Python中如何将DSM数据划分格网

要将DSM数据划分为格网，可以使用Python中的GDAL库和numpy库。下面是一个简单的示例代码：

import gdal
import numpy as np

# 读入DSM数据
dsm_file = 'dsm.tif'
dsm_dataset = gdal.Open(dsm_file)
dsm_band = dsm_dataset.GetRasterBand(1)
dsm_array = dsm_band.ReadAsArray()

# 设置格网大小
grid_size = 10  # 单位为米

# 计算格网数量
n_rows, n_cols = dsm_array.shape
n_grid_rows = int(np.ceil(n_rows / grid_size))
n_grid_cols = int(np.ceil(n_cols / grid_size))

# 创建新的栅格数据集
driver = gdal.GetDriverByName('GTiff')
grid_file = 'grid.tif'
grid_dataset = driver.Create(grid_file, n_grid_cols, n_grid_rows, 1, gdal.GDT_Float32)

# 定义投影和地理参考
grid_dataset.SetProjection(dsm_dataset.GetProjection())
grid_dataset.SetGeoTransform(dsm_dataset.GetGeoTransform())

# 创建格网数组
grid_array = np.zeros((n_grid_rows, n_grid_cols), dtype=np.float32)

# 将DSM数据按照格网划分
for i in range(n_grid_rows):
    for j in range(n_grid_cols):
        # 计算格网的行列范围
        row_start = i * grid_size
        row_end = min((i + 1) * grid_size, n_rows)
        col_start = j * grid_size
        col_end = min((j + 1) * grid_size, n_cols)
        
        # 计算格网的平均高度
        grid_height = np.mean(dsm_array[row_start:row_end, col_start:col_end])
        grid_array[i, j] = grid_height

# 将格网数组写入栅格数据集
grid_band = grid_dataset.GetRasterBand(1)
grid_band.WriteArray(grid_array)

# 保存并关闭数据集
grid_band.FlushCache()
grid_dataset = None

这个代码将DSM数据划分为10米大小的格网，并计算每个格网的平均高度，然后将结果保存到一个新的栅格数据集中。