Repository: ACCESS-Cloud-Based-InSAR/dem-stitcher (language: python)

Issue:
Missing glo-30 DEM tiles over Azerbaijan, Armenia
### The bug
Glo-30 tiles have missing tiles over said areas. There are downstream implications related to removing topographic phase for GUNWs.

A reference to some of the missing tiles: https://twitter.com/EricFielding/status/1531639815610830848

```
Country identifier | GeoCell ID
Armenia | N38E046
Armenia | N39E044
Armenia | N39E045
Armenia | N39E046
Armenia | N40E043
Armenia | N40E044
Armenia | N40E045
Armenia | N41E043
Armenia | N41E044
Armenia | N41E045
Azerbaijan | N38E046
Azerbaijan | N39E045
Azerbaijan | N39E046
Azerbaijan | N39E047
Azerbaijan | N40E045
Azerbaijan | N40E046
Azerbaijan | N40E047
Azerbaijan | N38E045
Azerbaijan | N38E046
Azerbaijan | N38E048
Azerbaijan | N38E049
Azerbaijan | N39E044
Azerbaijan | N39E045
Azerbaijan | N39E046
Azerbaijan | N39E047
Azerbaijan | N39E048
Azerbaijan | N39E049
Azerbaijan | N40E044
Azerbaijan | N40E045
Azerbaijan | N40E046
Azerbaijan | N40E047
Azerbaijan | N40E048
Azerbaijan | N40E049
Azerbaijan | N40E050
Azerbaijan | N41E044
Azerbaijan | N41E045
Azerbaijan | N41E046
Azerbaijan | N41E047
Azerbaijan | N41E048
Azerbaijan | N41E049
```

### To Reproduce
Check this package data [file](https://github.com/ACCESS-Cloud-Based-InSAR/dem-stitcher/blob/dev/dem_stitcher/data/glo_30.geojson.zip) and verify above tiles doe not exist

 ### Additional context
Downstream topographic phase removal - see Issue [#67](https://github.com/ACCESS-Cloud-Based-InSAR/DockerizedTopsApp/issues/67#issue-1254481042)



Discussion / hints:

Fix patch:
diff --git a/.gitignore b/.gitignore
index bddd5c5..4410990 100644
--- a/.gitignore
+++ b/.gitignore
@@ -102,8 +102,10 @@ tests/notebooks/
 !notebooks/Basic_Demo.ipynb
 !notebooks/Comparing_DEMs.ipynb
 !notebooks/Staging_a_DEM_for_ISCE2.ipynb
-!notebooks/organize_tile_data/Format_and_Organize_Data.ipynb
+!notebooks/organize_tile_data/0_Format_and_Organize_Data.ipynb
+!notebooks/organize_tile_data/1_Missing_glo-30_tiles.ipynb
 !notebooks/analysis_and_comparison/0_Alignment_with_original_DEM_tiles.ipynb
 !notebooks/analysis_and_comparison/1_Comparison_with_ISCE2_dem.ipynb
+!notebooks/Filling_in_missing_glo-30_tiles_with_glo-90_tiles.ipynb
 
 notebooks/analysis_and_comparison/la_test/
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8f32df1..ce0785d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,23 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [PEP 440](https://www.python.org/dev/peps/pep-0440/)
 and uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [2.2.1]
+
+### Added
+- Included Copernicus GLO-90 (as `glo_90`) and the missing GLO-30 tiles that are available as GLO-90 tiles as `glo_90_missing`
+- Demonstration on how to fill in `glo-30` tiles that are missing with `glo-90` tiles.
+- Exceptions that catch: a) no available tiles of specifed DEM as a `NoDEMCoverage` exception, b) badly specified `dem_name` as a `DEMNotSupported` exception and c) extent/bounds not of the form `xmin, ymin, xmax, ymax` as a `ValueError`
+- API keyword argument `fill_in_glo_30` to fill in `glo_30` tiles that are missing, but whose corresponding `glo_90` tiles are not.
+- Tests for Exceptions and added datasets
+
+### Changed
+- Since AWS registry removed zip, the `glo_30` and `glo_90` geojsons have precisely the tiles that are available (had to traverse bucket)
+- Notebooks to organize data have been updated
+
+### Fixed
+- Fixed #33 i.e. missing `glo_30` rasters over Azerbaijan and Armenia by filling in with available `glo_90`.
+
+
 ## [2.2.0]
 
 ### Added
diff --git a/README.md b/README.md
index 8f35c37..df24552 100644
--- a/README.md
+++ b/README.md
@@ -68,15 +68,16 @@ The [DEMs](https://github.com/ACCESS-Cloud-Based-InSAR/dem_stitcher/tree/main/de
 
 ```
 In [1]: from dem_stitcher.datasets import DATASETS; DATASETS
-Out[1]: ['srtm_v3', 'nasadem', 'glo_30', '3dep', 'ned1']
+Out[1]: ['srtm_v3', 'nasadem', 'glo_90_missing', 'glo_30', '3dep', 'glo_90', 'ned1']
 ```
 
-1. `glo-30`: Copernicus GLO-30 DEM 30 meter [[link](https://registry.opendata.aws/copernicus-dem/)]
+1. `glo_30`/`glo_90`: Copernicus GLO-30/GLO-90 DEM. They are the 30 and 90 meter resolution, respectively [[link](https://registry.opendata.aws/copernicus-dem/)]
 2. The USGS DEMSs:
    - `ned1`:  Ned 1 arc-second (deprecated by USGS) [[link](https://cugir.library.cornell.edu/catalog/cugir-009096)]
    - `3dep`: 3Dep 1 arc-second[[link](https://www.sciencebase.gov/catalog/item/imap/4f70aa71e4b058caae3f8de1)]
 3. `srtm_v3`: SRTM v3 [[link](https://dwtkns.com/srtm30m/)]
 4. `nasadem`: Nasadem [[link](https://lpdaac.usgs.gov/products/nasadem_hgtv001/)]
+5. `glo_90_missing`: these are tiles that are in `glo_90` but not in `glo_30`. They are over the countries Armenia and Azerbaijan. Used internally to help fill in gaps in coverage of `glo_30`.
 
 # Transformations
 
diff --git a/dem_stitcher/data/glo_30.geojson.zip b/dem_stitcher/data/glo_30.geojson.zip
index d0169f6..554b6b7 100644
Binary files a/dem_stitcher/data/glo_30.geojson.zip and b/dem_stitcher/data/glo_30.geojson.zip differ
diff --git a/dem_stitcher/data/glo_90.geojson.zip b/dem_stitcher/data/glo_90.geojson.zip
new file mode 100644
index 0000000..e4af9c1
Binary files /dev/null and b/dem_stitcher/data/glo_90.geojson.zip differ
diff --git a/dem_stitcher/data/glo_90_missing.geojson.zip b/dem_stitcher/data/glo_90_missing.geojson.zip
new file mode 100644
index 0000000..dbef017
Binary files /dev/null and b/dem_stitcher/data/glo_90_missing.geojson.zip differ
diff --git a/dem_stitcher/dem_readers.py b/dem_stitcher/dem_readers.py
index ac2b19b..fae8417 100644
--- a/dem_stitcher/dem_readers.py
+++ b/dem_stitcher/dem_readers.py
@@ -1,11 +1,10 @@
 import io
 import zipfile
-from typing import Tuple, Union
+from typing import Tuple
 
 import numpy as np
 import rasterio
 import requests
-from rasterio import RasterioIOError
 from rasterio.io import MemoryFile
 
 
@@ -14,14 +13,6 @@ def read_dem(dem_path: str) -> rasterio.DatasetReader:
     return ds
 
 
-def read_glo(dem_path: str) -> Union[rasterio.DatasetReader, None]:
-    # Some tiles do not exist
-    try:
-        return read_dem(dem_path)
-    except RasterioIOError:
-        return None
-
-
 def read_dem_bytes(dem_path: str, suffix: str = '.img') -> bytes:
     # online
     if (dem_path[:7] == 'http://') or (dem_path[:8] == 'https://'):
diff --git a/dem_stitcher/exceptions.py b/dem_stitcher/exceptions.py
new file mode 100644
index 0000000..0b04388
--- /dev/null
+++ b/dem_stitcher/exceptions.py
@@ -0,0 +1,6 @@
+class NoDEMCoverage(Exception):
+    """Throw this exception if extent/bounds does not cover any available DEM tiles"""
+
+
+class DEMNotSupported(Exception):
+    """Throw this exception if DEM Name is not supported"""
diff --git a/dem_stitcher/glo_30_missing.py b/dem_stitcher/glo_30_missing.py
new file mode 100644
index 0000000..74dd5d1
--- /dev/null
+++ b/dem_stitcher/glo_30_missing.py
@@ -0,0 +1,32 @@
+from typing import Tuple
+
+import numpy as np
+from rasterio.enums import Resampling
+from rasterio.io import MemoryFile
+from rasterio.merge import merge
+
+
+def merge_glo_30_and_90_dems(arr_glo_30,
+                             prof_glo_30,
+                             arr_glo_90,
+                             prof_glo_90) -> Tuple[np.ndarray, dict]:
+    with MemoryFile() as memfile_90:
+        dataset_90 = memfile_90.open(**prof_glo_90)
+        dataset_90.write(arr_glo_90, 1)
+
+        with MemoryFile() as memfile_30:
+            dataset_30 = memfile_30.open(**prof_glo_30)
+            dataset_30.write(arr_glo_30, 1)
+
+            merged_arr, merged_trans = merge([dataset_30, dataset_90],
+                                             resampling=Resampling['bilinear'],
+                                             method='first',
+                                             )
+            merged_glo_arr = merged_arr[0, ...]
+
+    prof_30_merged = prof_glo_30.copy()
+    prof_30_merged['transform'] = merged_trans
+    prof_30_merged['width'] = merged_glo_arr.shape[1]
+    prof_30_merged['height'] = merged_glo_arr.shape[0]
+
+    return merged_glo_arr, prof_30_merged
diff --git a/dem_stitcher/rio_tools.py b/dem_stitcher/rio_tools.py
index 324f1a1..cfc01a0 100644
--- a/dem_stitcher/rio_tools.py
+++ b/dem_stitcher/rio_tools.py
@@ -267,8 +267,8 @@ def _aligned_target(transform: Affine,
     ymax = transform.yoff
 
     dst_transform = Affine(res[0], 0, xmin, 0, -res[1], ymax)
-    dst_width = max(int(np.ceil((xmax - xmin) / res[0])), 1)
-    dst_height = max(int(np.ceil((ymax - ymin) / res[1])), 1)
+    dst_width = max(int(np.floor((xmax - xmin) / res[0])), 1)
+    dst_height = max(int(np.floor((ymax - ymin) / res[1])), 1)
 
     return dst_transform, dst_width, dst_height
 
diff --git a/dem_stitcher/stitcher.py b/dem_stitcher/stitcher.py
index dfc2f3f..676ce1d 100644
--- a/dem_stitcher/stitcher.py
+++ b/dem_stitcher/stitcher.py
@@ -14,9 +14,11 @@
 from shapely.geometry import box
 from tqdm import tqdm
 
