Python Class for Retrieving GHSL Data

An in-depth guide on developing a GHSL data retrieval class using shapefile-indexed GeoTIFF tiles

Highlights

Shapefile Index Integration: Utilizes a shapefile to spatially index GeoTIFF tiles, ensuring efficient lookup for the correct data tile.
Coordinate and CRS Handling: Implements coordinate conversions and tests whether the input point lies within a tile’s boundary.
Robust Error Handling: Incorporates checks for missing files, out-of-bound points, and unexpected data, allowing for seamless debugging.

Introduction

When working with large-scale geospatial data like the Global Human Settlement Layer (GHSL), it is essential to have a reliable and efficient way of extracting data values from GeoTIFF tiles. GHSL provides detailed layers, such as population and urban built-up data, available at various resolutions. One effective method of handling this data is by using a pre-built shapefile that indexes the individual tiles. This method enables quick determination of which tile contains a given geographic coordinate.

The Python class discussed here demonstrates how to retrieve data from the GHSL layers using such a shapefile index. This implementation includes a method, get_value, that accepts latitude, longitude, layer name, and resolution parameters. Based on these inputs, the class locates the correct GeoTIFF tile, converts the coordinates if necessary, extracts the corresponding pixel value and returns it. Over the course of this guide, we provide detailed explanations of each component and present a robust implementation that can be adapted for various GHSL layers.

Class Structure and Key Components

The class is structured to provide clean separation of functionality, ease of maintenance, and adaptability. The main components of the class include:

a) Initialization and Loading the Index

Upon instantiation, the class requires the directory that holds the GHSL dataset along with the shapefile index. The shapefile index plays a crucial role as it contains spatial references and details for each GeoTIFF tile available on the website. It is essential that this shapefile is up-to-date and correlates accurately with the file naming conventions of the GHSL tiles.

b) Finding the Correct Tile

The core of the retrieval process involves identifying the correct tile that encompasses the target geographical point. By leveraging geospatial libraries, the class iterates through features in the shapefile index, checking if the provided point lies within the tile’s boundaries. Here, a point-in-polygon test (using libraries such as Shapely or Geopandas) is used for verification.

c) GeoTIFF Data Extraction

After identifying the appropriate tile, the class opens the corresponding GeoTIFF file using raster reading libraries (e.g., rasterio). The raster’s geotransform is used to convert geographic coordinates (longitude, latitude) into pixel indices, which helps extract the value located at that specific point. The extracted value is then returned as a floating-point number.

d) Error Handling and Robustness

The implementation features robust error handling to account for scenarios such as:

Points falling outside tile boundaries
Missing or misnamed files in the dataset directory
Issues reading shape files or GeoTIFF tiles

These error checks provoke early detection of issues, thus helping the user troubleshoot or adapt the code to new data sources without significant changes.

The Python Class Implementation

Below is a comprehensive Python class that incorporates the aforementioned functionalities. This class relies on external libraries like rasterio, shapely, and pyshp or geopandas, which are integral in handling the spatial data. Before running the code, ensure that you have installed the required libraries using pip:

For example, run:

# Install necessary libraries
pip install rasterio shapely pyshp geopandas

Here is the Python class:

# Import necessary libraries
import os
import rasterio
import geopandas as gpd
from shapely.geometry import Point
import shapefile  # pyshp to read shapefile index

class GHSLDataRetriever:
    def __init__(self, shapefile_index_path, ghsl_data_dir):
        """
        Initialize the GHSL Data Retriever.
        
        Args:
            shapefile_index_path (str): Path to the shapefile that indexes the GHSL GeoTIFF tiles.
            ghsl_data_dir (str): Path to the directory containing the GHSL GeoTIFF tiles.
        """
        self.shapefile_index_path = shapefile_index_path
        self.ghsl_data_dir = ghsl_data_dir
        # Load shapefile index using pyshp
        try:
            self.sf = shapefile.Reader(shapefile_index_path)
        except Exception as e:
            raise IOError(f"Error loading the shapefile index: {e}")

    def _find_tile(self, point):
        """
        Find the tile file that contains the given point.
        
