vendor-utils

Read Quality Flag

Description: This section accesses and extracts various quality flags from a PlanetScope image file, such as cloud, snow, and shadow masks. These flags help identify specific conditions in each pixel, allowing for data quality assessment and filtering of undesirable areas.

Input:

• A PlanetScope TIFF file with associated quality assurance (QA) data (e.g., UDM2), and latitude and longitude coordinates for a point of interest.

Expected Output:

1. udm_data: Numpy array containing the quality mask values, representing different quality flags like cloud cover, snow presence, shadows, etc.
2. Quality mask information for a specified point (latitude, longitude) that indicates the data quality at that specific location.
3. Dictionary of available quality flags and their corresponding band indices in the UDM2 GeoTIFF file.

Code Snippet:

# Accessing different quality masks
udm_data_array  # The primary UDM data array containing quality information # Accessing UDM (Unusable Data Mask) data

udm_data = udm_data_array.values  # Numpy array format of quality mask data # Convert UDM data to numpy array format for further analysis

with PlanetScope.open(tif_file) as planet_obj: # Extract quality flags for a specific point by providing latitude and longitude
    # Print quality information at a specific geographic point
    print(planet_obj.get_quality_from_point(lat=28.866933958061644, lon=-97.90677761357139))

# Available Quality flags and their corresponding band indices in the UDM2 GeoTIFF
udm_band_indices = udm_data_array.attrs.get("bands_indices")  # Dictionary mapping quality flags to band indices
print("Available Quality Flags and Band Indices:", udm_band_indices)

# Extract individual quality masks, each representing a specific condition
with PlanetScope.open(tif_file) as planet_obj:    
    # Obtain the "clear" mask, marking areas without clouds or haze
    clear_mask = planet_obj.get_quality_mask("clear")
    
    # Obtain the "cloud" mask, indicating cloud-covered areas
    cloud_mask = planet_obj.get_quality_mask("cloud")
    
    # Obtain the "snow" mask, highlighting areas covered by snow
    snow_mask = planet_obj.get_quality_mask("snow")
    
    # Obtain the "shadow" mask, marking regions in shadow
    shadow_mask = planet_obj.get_quality_mask("shadow")
    
    # Obtain the "haze_light" and "haze_heavy" masks, differentiating light and heavy haze
    haze_light_mask = planet_obj.get_quality_mask("haze_light")
    haze_heavy_mask = planet_obj.get_quality_mask("haze_heavy")
    
    # Obtain the "confidence" mask, which may indicate the confidence level of the data quality
    confidence_mask = planet_obj.get_quality_mask("confidence")