-from .datasets import get_dem_tile_extents
-from .dem_readers import read_dem, read_glo, read_nasadem, read_ned1, read_srtm
+from .datasets import DATASETS, get_dem_tile_extents
+from .dem_readers import read_dem, read_nasadem, read_ned1, read_srtm
+from .exceptions import DEMNotSupported, NoDEMCoverage
 from .geoid import remove_geoid
+from .glo_30_missing import merge_glo_30_and_90_dems
 from .rio_tools import (reproject_arr_to_match_profile,
                         reproject_arr_to_new_crs, translate_profile,
                         update_profile_resolution)
@@ -24,18 +26,26 @@
 
 RASTER_READERS = {'ned1': read_ned1,
                   '3dep': read_dem,
-                  'glo_30': read_glo,
+                  'glo_30': read_dem,
+                  'glo_90': read_dem,
+                  'glo_90_missing': read_dem,
                   'srtm_v3': read_srtm,
                   'nasadem': read_nasadem}
 
 DEM2GEOID = {'ned1': 'geoid_18',
              '3dep': 'geoid_18',
              'glo_30': 'egm_08',
+             'glo_90': 'egm_08',
+             'glo_90_missing': 'egm_08',
              'srtm_v3': 'egm_96',
              'nasadem': 'egm_96'}
 
+PIXEL_CENTER_DEMS = ['srtm_v3', 'nasadem', 'glo_30', 'glo_90', 'glo_90_missing']
+
 
 def get_dem_tiles(bounds: list, dem_name: str) -> gpd.GeoDataFrame:
+    if dem_name not in DATASETS:
+        raise DEMNotSupported(f'Please use dem_name in: {", ".join(DATASETS)}')
     box_sh = box(*bounds)
     df_tiles_all = get_dem_tile_extents(dem_name)
     index = df_tiles_all.intersects(box_sh)
@@ -47,6 +57,12 @@ def get_dem_tiles(bounds: list, dem_name: str) -> gpd.GeoDataFrame:
     return df_tiles
 
 
+def intersects_missing_glo_30_tiles(extent: list) -> bool:
+    extent_geo = box(*extent)
+    df_missing = get_dem_tiles(extent, 'glo_90_missing')
+    return df_missing.intersects(extent_geo).sum() > 0
+
+
 def _download_and_write_one_tile(url: str,
                                  dest_path: Path,
                                  reader: Callable,
@@ -60,7 +76,7 @@ def _download_and_write_one_tile(url: str,
     dem_profile['driver'] = 'GTiff'
     with rasterio.open(dest_path, 'w', **dem_profile) as ds:
         ds.write(dem_arr, 1)
-        if dem_name in ['srtm_v3', 'nasadem', 'glo_30']:
+        if dem_name in PIXEL_CENTER_DEMS:
             ds.update_tags(AREA_OR_POINT='Point')
     return dem_profile
 
@@ -199,6 +215,25 @@ def merge_and_transform_dem_tiles(datasets: list,
     return dem_arr, dem_profile
 
 
+def patch_glo_30_with_glo_90(arr_glo_30: np.ndarray,
+                             prof_glo_30: dict,
+                             extent: list,
+                             stitcher_kwargs: dict) -> Tuple[np.ndarray, dict]:
+    if not intersects_missing_glo_30_tiles(extent):
+        return arr_glo_30, prof_glo_30
+
+    stitcher_kwargs['dem_name'] = 'glo_90_missing'
+    arr_glo_90, prof_glo_90 = stitch_dem(**stitcher_kwargs)
+
+    dem_arr, dem_prof = merge_glo_30_and_90_dems(arr_glo_30,
+                                                 prof_glo_30,
+                                                 arr_glo_90,
+                                                 prof_glo_90
+                                                 )
+
+    return dem_arr, dem_prof
+
+
 def stitch_dem(bounds: list,
                dem_name: str,
                dst_ellipsoidal_height: bool = True,
@@ -206,7 +241,8 @@ def stitch_dem(bounds: list,
                dst_resolution: Union[float, Tuple[float]] = None,
                n_threads_reproj: int = 5,
                n_threads_downloading=5,
-               driver: str = 'GTiff'
+               driver: str = 'GTiff',
+               fill_in_glo_30: bool = True
                ) -> Tuple[np.ndarray, dict]:
     """This is API for stitching DEMs
 
@@ -230,6 +266,10 @@ def stitch_dem(bounds: list,
         Threads for downloading tiles, by default 5
     driver : str, optional
         Output format in profile, by default 'GTiff'
+    fill_in_glo_30 : bool, optional
+        If `dem_name` is 'glo_30' then fills in missing `glo_30` tiles over Armenia and Azerbaijan with available
+        `glo_90` tiles, by default True. If the extent falls inside of the missing `glo_30` tiles, then `glo_90` is
+        upsample to 30 meters unless `dst_resolution` is specified.
 
     Returns
     -------
@@ -239,6 +279,14 @@ def stitch_dem(bounds: list,
         [notebooks](https://github.com/ACCESS-Cloud-Based-InSAR/dem-stitcher/tree/dev/notebooks)
         for demonstrations.
     """
+    # Used for filling in glo_30 missing tiles if needed
+    stitcher_kwargs = locals()
+
+    if dem_name not in DATASETS:
+        raise DEMNotSupported(f'Please use dem_name in: {", ".join(DATASETS)}')
+
+    if (bounds[0] > bounds[2]) or (bounds[1] > bounds[3]):
+        raise ValueError('Specify bounds as xmin, ymin, xmax, ymax in epsg:4326')
 
     df_tiles = get_dem_tiles(bounds, dem_name)
     urls = df_tiles.url.tolist()
@@ -249,7 +297,7 @@ def stitch_dem(bounds: list,
 
     # Datasets that permit virtual warping
     # The readers return DatasetReader rather than (Array, Profile)
-    if dem_name in ['glo_30', '3dep']:
+    if dem_name in ['glo_30', 'glo_90', '3dep', 'glo_90_missing']:
         def reader(url):
             return RASTER_READERS[dem_name](url)
         with ThreadPoolExecutor(max_workers=n_threads_downloading) as executor:
@@ -267,6 +315,20 @@ def reader(url):
                                     max_workers=n_threads_downloading)
         datasets = list(map(rasterio.open, dest_paths))
 
+    if not datasets:
+        # This is the case that an extent is entirely contained within glo_90
+        if ((dem_name == 'glo_30') and fill_in_glo_30 and intersects_missing_glo_30_tiles(bounds)):
+
+            stitcher_kwargs['dem_name'] = 'glo_90_missing'
+            # if dst_resolution is None, then make sure we upsample to 30 meter resolution
+            dst_resolution = stitcher_kwargs['dst_resolution']
+            stitcher_kwargs['dst_resolution'] = dst_resolution or 0.0002777777777777777775
+
+            dem_arr, dem_profile = stitch_dem(**stitcher_kwargs)
+            return dem_arr, dem_profile
+        else:
+            raise NoDEMCoverage(f'Specified bounds are not within coverage area of {dem_name}')
+
     dem_arr, dem_profile = merge_and_transform_dem_tiles(datasets,
                                                          bounds,
                                                          dem_name,
@@ -286,4 +348,10 @@ def reader(url):
     # Set driver in profile
     dem_profile['driver'] = driver
 