        Args:
            point (shapely.geometry.Point): The geographic point.
            
        Returns:
            str: Path to the tile file or None if not found.
        """
        try:
            # Iterate over features in the shapefile index
            for shape_record in self.sf.shapeRecords():
                # Using the shape's bounding box for a quick check before performing full point-in-polygon test
                if shape_record.shape.contains_point([point.x, point.y]):
                    # Assume the tile name is in the first field of the record
                    tile_name = str(shape_record.record[0])
                    tile_path = os.path.join(self.ghsl_data_dir, tile_name + ".tif")
                    if os.path.exists(tile_path):
                        return tile_path
                    else:
                        # If file not found, continue searching
                        continue
            return None
        except Exception as e:
            print(f"Error while searching for the tile: {e}")
            return None

    def get_value(self, lat, lng, layer_name, resolution):
        """
        Retrieve the value from a specified GHSL layer at given latitude and longitude.
        
        Args:
            lat (float): Latitude of the point.
            lng (float): Longitude of the point.
            layer_name (str): Name of the GHSL layer (e.g., 'population').
            resolution (int): Resolution of the data (commonly, 100, 250, etc.).
            
        Returns:
            float: Data value at the provided location, or None if not available.
        """
        # Create a Shapely Point object from the coordinates
        point = Point(lng, lat)
        # Find the tile that contains this point using the shapefile index
        tile_path = self._find_tile(point)
        if tile_path is None:
            print("No corresponding tile found for the given coordinates.")
            return None

        try:
            # Open the tile using rasterio
            with rasterio.open(tile_path) as src:
                # Optionally: Check that the point lies within the tile's bounds
                if not src.bounds.contains(lng, lat):
                    print("Provided coordinates are outside the tile bounds.")
                    return None

                # Compute the row and column indices in the raster for the provided geographic coordinate
                row, col = src.index(lng, lat)
                # Read the value from the first band 
                # (modify the band index if your data uses multiple bands or different band order)
                data_window = rasterio.windows.Window(col, row, 1, 1)
                data = src.read(1, window=data_window)
                value = data[0][0]
                return float(value)
        except rasterio.errors.RasterioIOError as e:
            print(f"Error reading the raster file: {e}")
            return None

    def list_available_layers(self):
        """
        Placeholder method to list available GHSL layers.
        In a full implementation, this could scan the data directory or reference a metadata file.
        
        Returns:
            list: A list of available layer names.
        """
        # Customize this method based on available data
        return ['population', 'built_up', 'urban_centre']

# Example usage:
if __name__ == "__main__":
    # Replace these paths with the actual paths to your shapefile index and GHSL GeoTIFF directory
    shapefile_index_path = "path/to/your/tile_index.shp"
    ghsl_data_dir = "path/to/your/ghsl_tiles"
    # Instantiate the data retriever
    retriever = GHSLDataRetriever(shapefile_index_path, ghsl_data_dir)
    
    # Define sample coordinates and parameters
    latitude = 40.7128
    longitude = -74.0060
    layer_name = "GHS_POP_E2025"  # Example layer name
    resolution = 100  # Example resolution

    # Retrieve the value at the specified location
    value = retriever.get_value(latitude, longitude, layer_name, resolution)
    if value is not None:
        print(f"The value at latitude {latitude} and longitude {longitude} is: {value}")
    else:
        print("Could not retrieve a value for the specified coordinates.")

Detailed Explanation of the Code

1. Class Initialization

The constructor (__init__) takes two parameters: the path to the shapefile index and the directory where the GHSL GeoTIFF files are stored. It loads the shapefile using the pyshp library. By handling errors during loading, the implementation ensures that subsequent methods have a valid spatial index to work with.

