully derives the shoreline using the satellite image, resulting in a single smoothed linestring without noisy features.
  9. deriveShorelines
    • Acquires and extracts shorelines given an array of dates.
  10. expandCoastline
    • Expands the given coordinates by a specified distance.
  11. rectifyJunctions
    • Normalizes the junctions of segments.

Key Variables

  • internalBandName: Used to store the name of the water index band.
  • threshold: Stores the threshold value for water feature identification.
  • firstImage: Used to store the first image for registration purposes.
  • maxPixels: Maximum number of pixels to process in certain operations.

Interdependencies

  • Earth Engine (ee): The module heavily relies on Google Earth Engine for geospatial operations.
  • Geodesy Module: Functions like computeBearing and computeDisplacement are imported from the geodesy module.
  • Metadata Module: Used for handling metadata.
  • Utils Module: Provides utility functions like getDate.
  • Material-UI Icons: Used for UI components, though not directly related to core functionality.

Core vs. Auxiliary Operations

  • Core Operations:
    • identifyWaterFeature
    • removeShorelineNoise
    • extractShoreline
    • deriveShoreline
    • deriveShorelines
  • Auxiliary Operations:
    • gaussianKernel
    • linearGaussianFilter
    • thresholdingAlgorithm
    • selectThreshold
    • expandCoastline
    • rectifyJunctions

Operational Sequence

  1. Image Acquisition: Images are acquired based on dates and satellite information.
  2. Water Feature Identification: The water feature is identified using the identifyWaterFeature function.
  3. Shoreline Extraction: The raw shoreline is extracted using extractShoreline.
  4. Noise Removal: Noise is removed from the shoreline using removeShorelineNoise.
  5. Smoothing: The shoreline is smoothed using linearGaussianFilter.
  6. Final Derivation: The final shoreline is derived and returned.

Performance Aspects

  • Scale and Resolution: The scale and optical resolution are critical for accurate shoreline extraction.
  • Thresholding: The choice of thresholding algorithm can significantly impact performance and accuracy.
  • Max Pixels: Operations are constrained by the maximum number of pixels to ensure performance.

Reusability

  • The module is highly reusable for any geospatial analysis involving shoreline extraction from satellite imagery. Functions like gaussianKernel and linearGaussianFilter can be reused in other contexts requiring smoothing and noise reduction.

Usage

  • The module is used to process satellite images to derive shorelines. It can be integrated into larger geospatial analysis systems or used as a standalone tool for shoreline extraction.

Assumptions

  • Image Quality: Assumes that the input satellite images are of sufficient quality and resolution.
  • Threshold Function: Assumes that the provided threshold function is appropriate for the given images.
  • Earth Engine Availability: Assumes that Google Earth Engine services are available and accessible.
  • Geospatial Accuracy: Assumes that the geospatial data (coordinates, geometry) provided is accurate.

This analysis provides a detailed overview of the extraction.js module, highlighting its purpose, key functions, and operational flow.

Flow Diagram [via mermaid]

graph TD
    A[User] -->|Request| B[deriveShorelines]
    B -->|Calls| C[deriveShoreline]
    C -->|Calls| D[extractShoreline]
    D -->|Calls| E[identifyWaterFeature]
    D -->|Calls| F[removeShorelineNoise]
    D -->|Calls| G[linearGaussianFilter]
    E -->|Uses| H[ee.Image]
    E -->|Uses| I[ee.Geometry]
    E -->|Uses| J[ee.Number]
    E -->|Uses| K[ee.Dictionary]
    E -->|Uses| L[ee.List]
    E -->|Uses| M[ee.Feature]
    E -->|Uses| N[ee.FeatureCollection]
    E -->|Uses| O[ee.Reducer]
    E -->|Uses| P[ee.Algorithms]
    F -->|Uses| Q[ee.Geometry]
    F -->|Uses| R[ee.Feature]
    F -->|Uses| S[ee.FeatureCollection]
    G -->|Uses| T[ee.List]
    G -->|Uses| U[ee.Number]
    G -->|Uses| V[ee.Algorithms]
    B -->|Returns| W[ee.FeatureCollection]
    W -->|Response| A