+    if (dem_name == 'glo_30') and fill_in_glo_30:
+        dem_arr, dem_profile = patch_glo_30_with_glo_90(dem_arr,
+                                                        dem_profile,
+                                                        bounds,
+                                                        stitcher_kwargs)
+
     return dem_arr, dem_profile
diff --git a/notebooks/Filling_in_missing_glo-30_tiles_with_glo-90_tiles.ipynb b/notebooks/Filling_in_missing_glo-30_tiles_with_glo-90_tiles.ipynb
new file mode 100644
index 0000000..9c24bad
--- /dev/null
+++ b/notebooks/Filling_in_missing_glo-30_tiles_with_glo-90_tiles.ipynb
@@ -0,0 +1,579 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "constant-scott",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates how `dem-stitcher` uses the Copernicus Glo-90 DEM to fill in Glo-30 missing areas over the countries Azerbaijan and Armenia as documented [here](https://github.com/ACCESS-Cloud-Based-InSAR/DockerizedTopsApp/issues/67).\n",
+    "\n",
+    "This could be used to combine different DEMs so we are sharing it. At the end of the notebook, we demonstrate the new API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "surrounded-julian",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:14:23.606178Z",
+     "start_time": "2022-06-14T23:14:23.585229Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "southeast-learning",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:14:45.579775Z",
+     "start_time": "2022-06-14T23:14:45.552899Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['srtm_v3', 'nasadem', 'glo_90_missing', 'glo_30', '3dep', 'glo_90', 'ned1']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dem_stitcher.datasets import DATASETS\n",
+    "from dem_stitcher.stitcher import stitch_dem, get_dem_tile_extents\n",
+    "import rasterio\n",
+    "from rasterio import plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "from rasterio.crs import CRS\n",
+    "from shapely.geometry import box\n",
+    "from rasterio.plot import show\n",
+    "from rasterio.io import MemoryFile\n",
+    "from rasterio.merge import merge\n",
+    "from rasterio.enums import Resampling\n",
+    "\n",
+    "DATASETS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "logical-institution",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:14:47.372689Z",
+     "start_time": "2022-06-14T23:14:47.352811Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# over Eastern Armenia and Western Turkey\n",
+    "bounds = [42, 39, 44, 41]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "serial-developer",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:42.932410Z",
+     "start_time": "2022-06-14T23:14:48.336597Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reading glo_30 Datasets: 100%|█| 11/11 [00:15<00:00,  1.43s/\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.31 s, sys: 4.25 s, total: 13.6 s\n",
+      "Wall time: 1min 54s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "X_glo_30, p_30 = stitch_dem(bounds,\n",
+    "                            dem_name='glo_30',\n",
+    "                            dst_ellipsoidal_height=True,\n",
+    "                            fill_in_glo_30=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "geological-stranger",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:44.301009Z",
+     "start_time": "2022-06-14T23:16:42.934875Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(38.0, 42.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "df_box = gpd.GeoDataFrame(geometry=[box(*bounds)],\n",
+    "                          crs=CRS.from_epsg(4326))\n",
+    "\n",
+    "df_box.boundary.plot(ax=ax, color='blue')\n",
+    "show(X_glo_30, transform=p_30['transform'], ax=ax)\n",
+    "\n",
+    "ax.set_xlim(bounds[0] - 1, bounds[2] +1)\n",
+    "ax.set_ylim(bounds[1] - 1, bounds[3] + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ordinary-welding",
+   "metadata": {},
+   "source": [
+    "# Does extent intersect missing tiles?\n",
+    "\n",
+    "We now have `glo_90_missing` as a DEM dataset to obtain the `glo_30` tiles that have representatives in `glo_90`. See `notebooks/organize_tile_data/1_Missing_glo-30_tiles.ipynb` how this geojson was generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "associate-associate",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:44.329605Z",
+     "start_time": "2022-06-14T23:16:44.302832Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"41.92 38.92 2.1599999999999966 2.1599999999999966\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,80.0)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.04319999999999993\" opacity=\"0.6\" d=\"M 44.0,39.0 L 44.0,41.0 L 42.0,41.0 L 42.0,39.0 L 44.0,39.0 z\" /></g></svg>"
+      ],
+      "text/plain": [
+       "<shapely.geometry.polygon.Polygon at 0x133c721f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extent_geo = df_box.geometry[0]\n",
+    "extent_geo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "assured-peoples",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:44.358779Z",
+     "start_time": "2022-06-14T23:16:44.332271Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(25, 4)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_missing = get_dem_tile_extents('glo_90_missing')\n",
+    "df_missing.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "harmful-religious",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:44.521570Z",
+     "start_time": "2022-06-14T23:16:44.360722Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x134491fd0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "df_missing.boundary.plot(ax=ax, color='y', label='missing glo_30_tiles')\n",
+    "df_box.plot(ax=ax, color='blue', alpha=.25)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "devoted-georgia",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:16:44.545404Z",
+     "start_time": "2022-06-14T23:16:44.523538Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_missing.intersects(extent_geo).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "minus-communist",
+   "metadata": {},
+   "source": [
+    "There are 5 extents that intersect (boundaries count)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "coral-atlanta",
+   "metadata": {},
+   "source": [
+    "# Get Missing Tiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "imposed-celebrity",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:17:01.125707Z",
+     "start_time": "2022-06-14T23:16:44.547237Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reading glo_90_missing Datasets: 100%|█| 5/5 [00:07<00:00,  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 913 ms, sys: 424 ms, total: 1.34 s\n",
+      "Wall time: 16.6 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "X_glo_90_missing, p_90 = stitch_dem(bounds,\n",
+    "                                    dem_name='glo_90_missing',\n",
+    "                                    dst_ellipsoidal_height=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "passing-equality",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:17:01.326817Z",
+     "start_time": "2022-06-14T23:17:01.127706Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(38.0, 42.0)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "df_box.boundary.plot(ax=ax, color='blue')\n",
+    "show(X_glo_90_missing, transform=p_90['transform'], ax=ax)\n",
+    "\n",
+    "ax.set_xlim(bounds[0] - 1, bounds[2] +1)\n",
+    "ax.set_ylim(bounds[1] - 1, bounds[3] + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "distributed-nirvana",
+   "metadata": {},
+   "source": [
+    "# Merge 30 and 90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "happy-johns",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:17:02.555838Z",
+     "start_time": "2022-06-14T23:17:01.328619Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with MemoryFile() as memfile_90:\n",
+    "    dataset_90 = memfile_90.open(**p_90)\n",
+    "    dataset_90.write(X_glo_90_missing,1)\n",
+    "        \n",
+    "    with MemoryFile() as memfile_30:\n",
+    "        dataset_30 = memfile_30.open(**p_30)\n",
+    "        dataset_30.write(X_glo_30, 1)\n",
+    "        \n",
+    "        merged_arr, merged_trans = merge([dataset_30, dataset_90],\n",
+    "                                         resampling=Resampling['bilinear'],\n",
+    "                                         method='first',\n",
+    "                                         )\n",
+    "        merged_arr = merged_arr[0, ...]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "nominated-salad",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:17:04.010615Z",
+     "start_time": "2022-06-14T23:17:02.559417Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "show(merged_arr, transform=merged_trans)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db6b41d0",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Using `dem-stitcher`\n",
+    "\n",
+    "See `tests/test_missing.py` for more examples how this notebook was distilled to ensure correctness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2e61630e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:18:37.343206Z",
+     "start_time": "2022-06-14T23:17:04.034770Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reading glo_30 Datasets: 100%|█| 11/11 [00:03<00:00,  3.06it\n",
