Python geopandas.overlay() Examples

def calc_surrounding_area(zone_gdf, buffer_m):
    geometry_without_holes = zone_gdf.convex_hull
    geometry_without_holes_gdf = Gdf(geometry=geometry_without_holes.values)
    geometry_without_holes_gdf["one_class"] = "buildings"
    geometry_merged = geometry_without_holes_gdf.dissolve(by='one_class', aggfunc='sum')
    geometry_merged_final = Gdf(geometry=geometry_merged.convex_hull)
    new_buffer = Gdf(geometry=geometry_merged_final.buffer(buffer_m))
    area = overlay(geometry_merged_final, new_buffer, how='symmetric_difference')

    # THIS IS ANOTHER METHOD, NOT FUNCTIONAL THOUGH
    # from shapely.ops import Point
    # # new GeoDataFrame with same columns
    # points = []
    # # Extraction of the polygon nodes and attributes values from polys and integration into the new GeoDataFrame
    # for index, row in zone_gdf.iterrows():
    #     for j in list(row['geometry'].exterior.coords):
    #         points.append(Point(j))
    #
    # concave_hull, edge_points = alpha_shape(points, alpha=0.1)
    # simple_polygons = [x for x in concave_hull]
    # geometry_merged_final = Gdf(geometry=simple_polygons)
    # geometry_merged_final.plot()
    # plt.show()
    # new_buffer = Gdf(geometry=geometry_merged_final.buffer(buffer_m))
    # area = overlay(geometry_merged_final, new_buffer, how='symmetric_difference')
    # area.plot()

    return area, geometry_merged_final 

def difference(infile1, infile2, outfile):

    gdf1 = gpd.read_file(infile1)
    gdf2 = gpd.read_file(infile2)

    gdf3 = gpd.overlay(gdf1, gdf2, how='symmetric_difference')

    gdf3.to_file(outfile) 

def extract_tile_items(
    raster_features, labels, min_x, min_y, tile_width, tile_height
):
    """Extract label items that belong to the tile defined by the minimum
    horizontal pixel `min_x` (left tile limit), the minimum vertical pixel
    `min_y` (upper tile limit) and the sizes ̀tile_width` and `tile_height`
    measured as a pixel amount.

    The tile is cropped from the original image raster as follows:
      - horizontally, between `min_x` and `min_x+tile_width`
      - vertically, between `min_y` and `min_y+tile_height`

    This method takes care of original data projection (UTM 37S, Tanzania
    area), however this parameter may be changed if similar data on another
    projection is used.

    Parameters
    ----------
    raster_features : dict
        Raw image raster geographical features (`north`, `south`, `east` and
    `west` coordinates, `weight` and `height` measured in pixels)
    labels : geopandas.GeoDataFrame
        Raw image labels, as a set of geometries
    min_x : int
        Left tile limit, as a horizontal pixel index
    min_y : int
        Upper tile limit, as a vertical pixel index
    tile_width : int
        Tile width, measured in pixel
    tile_height : int
        Tile height, measured in pixel

    Returns
    -------
    geopandas.GeoDataFrame
        Set of ground-truth labels contained into the tile, characterized by
    their type (complete, unfinished or foundation) and their geometry

    """
    area = get_tile_footprint(
        raster_features, min_x, min_y, tile_width, tile_height
    )
    bdf = gpd.GeoDataFrame(
        crs=from_epsg(raster_features["srid"]), geometry=[area]
    )
    reproj_labels = labels.to_crs(epsg=raster_features["srid"])
    tile_items = gpd.sjoin(reproj_labels, bdf)
    if tile_items.shape[0] == 0:
        return tile_items[["condition", "geometry"]]
    tile_items = gpd.overlay(tile_items, bdf)
    tile_items = tile_items.explode()  # Manage MultiPolygons
    return tile_items[["condition", "geometry"]]