final 2024_Remote_Sensing

2025-02-12 19:27:51 +01:00
parent 373d194038
commit 682eef1c3e
132 changed files with 964360 additions and 20192 deletions
@@ -0,0 +1,100 @@
+import rasterio
+import numpy as np
+import pandas as pd
+
+def map_two_digit_code(code: int) -> str:
+    """
+    Returns a string label like 'Water -> Agriculture'
+    by interpreting the tens digit as the 'from' class
+    and the ones digit as the 'to' class.
+
+    Example:
+      code = 34
+      tens_digit = 3 -> 'Forest'
+      ones_digit = 4 -> 'Agriculture'
+      return 'Forest -> Agriculture'
+    """
+    # Define what each digit means
+    digit_to_label = {
+        1: "Water",
+        2: "Urban",
+        3: "Forest",
+        4: "Agriculture"
+    }
+
+    # Tens digit = 'from' class
+    tens_digit = code // 10
+    from_label = digit_to_label.get(tens_digit, "Unknown")
+
+    # Ones digit = 'to' class
+    ones_digit = code % 10
+    to_label = digit_to_label.get(ones_digit, "Unknown")
+
+    return f"{from_label} -> {to_label}"
+
+def generate_change_detection_report(tif_path: str, output_csv_path: str = None) -> pd.DataFrame:
+    """
+    Reads a single-band change-detection GeoTIFF with classes from 11 to 44,
+    generates a report of pixel counts, area, and applies a label for every code.
+
+    :param tif_path: Path to the input GeoTIFF.
+    :param output_csv_path: If provided, saves the report as a CSV file.
+    :return: A pandas DataFrame with columns: ['Class_Value', 'Pixel_Count', 'Area', 'Label'].
+    """
+    # 1. Open the raster
+    with rasterio.open(tif_path) as src:
+        band = src.read(1)
+        transform = src.transform
+        
+        # Compute pixel area
+        pixel_width = transform[0]
+        pixel_height = -transform[4]  # Typically negative
+        pixel_area = pixel_width * pixel_height
+
+    # 2. Count unique class values
+    unique_vals, counts = np.unique(band, return_counts=True)
+
+    # 3. Filter classes from 11 to 44
+    valid_mask = (unique_vals >= 11) & (unique_vals <= 44)
+    filtered_classes = unique_vals[valid_mask]
+    filtered_counts = counts[valid_mask]
+
+    # 4. Calculate area per class
+    areas = filtered_counts * pixel_area
+
+    # 5. Create a DataFrame
+    df_report = pd.DataFrame({
+        "Class_Value": filtered_classes,
+        "Pixel_Count": filtered_counts,
+        "Area": areas
+    })
+
+    # 6. Apply the custom mapping function to get a text label (e.g., 'Water -> Agriculture')
+    df_report["Label"] = df_report["Class_Value"].apply(map_two_digit_code)
+
+    # 7. (Optional) sort by Class_Value
+    df_report.sort_values(by="Class_Value", inplace=True)
+
+    # 8. Optionally save the DataFrame to a CSV file
+    if output_csv_path:
+        df_report.to_csv(output_csv_path, index=False)
+
+    return df_report
+
+# ---------------
+# Example usage:
+if __name__ == "__main__":
+    input_tif = "change_detected/change_detection.tif"  # Update path as needed
+    report_df = generate_change_detection_report(
+        tif_path=input_tif,
+        output_csv_path="report/change_detection_report.csv"
+    )
+
+    print("Detailed Report (11–44):")
+    print(report_df)
+
+    # Optional: Summarize area by the Label column
+    summary = report_df.groupby("Label")["Area"].sum().reset_index()
+    summary.columns = ["Label", "Total_Area"]
+    print("\nArea Summary by Label:")
+    print(summary)