+      "Reading glo_90_missing Datasets: 100%|█| 5/5 [00:01<00:00,  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extent = [42.95, 40.45, 43.05, 40.55]\n",
+    "\n",
+    "dst_area_or_point = 'Point'\n",
+    "dst_ellipsoidal_height = False\n",
+    "dem_name = 'glo_30'\n",
+    "\n",
+    "X_filled, p_filled = stitch_dem(bounds,\n",
+    "                                dem_name=dem_name,\n",
+    "                                dst_ellipsoidal_height=dst_ellipsoidal_height,\n",
+    "                                dst_area_or_point=dst_area_or_point,\n",
+    "                                fill_in_glo_30=True)\n",
+    "\n",
+    "show(X_filled, transform=p_filled['transform'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d6f34a28",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:50:27.566211Z",
+     "start_time": "2022-06-14T23:50:18.043529Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open('glo_30_filled_in.tif', 'w', **p_filled) as ds:\n",
+    "    ds.write(X_filled, 1)\n",
+    "    ds.update_tags(AREA_OR_POINT='Point')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dem-stitcher",
+   "language": "python",
+   "name": "dem-stitcher"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/organize_tile_data/0_Format_and_Organize_Data.ipynb b/notebooks/organize_tile_data/0_Format_and_Organize_Data.ipynb
new file mode 100644
index 0000000..89fef6c
--- /dev/null
+++ b/notebooks/organize_tile_data/0_Format_and_Organize_Data.ipynb
@@ -0,0 +1,1665 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "external-chick",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:01:11.398263Z",
+     "start_time": "2022-06-10T01:01:08.226409Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import rasterio\n",
+    "from shapely.geometry import box, Point\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "from itertools import starmap\n",
+    "import requests\n",
+    "import zipfile\n",
+    "import io\n",
+    "import boto3\n",
+    "from tqdm import tqdm\n",
+    "from rasterio.crs import CRS\n",
+    "\n",
+    "from dem_stitcher.geojson_io import to_geojson_gzip\n",
+    "from dem_stitcher.dem_readers import read_srtm, read_nasadem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "western-carroll",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:01:11.403538Z",
+     "start_time": "2022-06-10T01:01:11.400848Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "DATA_PATH = Path('data')\n",
+    "DATA_PATH.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "overall-harris",
+   "metadata": {},
+   "source": [
+    "# SRTM\n",
+    "\n",
+    "From here: https://figshare.com/articles/dataset/Vector_grid_of_SRTM_1x1_degree_tiles/1332753"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "patent-inflation",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-02T00:08:05.537149Z",
+     "start_time": "2022-06-02T00:08:02.884778Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>N00E006</td>\n",
+       "      <td>POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>N00E009</td>\n",
+       "      <td>POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>N00E010</td>\n",
+       "      <td>POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>N00E011</td>\n",
+       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>N00E012</td>\n",
+       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id                                           geometry\n",
+       "0  N00E006  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....\n",
+       "1  N00E009  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...\n",
+       "2  N00E010  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...\n",
+       "3  N00E011  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...\n",
+       "4  N00E012  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_srtm_raw = gpd.read_file('https://ndownloader.figshare.com/files/1944795')\n",
+    "df_srtm_raw.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "compact-concept",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-02T00:08:05.557136Z",
+     "start_time": "2022-06-02T00:08:05.539186Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>N00E006</td>\n",
+       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
+       "      <td>POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>N00E009</td>\n",
+       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
+       "      <td>POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>N00E010</td>\n",
+       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
+       "      <td>POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>N00E011</td>\n",
+       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
+       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>N00E012</td>\n",
+       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
+       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tile_id                                                url  \\\n",
+       "0  N00E006  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
+       "1  N00E009  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
+       "2  N00E010  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
+       "3  N00E011  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
+       "4  N00E012  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....  \n",
+       "1  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...  \n",
+       "2  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...  \n",
+       "3  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...  \n",
+       "4  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_srtm = df_srtm_raw.copy()\n",
+    "df_srtm.rename(columns={'id': 'tile_id'}, inplace=True)\n",
+    "df_srtm['url'] = df_srtm['tile_id'].map(lambda tile_id: f'http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/{tile_id}.SRTMGL1.hgt.zip')\n",
+    "df_srtm = df_srtm[['tile_id', 'url', 'geometry']].copy()\n",
+    "df_srtm.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "straight-spending",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-02T00:08:06.677700Z",
+     "start_time": "2022-06-02T00:08:05.558917Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/srtm_v3.geojson.zip')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_geojson_gzip(df_srtm, DATA_PATH/'srtm_v3.geojson.zip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "extraordinary-scholar",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-02T00:08:11.162882Z",
+     "start_time": "2022-06-02T00:08:06.680454Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "urls = df_srtm.url.to_list()\n",
+    "\n",
+    "X, p = read_srtm(urls[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "coastal-saver",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-02T00:08:11.751225Z",
+     "start_time": "2022-06-02T00:08:11.164982Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c7405c30>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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5paHMcpuxaj0EQYjmnxA5l6nY7yoQZSLWckjvvogsp3uv42i6N/B1EVlO9/4GZbo3gIgsp3u7QJwRpK7NYqgieXOE1pb2nKZmJWgYQYDZE4JWQjUD/a69niuh2c/sfU+F5ECuoJoJw04mtOYQSGNF6/KBLlENFGIF1dy2D6GHHEGiEFA0Lx2Y5W2xOD1Lz8qjpjNgWyOwVnaREu4sBV1wLlvU3ZEP4pbp3j8E/JSIfBx4FbMybmDi8fmVty2nePfcwXRvzNJgLBt3corOo6IUSGlTo+OaNKlJU8ts1AhpPGF0vUelptuOzJ7IDFNhfC2gUqFRiG1GK2gvCO1lRaOClhLvxnwOOZsz0qyHkikd5Nh6GAEIIZXK7gprbhuwlOtcch+C2k1y6YCdjgXgqNpT7f64quuR/fM+Ku5luvcvAt8PPItZGD+7PPQ2b/fp3medUD5kuWQpKha+nAb6jUi7HRmmkTQSml1lfNWshH5TWFwMpLp8VudKe9m2H/2FDFUmjzPDdiJtJdJmJo0zaWL+Ca0gjZQ0spBnrkCjWREarUZDln9lYuFOrYIVdKmaH0LEkqeWKdm3EiNSV+tfP8OcyIK43XRvVX1r5fVfAn6jPPXp3ueQZTdpQkDryDCJDBsRSUq3U9KmYyQ1wuGTwrBhn9pux8KXGqBqhdia03F4qmO00TH0EZ2I+RWrBCr0B00p25aSLRnIEVIDIKSsSLKqzzgoKqWYa9m3sgrWKb9EMRSQ4dhKkKqymowgJW0zHfsllrUd52Sr8a4CsW66t4g8VfwTAD8GfKk8fgn45yLyc5iTcjndO4nIvog8h21RPg78H/fvUpxHRgg2q6Kprdw6CrkJdJvBHIrBtg2xtVBlmij9Ux1koR9q+i3zDVSLQEgw/q6w/6RwZfuAxVBRx8TBwizJIQWGUUSHYA7QIChmLRA48k1I1qMEK3NMChKtnoNc8iEAGcpELilbi7RS+r3akk4sj+K8cS/Tvf+GiDyL/ZN/A/gJ8One55KSKanRzPdc6iDASrPbi1AdQr8J3YWMXurZvnDIpY1D/v/qMpqs0EpyIM4h9FC/VVN/T2J7tGCvHTOqB/ohMgwREUWTIH0gLI79D5Igdko4+ra3O2tTJ0gSy7SE4+Qp0ePQ5y2FWstScogmEMNw7tKuTxLF+A/c3n/w8ju85x8A/+A2668Cf/FOTtA5/ch4BENCtzbQ2nIO0sjyEtIIhqnSXsloUKZPzriyNePSeMZ7J/tUIfONty6T60Q7G9tW48Ack/vtiMvjGdkKLRiSbWNCVLTO0AVkkJW6DBOJMBTrYcX3AGYthD5brQbYNmFI1mkqhjJgJ5c2+qWIa7nF6Idz2UTGMymdeyMEZDKxlm5dj05qYpuoZwENMEyiRRvGiQtXDri0cchf2HmLy80BtSS+xhWevLRHlyLXBPJBTRoF8lZiSIHXvvMkAJOmJwZlNOrpuoo0BDQoaZoRAvU+JggK1UJNJJL1sAyDErpM6IppEayRjBSh0Lo69kUsr2vpkyhNbvUcigO4QDj3Qhlqo+OSVj1vkW5AolDNBpZ/Xt12ZNi2rcEPXHiT/377q1yIM/50uMDVnS2CKK/dfC/XwwZxu+PJ79tDgO/ubdDujqEXDgQYJ6pRYmgjDAHGCSUSuuPtBWJZlVKSs5YVn6JKbmKp0QAkW8LU8lpWe0WkFSGQADogMb59CPA5wQXCuTtK70bZmKIxok1FntTkpiKPIt1ORbsTaXdKxGJj4PLGIbUkZrmhloGN0PJfTP+UP55fIYbM9uacSdPzvVs3OBhGvHH1AvSWwxAPAvF6JI2V0YGVe3cXMzJYQlV9YElSoXSRS42Q43EGpmQlztPRh1+rgJBRygi/stVYndupXW/bi2XvynOIC4Rz5ywrNRvLlZauhyqSm4Y8KvkOjTCMj/2ABKVPkd+/+TSv7b2Xw75hWnf88JWvsF0teP/GDaqQuT6f8uo3v4dLOzOacU8flLxXE1shzoVmV4gL8zk0u6GkWZdsSYE0tgrP+tBCm7G16VqxzUht3a8zJakqBOtZqWIOSUBS6Wc5JKSKaJ9t0E4+f9YDuEA4d0tTW/XjMlVZFclWJZlGgXY7kCth2LBsRp1HvnX1Ik+9Z5fdNOY7b+0QmsRm3dKExFvzLa7ubxJE6Q9r3rp5ibDZI1EJnQlBGllmZWjNEVm39kHXsPQ7FOthZDkQobc+E7TH56WVEDoh9GLdretAmA/IUaCNt0cqliP9huEh/wOfDlwgnDtnmSOQrW2chiIQQ6Y67MlNoDkQOoF6X5AshL4i7Ufe2