考虑使用Rtree来帮助识别多边形可能与哪些网格单元相交。这样，您可以删除与lat/隆恩数组一起使用的for循环，这可能是较慢的部分。
你的代码结构应该像这样：

from shapely.ops import cascaded_union
from rtree import index
idx = index.Index()

# Populate R-tree index with bounds of grid cells
for pos, cell in enumerate(grid_cells):
    # assuming cell is a shapely object
    idx.insert(pos, cell.bounds)

# Loop through each Shapely polygon
for poly in polygons:
    # Merge cells that have overlapping bounding boxes
    merged_cells = cascaded_union([grid_cells[pos] for pos in idx.intersection(poly.bounds)])
    # Now do actual intersection
    print(poly.intersection(merged_cells).area)

从2013/2014年开始Shapely就有STRtree了，我用过，看起来效果不错。
下面是文档字符串的一个片段：
STRtree是一个R树，它是使用Sort-Tile-Recursive算法创建的。STRtree以一系列几何对象作为初始化参数。初始化后，可以使用查询方法对这些对象进行空间查询。

>>> from shapely.geometry import Polygon
>>> from shapely.strtree import STRtree
>>> polys = [Polygon(((0, 0), (1, 0), (1, 1))), Polygon(((0, 1), (0, 0), (1, 0))), Polygon(((100, 100), (101, 100), (101, 101)))]
>>> s = STRtree(polys)
>>> query_geom = Polygon([(-1, -1), (2, 0), (2, 2), (-1, 2)])
>>> result = s.query(query_geom)
>>> polys[0] in result
True

下面是另一个版本的答案，但对IoU有更多的控制

def merge_intersecting_polygons(list_of_polygons, image_width, image_height):
"""
merge intersecting polygons with shapely library
merge only if Intersection over Union is greater than 0.5
speed up with STRTree
"""
# create shapely polygons
shapely_polygons = []
for polygon in list_of_polygons:
    shapely_polygons.append(Polygon(polygon))
# create STRTree
tree = STRtree(shapely_polygons)
# merge polygons
merged_polygons = []
for i, polygon in enumerate(shapely_polygons):
    # find intersecting polygons
    intersecting_polygons = tree.query(polygon)
    # merge intersecting polygons
    for intersecting_polygon in intersecting_polygons:
        if polygon != intersecting_polygon:
            # compute intersection over union
            intersection = polygon.intersection(intersecting_polygon).area
            union = polygon.union(intersecting_polygon).area
            iou = intersection/union
            if iou > 0.5:
                # merge polygons
                polygon = polygon.union(intersecting_polygon)
    # add merged polygon to list
    merged_polygons.append(polygon)
# remove duplicates
merged_polygons = list(set(merged_polygons))
# convert shapely polygons to list of polygons
list_of_polygons = []
for polygon in merged_polygons:
    list_of_polygons.append(np.array(polygon.exterior.coords).tolist())
return list_of_polygons