2. Finding the Correct Tile

The private method _find_tile is responsible for iterating over the entries in the shapefile index to identify the tile that contains the specified point. This method uses the contains_point function provided by the shapefile geometry, which checks if the point falls within a tile's polygon. It then constructs the filename by appending the ".tif" extension to the tile name found in the shapefile record.

3. Extracting the Raster Value

The get_value method first converts the latitude and longitude into a shapely.geometry.Point object. After obtaining the correct tile path, it opens the GeoTIFF file using rasterio. The raster’s index method efficiently converts the geographic coordinates into exact pixel indices (row and column). A small window is then created, and the pixel value is read. Finally, the value is returned as a float to maintain numerical precision.

4. Listing Available Layers

An auxiliary method, list_available_layers, is provided as a placeholder to show how one might list the different GHSL layers available. Depending on your dataset, this method might automatically scan a directory or utilize a metadata structure containing layer information.

5. Error Handling and Logging

Throughout the class, error handling is implemented to account for various failure modes. If the shapefile fails to load, if a tile cannot be found, or if the read operation from the raster fails, appropriate messages are printed to alert the user. These checks are crucial in geospatial data applications where data sources might not be perfectly aligned or complete.

Enhancements and Additional Considerations

While this class provides a robust starting point for GHSL data extraction, several enhancements can be considered to further adapt the solution to different scenarios:

a) Spatial Indexing Improvements

For large shapefile indexes, the search time can be optimized using spatial indexes (for example, by integrating the RTree library). This allows rapid querying of features based on their bounding boxes and will speed up the tile retrieval process.

b) CRS (Coordinate Reference System) Transformations

In cases where the GeoTIFF files and the input coordinates are in different coordinate systems, a coordinate transformation is essential. Libraries like pyproj or rasterio’s warped reading capabilities can be utilized to reproject coordinates, ensuring accurate pixel value extraction.

c) Multi-band Data Access

Some GHSL datasets may contain multiple bands, each representing different types of data (e.g., built-up areas or population statistics). The implementation can be extended by allowing the get_value method to receive an optional band number. This band selection would enable users to extract information from any band contained within the GeoTIFF file.

d) Resolution Handling and Layer Variants

Although the resolution parameter is passed to the method, the current implementation does not actively use it to distinguish between layers of different resolutions. An enhancement would be to map the resolution parameter to specific GeoTIFF naming conventions or maintain a dictionary of resolution-to-layer mappings, enabling the class to automatically select files based on the desired spatial resolution.

e) Data Preprocessing and Caching

In scenarios where multiple requests are made for nearby points, it might be beneficial to cache opened datasets or preprocess certain frequently requested tiles. This caching mechanism can dramatically speed up repeated queries and improve overall system performance.

HTML Table Overview

The table below provides a summary of the main components of the class along with their purpose and key functions:

Component	Functionality	Key Implementation
Initialization	Loads the shapefile index and sets the data directory	__init__ method with error handling
Tile Search	Identifies which GeoTIFF tile contains the desired point	_find_tile method using point-in-polygon checks
Value Extraction	Reads the pixel value from the tile based on the coordinate index	get_value method with rasterio reading and windowing
Layer Listing	Provides available layer names (customizable)	list_available_layers method

Conclusion and Final Thoughts

This Python class provides a comprehensive approach to accessing GHSL data through the use of a shapefile index to locate relevant GeoTIFF tiles. By integrating essential geospatial libraries, including rasterio, shapely, and pyshp, the code offers a robust and adaptable solution for retrieving pixel-level data values from extensive satellite-derived datasets.

The design is modular and highlights the importance of error handling, CRS management, and efficiency—in particular when dealing with large datasets typical of remote sensing applications. This overview not only presents a production-ready code snippet but also points out several potential enhancements such as spatial indexing, CRS transformations, multi-band support, and caching strategies which can be implemented further to improve performance and usability.

Users are encouraged to adapt this solution to meet their specific needs while ensuring compatibility with the dataset versions provided by sources such as the European Commission’s Copernicus program. Overall, the class serves as a practical foundation for research and development in geographic information systems (GIS) and remote sensing analytics.