L1ihVXzQN4UvvLdJ7i8ccisa5jNxkymLRKVuBcJ1yP9TkZ3evIoEmeBkKxhzFFkokQvYgf1frKoZYTQ5uMUbFXriM1yjoYgKsgAoUuEbjgOdS659fk5TLMGFwjnbukHGEWrvZiOyhDcYtYvEuMhE1JFOhSGmTC6gXWNamCYQrdtjWUP9ibs35jCEJAm0VQDs6DkRomtMP1WZPGEfes3N4V638q9Y2/dpVSg6pTYZkJSYpusejQrkjLVvliBllLqQpZTwDkSAY2CUFKuczrKhzjqMhUjdP2j+Xd+xLhAOHfGsh6hqZGmhnlr/oftsSVI1YHFpZrUCP2mdYbqN0vkAPt2H6ZKvR9InTAA0gvxMJA2AvUVEw4ZhNAK9QzCG4F+i5XycKhaaA7MJyAJQn+c6BTnNp9TenM6StIVMYiWcSmC1tFme7aD+VFgJWmqdLguaddH7fLPmR/CBcK5M0QIG1Pbk9cVLFpk/5A4JMKkIW00IDXDWKzpbG9OymoBza7SbwlhEEbXlWEqtCnSb2dCB9Vh5Gq+jG4NR47IHGF8TRl/1yo6h4nQT8rWIVueQxoLqYk0+xDbTNqoyVUgLhJxd3HUNUqGhGh37FeolunWCdru6BI12wQuKSXhlma9gtdiOM4aUkJThgvbxXqoLAVZxmgIxIOW0fWKam4Vk4dPREInbFxNhF5pu0hqzKmoUahmgkogLkqhVhUYUk2zJ4TORGFxWWhuKuMbmXYIDFMpjW0t6qARxjesNd2wUaHBhAJVdBTRYMOAw4FZO/RmMcjiWCxsNoaataCKhmhJUhJKK/yVEOg5EQdwgXDuBs3oeGRmd872zdwPpI1N4hximxBVuh3rUr35ZqLZG46K/lWENBJiZ36E2JqPwsqxrV29ZGj2LdGpvSB0O0K1EEZ7mfpQ6LbNQsljGO0qOcL8ig3OGe8my82IQhhFwiIRusxwaQOtAtXewnpALAfo9IOJRt+XRrUKqTtKktL+fEYwwAXCuVNCgAvbSErkqVVYhsMW6QeaN25aViWAVsRFpJ4r4+92SMr0WzUqUvo0lGSmRhndEKq5luE4FqLotq3Aa3RTGV/DmsSUL+7RbiIMVi3abZslkUZCNbeQ6v7T1hC32QtMvjuQ60DobDsRupJindW2Fkf+hmyJX1WFLhbQJTR1x6+fU1wgnDtGDg6hqoiHi9L/oUInI2g7ZG9GXNTI1sT8FX0mHnb0O2PaixWz9wYTg2DVncsPvkaoDy0bcti0ZCiNwuKisPmnidhmqsNEt23t6cbXetKooZrDaM8a2o6vWw+H2VMRDVbBOb9SIQlGNxP1wYAM2fwkQGgHpCvWQ8roYoGMRiDF93DOxQFcIJw7JWe07eybdmNS6i968qRBJxuEPZukLfOOatETR6Wr9STQbZb5msUfGMeQDy1NWjIMI6FaKPUeVHOoWmX+nsDsiUA9C2y0mfF3WhApQ3sBtfyHcZsJKUNScjMyP8RC6bbMwThMA6IV9ZCJ+631oiyRC+kHtO9hGGyLMQzHVsU5xwXCuXOqCh03DDsTYh2RwxZUyaOKfHmTsBgIB3OA8o09OqqyHO3ah67dDqQRRwlP9cychc1BYnTT1kRLL4daqOc2oDfMB9JmwzCJpFoY7evRTM4470GV8XcscSsuBkbXImlSgXA04DcshiMHpbT9saOybkwkUj53fR/W4QLh3DmDtY+P+y1Ugbw9ISzMkdfvNMhGRVOFsvdPxFlL2KqpZ1Z6Pb8cSRNhmNrczTQScm2p0mSoFqlUYipxkei3qpJOnZCckSGTGxONfhLonq6ZXA+MrkM86KhuzK0Aqx8IqlQxWuepUW3bi+VEwH44shKkrk0cQn1sQTguEM6do12H3NwnNDWMGvLWxD542dKt+237s0qTSOgy47cOAajm+Th6sbBelHlbaS+ChkC9r8Q2kMaB2GXq3Z5q1hMXA+3FEblkQYYhM7pREpuuNPTT5XZDoVo2eqFUnAZk3pqfYR/kcGxrbV8SoSI6OywXplbBeQ7nX6zDBcK5YyRGZHNqadbFx9BvNwybZvb308Awqom9EjrQOlLNBtIo2syJMg0Lhf7SQL8jxEVFNYP5JWtwO7kGuWoY3eypdluam0J3qSGPKuKsQ7qBtDlidH1gdMO2D2COTZqKtFWZBdNnYhUsW3LvAG7sWYPdGE0M1Po/aNvaxbnl8DZcIJw7RocBmVsuQR5PbAJVqXUY7Saa/cz++yqGZOXW/XZDagKLS9awpd8Q+i0hjaHebhm6ivkT0Ybn7MFoNzOMhdhBP62QPhP3WsZDJo1NIEiKVkKcD8R5j4ogyUKYeVKjlTBMI/X+ylSsyRiZL9AhQd8fJ32d025RJ8EFwrlzcrb+k9MR8eahVXTGwHS/Ixz26CgCU67/lzXDpGK0G4idlVwP4+J4jDC6DvOvb6AXExqVai6Mb2SafQtphiFDNuFJ2yOq3YWlSZRUaLJZDCpCOGwhJSRlQj8gSZE+H4UydWNMriOyGFuUpe3Q+cIEw8VhLS4Qzl0hi84iAUCeNFQ358i8RScjUlOhEZo96wvZzMxnkGob1jt7MtJvWwl2vWdDdiUJ8/faIJtuS5heFZq9gXg4WB7FpYnVU/QZaXvy5phcB/qtSFMHKiC0PcxbO7chId3oqPlLaM1noXVlfodkiVPq/oZ35CTTvcci8gUR+b0y3fvvl/VLIvI5Eflaub+48h6f7n3Gydeuo4dmPYTr+8jugZVTi6BNoN4bmFxLTK4nJm/MkT7Tb1X0G+FoWE41h5CgmgWquUCG9qJaavWmNZwRVUhKdWAf8LAMS5YJV6FXUhMs9HlhQt7ZQLem5pTcm1lZegiW4dkPcH0Xub4LN/fQw7k7JN+Fk8wRa4G/pqp/CRuz95Ey/OZTwCuq+gzwSnl+63TvjwC/ICKlVvZouvcz5faR+3cpzkMlZ3TRonv7aNehW1OGK9vkaY0MloNQHySaGx3SZ4ZpRWqE/fdbanRz0ywMgHoGcWHNZaqZNZy13Aa1rULOhIOF+ToWnY3LU8t9aHZ76v0eyUp7acTsezcZLkzQ6RiGATlcAFgkI4i1kZvPTRz689nj4U54V4FQ46A8rctNsSneL5b1F7FJ3bAy3VtVvw4sp3s/RZnurdaJ41dW3uM8jqTS0HV7k7Q1ZtgwB2I8aEljK9SqDjqkT6XLtVkM9czuZbBbvafU+9DswfY3MvW+pWKHlG3GxubIQpKlqIoYSRsNoR0I7UDcawmLntBn5pcj7aUROmlgPLK6CiBvTdDZoTko6+a4v4Pzjpx0NmcEfgf4z4H/S1V/S0SeXI7eU9U3ReSJcvg9T/d2Hh9kY0L/xDZkZXR1Zn6IzbH1fuwzctiSN8ZoE6jazOiGkCsh9JYYNbqpTK8m5pctwjF9q2d6FVITkEEJi+I7GFWE3daSmIKY87FNVrY9JHIYUx8MbL4hNsEbjrYhcnAIOrHQ5sHMsyTvgBMJRBmd96yIXAB+TUTeaTrWPU/3FpFPYlsRxrJxklN0HhVtRxpHmu/MCNf2rHx6Hqivl45OImgdCG0idBUyQEx63PBFoZ4NVPNkFsdhTzjs0KayqsvSFFeSok1tgjBvCUNCN8YWDmk75LClGjJxv4hCX6ZhSUBnM5jNoKpsROAyMcp5V+4oiqGqN0Xk32G+g7eWA3zL9uFqOeyep3ur6qeBTwPshMsu96cY7TqqWU/YnVl+RAhIyrDokcMFOh3blmNzRK6Fqi1t4VqlnwZybf0hmmtzy25M1l9Cg9g2IJTWcHDcDm7RQl2Rm4hOaipVZP8Q2h4pDWHslwRkY4LOxSyHrj+3zWfvlpNEMa4UywERmQA/DHwFm+L9QjnsBeDXy+OXgI+JyEhEPsDxdO83gX0Rea5ELz6+8h7ncSUr8cbhUTKSlg+3dBZtkK5Hq8Cw2YBYwVZzkBEt8zKAxeWK+VMbDJc2bDsybpC2RxZtue/tQ19Su7U0eom7c6rrM2sOs7Np3aIWLdp25m/Y3TNhmC9WJmd51OJOOIkF8RTwYvFDBOCzqvobIvKbwGdF5BPAN4EfB5/ufe7QbHt8Efv23xjbvnHRolsb5K0xw5ZZD6G1yESuxEQkK2Gw/pKxzWgl5GmN1oGYskUguoU1x43ROmcX56J0vW05DhcIoJOR3XY2LMKxOzvqM6nLhrSeEHXHiJ5yh81OuKzPjZ9/1KfhrCMEZGPDahpihEs7ln8gQnrqEmlakWMosynMYE1NII2E1JR+ktcS9X5PGEoH6iHbcN3WOlEdWQCTsRVUzebWUHZ70/wTB4dH2xvdLpZE25mzsuvNmmhbF4g1fH7xMrv52m33Xp5J6dwzenhoBVwXp+iy8Onijs2bGJS4bO2GpU2Lqg2uydYUJnbL9vVFHJKVdSNiOQ+lMY0eHCJVRJoabVtkNretRV3BYoFmPfKE65DQnNBF69uKe+AkiVKO8+40tW0x9mfIZGJzM/pEWPRU39m3QTZ9RmU5MzNTzzL13mA1F2JDcKAIRWnaIimbFVFX5rDsOrMEJFiyU9dbu7uqQofBrIWug978EC4O94ZbEM69o/aB1tmh+SQ2NpDZnDjDfBGqRBHylU0kw+haS5gP5GltPSRSRmOw4qpFZ/kLt/aETGWQjYi1h0v5yP+h25tI05hYdJ2JQvDvvvuBC4Rzb5QPsM7nR1O/df/g2Jk4amytTLiqdxeE3UPLhtxuIEGcJ2Q+P/p5knq0ts7UNg6vbEGa+miiuB7OoW1LPUUu80EzEqMN9fE06vuCC4Rz/8gZRqOj+ZZyYfuoSEr6gfpPb9px/UC+sIUMyjCJSK6Jg4lAOFyUAqsS6UhlpN5yiviQLAy6MYW6NmFqWxiNzFk6amB26LUW9wkXCOf+UcbVMR5BDLZt2D9E284G1SzDlCJI21HdyEgaW9ZjwOZm5mxFVlU0ByWU0XmlmSzAqCZPx7A5JuxFdP/AWtbHQN6amGOt620r4twTLhDOfUOqCk0ZKUN22Z9ZREMCSmVbBBETiyqiIVi15pCtJVyy7tiaszV1oXp7aDJYMpYMyfwMvaVfU1UmCMNQBvS6/+F+4QLh3De060pH6FKD0XZmUYQSnijOQ60rtKnQKpBGEamC1WesDtDte2Tpf7g1f2FIVr7ddha9KJEKnS+QP7kKWY97TDr3hAuEc3/JGZ0vjp2X1EhVrIe6MnGYjkoIU4ltor08BoEwm1tq9DAgY+vncDRUF8wXAUcl3KQMsbeReSJoSl6IdZ9xgXDuP7d848tkQr64ifTJLIemMr8D1o06V1bxCRyFM7Xvken0yCfxtnyG0mhWVaHzITcPEhcI54FyZDk0lXWFGhJaBfoLY0JrH3qb3zlYtaVmVG1oDmV7Ik1tPoa2LfkRdox3o37wuEA4D5ayLQjz3nwGm1MAq80Yx6OOIGHWom17HKlYOhpjREcmMiJi7eK8Vf1DwwXCebCkBF1vbeaHAVm0hBjITUUcMrkK1himVIMebSW6Ht0/sLTt4lfQMvLPB+s+PFwgnAeKqprlMAzIdIKOR+RJDWJdn6obB+jmxD7wMRw7N5d+h1Da0x8eegr1I8AFwnkoyLR0mg5COOyOLAGZt+ZvmC/I88XxG3K2kOjh4dudkG45PFRcIJwHRwhIUyPTMWBNXqxPpFjNRG2Zk7q3b9mWt1Zeuhg8ctxecx4cOVuWYwhHKdb0g2U/xhLaHJeciKryVvSnELcgnAeOlgpM6YtfISWkD0gdydtT+5bqB89+PIW4BeE8eJap1iUrUmZW2r1MltLJyLtNn1LcgnAeDDHa1mIYkBt7VjPRlk5PMVo9RSnflkWL7u+7z+EU4gLhPDCkqqz3Q7dSdp0z9ANhbp2mZLC+kbrw7cVpxAXCeTCkZNWdS0KwTMjla/v7CFsmGJ33bTitnGRwzlhEviAivyciXxaRv1/Wf0ZE/kREvlhuz6+856dF5HUR+aqI/MjK+g+KyB+U1/5xGaDjnFWW+Qu3SW7Srre06ba1lvnOqeQkFkQL/DVVPRCRGvgPIrIcePPzqvqPVg8WkQ8CHwN+APhzwL8RkT9fhuf8IjZz8/PAy9gIPx+ecx7IGV0VimVZuFdinmre1YJQ46A8rcvtnf5XPwp8RlVbVf068Drw4TK/c1tVf1OtwP9XgB+9p7N3Tj2yktsgIm8XhGXBlTsnTy0nCnOKSBSRL2IDej+nqr9VXvopEfl9EfllEblY1p4GvrXy9m+XtafL41vXb/f7Pikir4rIqx3uvHpsESlNbK1ngw6Di8FjxokEQlWTqj6LTeT+sIj8RWy78P3As8CbwM+Ww2/nV9B3WL/d7/u0qn5IVT/UMDrJKTqnEVXrOO2WwmPLHSVKqepN4N8BH1HVt4pwZOCXgA+Xw74NvH/lbe8D3ijr77vNunNWyfntkQznseMkUYwrInKhPJ4APwx8pfgUlvwY8KXy+CXgYyIyEpEPAM8AX1DVN4F9EXmuRC8+Dvz6/bsUx3HuNyeJYjwFvCgiEROUz6rqb4jI/y0iz2LbhG8APwGgql8Wkc8CfwgMwE+WCAbA3wT+KTDBohcewXCcU4zoKQ8z7YTL+tz4+Xc/0Dm93BLedE4Xn1+8zG6+dtucpFMvECKyD3z1UZ/HQ+Q9wHcf9Uk8RM7b9cLpu+bvVdUrt3vhcUi1/qqqfuhRn8TDQkRe9es92zxO1+zl3o7jrMUFwnGctTwOAvHpR30CDxm/3rPPY3PNp95J6TjOo+NxsCAcx3lEuEA4jrOWUysQIvKR0nDmdRH51KM+n7ulVLpeFZEvraxdEpHPicjXyv3Fldce62Y7IvJ+Efm3IvJaaTD0t8r6Wb7mdU2VHv9r1tJp+DTdgAj8EfCfAQ3we8AHH/V53eW1/HfAXwG+tLL2vwOfKo8/Bfxv5fEHy7WOgA+Uf4NYXvsC8N9gVbH/Cvjrj/ra1lzvU8BfKY+3gP9YrussX7MAm+VxDfwW8NxZuObTakF8GHhdVf9YVTvgM1gjmscOVf33wPVblj8KvFgev8hx45zHvtmOqr6pqr9bHu8Dr2F9P87yNavevqnSY3/Np1Ug1jWdOSs8qVbdSrl/oqzfc7Od04SIfB/wl7Fv1DN9zWuaKj3213xaBeLEzWXOGPfcbOe0ICKbwK8Cf1tV997p0NusPXbXrLdvqrSOx+aaT6tArGs6c1Z4a9lPo9xfLetnotlOaW78q8A/U9V/WZbP9DUv0ZWmSpyBaz6tAvHbwDMi8gERabAu2S894nO6n7wEvFAev8Bx45zHvtlOOb9/Arymqj+38tJZvubbNlXiLFzzo/YAv4Nn+HnMA/5HwN991OdzD9fxL7CenT32DfEJ4DLwCvC1cn9p5fi/W675q6x4sIEPYV27/gj4PylZsKftBvy3mFn8+8AXy+35M37N/xXw/5Vr/hLw98r6Y3/NnmrtOM5aTusWw3GcU4ALhOM4a3GBcBxnLS4QjuOsxQXCcZy1uEA4jrMWFwjHcdbynwCgWDXsbVrPLgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thorough-sentence",
+   "metadata": {},
+   "source": [
+    "# NASADEM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "large-surge",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:27:39.732405Z",
+     "start_time": "2022-02-02T21:27:39.718329Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>N00E006</td>\n",
+       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
+       "      <td>POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>N00E009</td>\n",
+       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
+       "      <td>POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>N00E010</td>\n",
+       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
+       "      <td>POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>N00E011</td>\n",
+       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
+       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>N00E012</td>\n",
+       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
+       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tile_id                                                url  \\\n",
+       "0  N00E006  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
+       "1  N00E009  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
+       "2  N00E010  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
+       "3  N00E011  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
+       "4  N00E012  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....  \n",
+       "1  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...  \n",
+       "2  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...  \n",
+       "3  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...  \n",
+       "4  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_nasadem = df_srtm.copy()\n",
