For your reference, we created and compared the following five methods for extracting LiDAR elevation data along a straight line:
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Shapely Points and Distance: In this method, we created Shapely points and used a distance-based approach to identify points within a specified distance from the line. This involved checking if each point lies within the desired range using the
within_distancemethod of Shapely. -
Raster Masking: This method involved using raster files to represent elevation data. We masked the elevation raster using a Shapely line to extract elevation data directly from the raster cells that intersected with the line. This approach leverages the spatial indexing capabilities of raster data for efficient extraction.
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Dot Product with NumPy: Here, we stacked the LiDAR points into NumPy arrays and computed the dot product. By treating the LiDAR data as a matrix, we performed matrix operations to extract the relevant elevation information. This method is computationally intensive but can be very precise.
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Difference Vector Calculation: We selected a few hundred points along the sampling line and calculated the difference vectors between these points and the 3D elevation points. For each sample point on the line, we identified the elevation point with the minimum distance by comparing the magnitudes of the difference vectors. This method ensures precise matching of elevation data to the line points.
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cKDTree for Nearest Neighbor: Using the cKDTree from the SciPy library, we created a k-dimensional tree to find the nearest neighbor points from the LiDAR data to the points on the line efficiently. This method involves spatial indexing to perform fast nearest neighbor searches, making it highly efficient for large datasets.