def write_cog(layout, location, data, filename_template):
    """
    Write a GPS tile out as a Cloud-Optimized GeoTIFF

    Uses GDAL to write a COG out to disk, utilizing the given layout and location
    to generate the GeoTIFF header.

    Args:
        layout (``gps.LayoutDefinition``): The layout for the source layer
        location (``gps.SpatialKey`` or ``gps.SpaceTimeKey``): The cell identifier
            in the layer corresponding to the image data
        data (``gps.Tile``): The image data
        filename_template (str): A pattern giving the destination for the target
            image.  Contains two '{}' tokens which will be ``.format``ed with the
            column and row of the ``location``, in that order.  May be an S3 uri or
            local path.
    """
    bands, w, h = data.cells.shape
    nodata = data.no_data_value
    dtype = data.cells.dtype
    cw, ch = cell_size(layout)
    ex = extent_for_cell(layout, location)
    overview_level = int(log(w) / log(2) - 8)

    with rstr.io.MemoryFile() as memfile:
        with memfile.open(driver='GTiff',
                          count=bands,
                          width=w,
                          height=h,
                          transform=Affine(cw,  0.0, ex.xmin,
                                           0.0, -ch, ex.ymax),
                          crs=rstr.crs.CRS.from_epsg(4326),
                          nodata=nodata,
                          dtype=dtype,
                          compress='lzw',
                          tiled=True) as mem:
            windows = list(mem.block_windows(1))
            for _, w in windows:
                segment = data.cells[:, w.row_off:(w.row_off + w.height), w.col_off:(w.col_off + w.width)]
                mem.write(segment, window=w)
                mask_value = np.all(segment != nodata, axis=0).astype(np.uint8) * 255
                mem.write_mask(mask_value, window=w)

            overviews = [2 ** j for j in range(1, overview_level + 1)]
            mem.build_overviews(overviews, rwarp.Resampling.nearest)
            mem.update_tags(ns='rio_oveview', resampling=rwarp.Resampling.nearest.value)

            uri = urlparse.urlparse(filename_template)
            if uri.scheme == 's3':
                boto3.resource('s3').Bucket(uri.netloc).upload_fileobj(memfile, uri.path.format(location.col, location.row)[1:])
            else:
                rshutil.copy(mem, filename_template.format(location.col, location.row), copy_src_overviews=True)