+    "URL = 'https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_HGT.001/2000.02.11'\n",
+    "df_nasadem['url'] = df_nasadem.tile_id.map(lambda tile_id: f'{URL}/NASADEM_HGT_{tile_id.lower()}.zip')\n",
+    "df_nasadem.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "iraqi-anderson",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:28:26.060249Z",
+     "start_time": "2022-02-02T21:27:39.734133Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "urls = df_nasadem.url.to_list()\n",
+    "\n",
+    "X, p = read_nasadem(urls[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "disturbed-occasions",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:28:26.632017Z",
+     "start_time": "2022-02-02T21:28:26.061984Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1ce0dc670>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "amended-model",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:28:27.732435Z",
+     "start_time": "2022-02-02T21:28:26.633688Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/nasadem.geojson.zip')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_geojson_gzip(df_nasadem, DATA_PATH/'nasadem.geojson.zip')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da2a1697",
+   "metadata": {},
+   "source": [
+    "# GLO\n",
+    "\n",
+    "In the past, there was a shapefile that could be accessed, but that is now gone. We go through the buckets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "601da129",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:24:05.123476Z",
+     "start_time": "2022-06-10T01:24:05.120683Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Can update when new year is available\n",
+    "YEAR = '2021'\n",
+    "\n",
+    "# Change resolution to 30 or 90\n",
+    "RES = 90\n",
+    "\n",
+    "assert(RES in [30, 90])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "central-retail",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:24:10.968960Z",
+     "start_time": "2022-06-10T01:24:05.335661Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_30_N00_00_E006_00_ACM.kml',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 58, tzinfo=tzutc()),\n",
+       "  'ETag': '\"8fad074b0c6d0c80716f694674b90a1e\"',\n",
+       "  'Size': 732,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_COG_30_N00_00_E006_00_EDM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 57, tzinfo=tzutc()),\n",
+       "  'ETag': '\"6b9d5ae5591a9ad5b5da334bbe9cf45e\"',\n",
+       "  'Size': 50244,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_COG_30_N00_00_E006_00_FLM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 54, tzinfo=tzutc()),\n",
+       "  'ETag': '\"d34a617f3c36dde49e998546e2ad423d\"',\n",
+       "  'Size': 53031,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_COG_30_N00_00_E006_00_HEM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 56, tzinfo=tzutc()),\n",
+       "  'ETag': '\"5413c19eec8f19605d579230b83a0f78\"',\n",
+       "  'Size': 483015,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_COG_30_N00_00_E006_00_WBM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 57, tzinfo=tzutc()),\n",
+       "  'ETag': '\"17e0cde30aabb19fbfd9c38dd2924ea0\"',\n",
+       "  'Size': 27621,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s3client = boto3.client('s3')\n",
+    "BUCKET_NAME = f'copernicus-dem-{RES}m'\n",
+    "objects = s3client.list_objects(Bucket=BUCKET_NAME, \n",
+    "                                # Change this for previous years\n",
+    "                                Prefix=YEAR)\n",
+    "objects['Contents'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c450d2ae",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:24:10.976109Z",
+     "start_time": "2022-06-10T01:24:10.971493Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "paginator = s3client.get_paginator('list_objects_v2')\n",
+    "pages = paginator.paginate(Bucket=BUCKET_NAME, \n",
+    "                           Prefix='2021')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6a899284",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:44.428238Z",
+     "start_time": "2022-06-10T01:24:10.978116Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "424it [03:33,  1.99it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/AUXFILES/Copernicus_DSM_30_N00_00_E006_00_ACM.kml',\n",
+       " 'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 58, tzinfo=tzutc()),\n",
+       " 'ETag': '\"8fad074b0c6d0c80716f694674b90a1e\"',\n",
+       " 'Size': 732,\n",
+       " 'StorageClass': 'STANDARD'}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_contents = []\n",
+    "for page in tqdm(pages):\n",
+    "    all_contents.extend(page['Contents'])\n",
+    "\n",
+    "all_contents[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fcf64c32",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:44.628225Z",
+     "start_time": "2022-06-10T01:27:44.431478Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': '2021/Copernicus_DSM_COG_30_N00_00_E006_00_DEM/Copernicus_DSM_COG_30_N00_00_E006_00_DEM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 19, 3, 59, tzinfo=tzutc()),\n",
+       "  'ETag': '\"0c3f5310131359cfa2dfb028bf3510fd\"',\n",
+       "  'Size': 490119,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E009_00_DEM/Copernicus_DSM_COG_30_N00_00_E009_00_DEM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 4, 21, 52, 36, tzinfo=tzutc()),\n",
+       "  'ETag': '\"4a63321e22c176df173951a67c79580a\"',\n",
+       "  'Size': 3210718,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': '2021/Copernicus_DSM_COG_30_N00_00_E010_00_DEM/Copernicus_DSM_COG_30_N00_00_E010_00_DEM.tif',\n",
+       "  'LastModified': datetime.datetime(2022, 4, 5, 1, 52, 6, tzinfo=tzutc()),\n",
+       "  'ETag': '\"c577e54674b6ae8abd4b246a25a52936\"',\n",
+       "  'Size': 5763057,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tif_obs = list(filter(lambda ob: '.tif' in ob['Key'], all_contents))\n",
+    "tif_obs = list(filter(lambda ob: ('AUXFILES' not in ob['Key']) and ('PREVIEW' not in ob['Key']), tif_obs))\n",
+    "tif_obs[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b1c702c0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:44.634213Z",
+     "start_time": "2022-06-10T01:27:44.630586Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26475"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(tif_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "755f47d2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:44.642662Z",
+     "start_time": "2022-06-10T01:27:44.636194Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_bbox_glo(tile_id):\n",
+    "    tokens = tile_id.split('_')\n",
+    "    lat_sign = tokens[4]\n",
+    "    lon_sign = tokens[6]\n",
+    "    \n",
+    "    if lat_sign[0] == 'N':\n",
+    "        lat = int(lat_sign[1:])\n",
+    "    elif lat_sign[0] == 'S':\n",
+    "        lat = -int(lat_sign[1:])\n",
+    "    else:\n",
+    "        raise ValueError('Latitude token not expected')\n",
+    "    \n",
+    "    if lon_sign[0] == 'W':\n",
+    "        lon = -int(lon_sign[1:])\n",
+    "    elif lon_sign[0] == 'E':\n",
+    "        lon = int(lon_sign[1:])\n",
+    "    else:\n",
+    "        raise ValueError('Longitude token not expected')\n",
+    "    return box(lon, lat, lon + 1, lat+1)\n",
+    "\n",
+    "def format_glo(data):\n",
+    "    out_data = {}\n",
+    "    key = data['Key']\n",
+    "    tile_id = key.split('/')[-1].replace('.tif', '')\n",
+    "    out_data['tile_id'] = tile_id\n",
+    "    out_data['url'] = f'https://copernicus-dem-{RES}m.s3.amazonaws.com/{key}'\n",
+    "    out_data['last_modified'] = data['LastModified']\n",
+    "    out_data['geometry'] = get_bbox_glo(tile_id)\n",
+    "    return out_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2fcb3e1d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:45.127351Z",
+     "start_time": "2022-06-10T01:27:44.644397Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Copernicus_DSM_COG_30_N00_00_E006_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "      <td>POLYGON ((7.00000 0.00000, 7.00000 1.00000, 6....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Copernicus_DSM_COG_30_N00_00_E009_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "      <td>POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Copernicus_DSM_COG_30_N00_00_E010_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Copernicus_DSM_COG_30_N00_00_E011_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Copernicus_DSM_COG_30_N00_00_E012_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "      <td>POLYGON ((13.00000 0.00000, 13.00000 1.00000, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    tile_id  \\\n",
+       "0  Copernicus_DSM_COG_30_N00_00_E006_00_DEM   \n",
+       "1  Copernicus_DSM_COG_30_N00_00_E009_00_DEM   \n",
+       "2  Copernicus_DSM_COG_30_N00_00_E010_00_DEM   \n",
+       "3  Copernicus_DSM_COG_30_N00_00_E011_00_DEM   \n",
+       "4  Copernicus_DSM_COG_30_N00_00_E012_00_DEM   \n",
+       "\n",
+       "                                                 url  \\\n",
+       "0  https://copernicus-dem-90m.s3.amazonaws.com/20...   \n",
+       "1  https://copernicus-dem-90m.s3.amazonaws.com/20...   \n",
+       "2  https://copernicus-dem-90m.s3.amazonaws.com/20...   \n",
+       "3  https://copernicus-dem-90m.s3.amazonaws.com/20...   \n",
