The following script calculates the number of images required for a drone to cover the United States ground area so that each image can be vectorized and used for analysis.
def calculate_num_images(
us_area_m2=9.15e12, # U.S. land area in square meters
image_width_px=5472, # width of image in pixels (20MP camera default)
image_height_px=3648, # height of image in pixels
gsd_m=0.03, # ground sampling distance in meters/pixel (3 cm/pixel at 100m AGL)
frontlap=0.70, # front overlap fraction (e.g., 70%)
sidelap=0.70 # side overlap fraction (e.g., 70%)
):
"""
Calculate the number of drone images required to cover a given area.
Parameters:
- us_area_m2: total area to cover (default: U.S. land area ~9.15e12 m^2)
- image_width_px, image_height_px: image resolution in pixels
- gsd_m: ground sampling distance in meters/pixel
- frontlap, sidelap: overlap fractions (0.0–1.0)
Returns:
- num_images: estimated number of images required
- footprint_raw: raw footprint area per image (m^2)
- footprint_eff: effective new coverage per image with overlap (m^2)
"""
# Ground footprint dimensions
width_m = image_width_px * gsd_m
height_m = image_height_px * gsd_m
# Raw footprint area
footprint_raw = width_m * height_m
# Effective coverage per image with overlap
footprint_eff = footprint_raw * (1 - frontlap) * (1 - sidelap)
# Number of images required
num_images = us_area_m2 / footprint_eff
return num_images, footprint_raw, footprint_eff
if __name__ == "__main__":
# Example with defaults
num_images, raw_area, eff_area = calculate_num_images()
print(f"Raw footprint per image: {raw_area:,.0f} m^2")
print(f"Effective coverage per image (with overlap): {eff_area:,.0f} m^2")
print(f"Total images required: {num_images:,.0f}")
# Try different parameters
num_images_alt, _, _ = calculate_num_images(gsd_m=0.025, frontlap=0.65, sidelap=0.65)
print(f"\nWith 2.5 cm/pixel GSD and 65% overlap:")
print(f"Total images required: {num_images_alt:,.0f}")
def calculate_storage_cost(
total_images,
image_size_kb=26, # size per image in KB
tier1_limit_tb=50, # first tier limit in TB
tier1_price_per_gb=0.018, # USD per GB for first 50 TB
tier2_limit_tb=450, # second tier limit in TB
tier2_price_per_gb=0.0173 # USD per GB for next 450 TB
):
"""
Calculate monthly Azure storage cost for given number of images.
Parameters:
- total_images: number of images to store
- image_size_kb: size of each image in KB (default 26 KB)
- tier1_limit_tb: size of first pricing tier in TB (default 50 TB)
- tier1_price_per_gb: price per GB for first tier
- tier2_limit_tb: size of second pricing tier in TB (default 450 TB)
- tier2_price_per_gb: price per GB for second tier
Returns:
- total_cost: monthly cost in USD
- total_storage_tb: total storage required in TB
"""
# Convert image size to bytes
image_size_bytes = image_size_kb * 1024
total_bytes = total_images * image_size_bytes
# Convert to GB (binary, 1 GB = 2^30 bytes)
total_gb = total_bytes / (2**30)
total_tb = total_gb / 1024
# Calculate tiered cost
cost = 0.0
remaining_gb = total_gb
# Tier 1
tier1_limit_gb = tier1_limit_tb * 1024
if remaining_gb > 0:
gb_in_tier1 = min(remaining_gb, tier1_limit_gb)
cost += gb_in_tier1 * tier1_price_per_gb
remaining_gb -= gb_in_tier1
# Tier 2
tier2_limit_gb = tier2_limit_tb * 1024
if remaining_gb > 0:
gb_in_tier2 = min(remaining_gb, tier2_limit_gb)
cost += gb_in_tier2 * tier2_price_per_gb
remaining_gb -= gb_in_tier2
# Beyond tier 2 (if needed, assume same as tier2 price)
if remaining_gb > 0:
cost += remaining_gb * tier2_price_per_gb
return cost, total_tb
if __name__ == "__main__":
# Example: 5.66 billion images at 26 KB each
total_images = int(5.66e9)
cost, storage_tb = calculate_storage_cost(total_images)
print(f"Total storage required: {storage_tb:,.1f} TB")
print(f"Monthly cost: ${cost:,.2f}")
# Try with a smaller dataset
test_images = 1_000_000
cost_test, storage_tb_test = calculate_storage_cost(test_images)
print(f"\nFor {test_images:,} images:")
print(f"Storage required: {storage_tb_test:.3f} TB")
print(f"Monthly cost: ${cost_test:.2f}")
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