+       "4  https://copernicus-dem-90m.s3.amazonaws.com/20...   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((7.00000 0.00000, 7.00000 1.00000, 6....  \n",
+       "1  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...  \n",
+       "2  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...  \n",
+       "3  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...  \n",
+       "4  POLYGON ((13.00000 0.00000, 13.00000 1.00000, ...  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tile_data = list(map(format_glo, tif_obs))\n",
+    "\n",
+    "df_glo = pd.DataFrame(tile_data)\n",
+    "df_glo = df_glo.sort_values(by=['tile_id', 'last_modified'], ascending=True)\n",
+    "df_glo.drop_duplicates(subset='tile_id', keep='last', inplace=True)\n",
+    "df_glo = gpd.GeoDataFrame(df_glo, geometry=df_glo.geometry, crs=CRS.from_epsg(4326))\n",
+    "df_glo = df_glo[['tile_id', 'url', 'geometry']].copy()\n",
+    "df_glo.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1b82f848",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:47.387715Z",
+     "start_time": "2022-06-10T01:27:45.129261Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/glo_90.geojson.zip')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_geojson_gzip(df_glo, DATA_PATH/f'glo_{RES}.geojson.zip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "another-sponsorship",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-10T01:27:52.633026Z",
+     "start_time": "2022-06-10T01:27:47.389711Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 1200, 'height': 1200, 'count': 1, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0008333333333333334, 0.0, 17.999583333333334,\n",
+       "       0.0, -0.0008333333333333334, 1.0004166666666667), 'blockxsize': 2048, 'blockysize': 2048, 'tiled': True, 'compress': 'deflate', 'interleave': 'band'}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "urls = df_glo.url.tolist()\n",
+    "k = 10\n",
+    "\n",
+    "with rasterio.open(urls[k]) as ds:\n",
+    "    X = ds.read(1)\n",
+    "    p = ds.profile\n",
+    "plt.imshow(X)\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "generous-selling",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-06-02T23:53:00.097929Z",
+     "start_time": "2021-06-02T23:53:00.095392Z"
+    }
+   },
+   "source": [
+    "# 3dep\n",
+    "\n",
+    "## Read through S3 Bucket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "entire-power",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:28:48.559716Z",
+     "start_time": "2022-02-02T21:28:45.318116Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.gpkg',\n",
+       "  'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
+       "  'ETag': '\"c60495b0dcba350365514933a716d586\"',\n",
+       "  'Size': 98304,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.jpg',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 35, tzinfo=tzutc()),\n",
+       "  'ETag': '\"3ccc340f6c4664b0183267239f40cf6e\"',\n",
+       "  'Size': 969,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.tif',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
+       "  'ETag': '\"90d8ba82df12261245aa09cbbaf22589\"',\n",
+       "  'Size': 1542700,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.xml',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
+       "  'ETag': '\"5fe5e0cc9932d4bd9413d1482047977a\"',\n",
+       "  'Size': 11221,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e163/USGS_1_n06e163.gpkg',\n",
+       "  'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
+       "  'ETag': '\"f33e5ede13fe25c7fb5f774d84d278bc\"',\n",
+       "  'Size': 98304,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s3client = boto3.client('s3')\n",
+    "BUCKET_NAME = 'prd-tnm'\n",
+    "objects = s3client.list_objects(Bucket=BUCKET_NAME,\n",
+    "                                Prefix='StagedProducts/Elevation/1/TIFF/')\n",
+    "objects['Contents'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "affecting-plate",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:28:48.596019Z",
+     "start_time": "2022-02-02T21:28:48.561249Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "paginator = s3client.get_paginator('list_objects_v2')\n",
+    "pages = paginator.paginate(Bucket=BUCKET_NAME, \n",
+    "                           Prefix='StagedProducts/Elevation/1/TIFF/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "international-court",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:03.551392Z",
+     "start_time": "2022-02-02T21:28:48.597577Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "55it [00:14,  3.68it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.gpkg',\n",
+       " 'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
+       " 'ETag': '\"c60495b0dcba350365514933a716d586\"',\n",
+       " 'Size': 98304,\n",
+       " 'StorageClass': 'STANDARD'}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_contents = []\n",
+    "for page in tqdm(pages):\n",
+    "    all_contents += page['Contents']\n",
+    "\n",
+    "all_contents[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "capital-glass",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:03.569770Z",
+     "start_time": "2022-02-02T21:29:03.553226Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.tif',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
+       "  'ETag': '\"90d8ba82df12261245aa09cbbaf22589\"',\n",
+       "  'Size': 1542700,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e163/USGS_1_n06e163.tif',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
+       "  'ETag': '\"f2799568910a5217bafdcc6f122d5729\"',\n",
+       "  'Size': 1253435,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n07e134/USGS_1_n07e134.tif',\n",
+       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 35, tzinfo=tzutc()),\n",
+       "  'ETag': '\"1fbc41c777e38a20b20f5a222439dca2\"',\n",
+       "  'Size': 1125374,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tif_obs = list(filter(lambda ob: '.tif' in ob['Key'], all_contents))\n",
+    "tif_obs[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "competitive-significance",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:03.575664Z",
+     "start_time": "2022-02-02T21:29:03.571489Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_bbox(tile_id):\n",
+    "    lat_sign = tile_id[0]\n",
+    "    lat_num = int(tile_id[1:3])\n",
+    "    lon_sign = tile_id[3]\n",
+    "    lon_num = int(tile_id[4:7])\n",
+    "    \n",
+    "    assert((lat_sign in ['n', 's']) and (lon_sign in ['e', 'w']))\n",
+    "    lon = lon_num if lon_sign == 'e' else -lon_num\n",
+    "    lat = lat_num if lat_sign == 'n' else -lat_num\n",
+    "    \n",
+    "    return box(lon, lat - 1, lon + 1, lat)\n",
+    "\n",
+    "def format_data(data):\n",
+    "    out_data = {}\n",
+    "    key = data['Key']\n",
+    "    out_data['tile_id'] = key.split('/')[-2]\n",
+    "    out_data['url'] = f'https://prd-tnm.s3.amazonaws.com/{key}'\n",
+    "    out_data['last_modified'] = data['LastModified']\n",
+    "    out_data['geometry'] = get_bbox(out_data['tile_id'])\n",
+    "    return out_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "capable-proof",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:03.854848Z",
+     "start_time": "2022-02-02T21:29:03.577160Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3811</th>\n",
+       "      <td>n06e162</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((163.00000 5.00000, 163.00000 6.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3812</th>\n",
+       "      <td>n06e163</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((164.00000 5.00000, 164.00000 6.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3813</th>\n",
+       "      <td>n07e134</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((135.00000 6.00000, 135.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3814</th>\n",
+       "      <td>n07e151</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((152.00000 6.00000, 152.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3815</th>\n",
+       "      <td>n07e152</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((153.00000 6.00000, 153.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tile_id                                                url  \\\n",
+       "3811  n06e162  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "3812  n06e163  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "3813  n07e134  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "3814  n07e151  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "3815  n07e152  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "\n",
+       "                                               geometry  \n",
+       "3811  POLYGON ((163.00000 5.00000, 163.00000 6.00000...  \n",
+       "3812  POLYGON ((164.00000 5.00000, 164.00000 6.00000...  \n",
+       "3813  POLYGON ((135.00000 6.00000, 135.00000 7.00000...  \n",
+       "3814  POLYGON ((152.00000 6.00000, 152.00000 7.00000...  \n",
+       "3815  POLYGON ((153.00000 6.00000, 153.00000 7.00000...  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tile_data = list(map(format_data, tif_obs))\n",
+    "\n",
+    "df_3dep = pd.DataFrame(tile_data)\n",
+    "df_3dep = df_3dep.sort_values(by=['tile_id', 'last_modified'], ascending=True)\n",
+    "df_3dep.drop_duplicates(subset='tile_id', keep='last', inplace=True)\n",
+    "df_3dep = gpd.GeoDataFrame(df_3dep, geometry=df_3dep.geometry, crs=CRS.from_epsg(4326))\n",
+    "df_3dep = df_3dep[['tile_id', 'url', 'geometry']].copy()\n",
+    "df_3dep.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "attended-lunch",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:03.859955Z",
+     "start_time": "2022-02-02T21:29:03.856435Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/1/TIFF/historical/n06e162/USGS_1_n06e162_20130911.tif'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_3dep.url.tolist()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "qualified-moldova",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:04.142852Z",
+     "start_time": "2022-02-02T21:29:03.861434Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/3dep.geojson.zip')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df_3dep.to_file('3dep.geojson', driver='GeoJSON')\n",
+    "to_geojson_gzip(df_3dep, DATA_PATH/'3dep.geojson.zip')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "behavioral-mount",
+   "metadata": {},
+   "source": [
+    "# Ned1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "essential-daughter",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:07.310341Z",
+     "start_time": "2022-02-02T21:29:04.144424Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': 'StagedProducts/Elevation/1/IMG/Thumbs (2).db',\n",
+       "  'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 53, tzinfo=tzutc()),\n",
+       "  'ETag': '\"35a3ffdeaa2e01bc650852ba3b148076\"',\n",
+       "  'Size': 10240,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/Thumbs.db',\n",
+       "  'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 54, tzinfo=tzutc()),\n",
+       "  'ETag': '\"94e7b4e49dd2eaba551bb7f9c3f5fb66\"',\n",
+       "  'Size': 15872,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG.zip',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
+       "  'ETag': '\"afec7651ba529aa7946beaa4bdd55585\"',\n",
+       "  'Size': 325350,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG_thumb.jpg',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 29, tzinfo=tzutc()),\n",
+       "  'ETag': '\"1a5a97eb77835437042e550f70014f63\"',\n",
+       "  'Size': 870,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w067_IMG.zip',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
+       "  'ETag': '\"fb0273eabf422c0488571586c50f243a\"',\n",
+       "  'Size': 2472963,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s3client = boto3.client('s3')\n",
+    "BUCKET_NAME = 'prd-tnm'\n",
+    "objects = s3client.list_objects(Bucket=BUCKET_NAME,\n",
+    "                                Prefix='StagedProducts/Elevation/1/IMG/')\n",
+    "objects['Contents'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "varying-grade",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:07.314750Z",
+     "start_time": "2022-02-02T21:29:07.312020Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "paginator = s3client.get_paginator('list_objects_v2')\n",
+    "pages_ned1 = paginator.paginate(Bucket=BUCKET_NAME, \n",
+    "                                Prefix='StagedProducts/Elevation/1/IMG/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "square-prerequisite",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:09.648598Z",
+     "start_time": "2022-02-02T21:29:07.316455Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "9it [00:02,  3.87it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Key': 'StagedProducts/Elevation/1/IMG/Thumbs (2).db',\n",
+       " 'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 53, tzinfo=tzutc()),\n",
+       " 'ETag': '\"35a3ffdeaa2e01bc650852ba3b148076\"',\n",
+       " 'Size': 10240,\n",
+       " 'StorageClass': 'STANDARD'}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_contents_ned1 = []\n",
+    "for page in tqdm(pages_ned1):\n",
+    "    all_contents_ned1 += page['Contents']\n",
+    "\n",
+    "all_contents_ned1[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "focused-berry",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:09.659311Z",
+     "start_time": "2022-02-02T21:29:09.653740Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG.zip',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
+       "  'ETag': '\"afec7651ba529aa7946beaa4bdd55585\"',\n",
+       "  'Size': 325350,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w067_IMG.zip',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
+       "  'ETag': '\"fb0273eabf422c0488571586c50f243a\"',\n",
+       "  'Size': 2472963,\n",
+       "  'StorageClass': 'STANDARD'},\n",
+       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w068_IMG.zip',\n",
+       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 24, tzinfo=tzutc()),\n",
+       "  'ETag': '\"2be372ac1356440c568f671f4ebd5271\"',\n",
+       "  'Size': 669595,\n",
+       "  'StorageClass': 'STANDARD'}]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zip_ob = list(filter(lambda ob: '.zip' in ob['Key'], all_contents_ned1))\n",
+    "zip_ob[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "republican-bristol",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:09.665788Z",
+     "start_time": "2022-02-02T21:29:09.660984Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_bbox(tile_id):\n",
+    "    lat_sign = tile_id[0]\n",
+    "    lat_num = int(tile_id[1:3])\n",
+    "    lon_sign = tile_id[3]\n",
+    "    lon_num = int(tile_id[4:7])\n",
+    "    \n",
+    "    assert((lat_sign in ['n', 's']) and (lon_sign in ['e', 'w']))\n",
+    "    lon = lon_num if lon_sign == 'e' else -lon_num\n",
+    "    lat = lat_num if lat_sign == 'n' else -lat_num\n",
+    "    \n",
+    "    return box(lon, lat - 1, lon + 1, lat)\n",
+    "\n",
+    "def format_data(data):\n",
+    "    out_data = {}\n",
+    "    key = data['Key']\n",
+    "    tile_id = key.split('/')[-1]\n",
+    "    tile_id_tokens = tile_id.split('_')\n",
+    "    tile_id_tokens = list(filter(lambda x: x[0] in ['n', 's'], tile_id_tokens))\n",
+    "    assert(len(tile_id_tokens) == 1)\n",
+    "    out_data['tile_id'] = tile_id_tokens[0].replace('.zip', '')\n",
+    "    out_data['url'] = f'https://prd-tnm.s3.amazonaws.com/{key}'\n",
+    "    out_data['last_modified'] = data['LastModified']\n",
+    "    out_data['geometry'] = get_bbox(out_data['tile_id'])\n",
+    "    return out_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "affected-light",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:09.736776Z",
+     "start_time": "2022-02-02T21:29:09.667330Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>n06e162</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((163.00000 5.00000, 163.00000 6.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1018</th>\n",
+       "      <td>n06e163</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((164.00000 5.00000, 164.00000 6.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1019</th>\n",
+       "      <td>n07e134</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((135.00000 6.00000, 135.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1020</th>\n",
+       "      <td>n07e151</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((152.00000 6.00000, 152.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1021</th>\n",
+       "      <td>n07e152</td>\n",
+       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
+       "      <td>POLYGON ((153.00000 6.00000, 153.00000 7.00000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tile_id                                                url  \\\n",
+       "1017  n06e162  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "1018  n06e163  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "1019  n07e134  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "1020  n07e151  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "1021  n07e152  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
+       "\n",
+       "                                               geometry  \n",
+       "1017  POLYGON ((163.00000 5.00000, 163.00000 6.00000...  \n",
+       "1018  POLYGON ((164.00000 5.00000, 164.00000 6.00000...  \n",
+       "1019  POLYGON ((135.00000 6.00000, 135.00000 7.00000...  \n",
+       "1020  POLYGON ((152.00000 6.00000, 152.00000 7.00000...  \n",
+       "1021  POLYGON ((153.00000 6.00000, 153.00000 7.00000...  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tile_data_ned1 = list(map(format_data, zip_ob))\n",
+    "\n",
+    "df_ned1 = pd.DataFrame(tile_data_ned1)\n",
+    "df_ned1 = df_ned1.sort_values(by=['tile_id', 'last_modified'], ascending=True)\n",
+    "df_ned1.drop_duplicates(subset='tile_id', keep='last', inplace=True)\n",
+    "df_ned1 = gpd.GeoDataFrame(df_ned1, geometry=df_ned1.geometry, crs=CRS.from_epsg(4326))\n",
+    "df_ned1 = df_ned1[['tile_id', 'url', 'geometry']].copy()\n",
+    "df_ned1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "north-wheat",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-02-02T21:29:10.010145Z",
+     "start_time": "2022-02-02T21:29:09.738259Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/ned1.geojson.zip')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df_ned1.to_file('ned1.geojson', driver='GeoJSON')\n",
+    "to_geojson_gzip(df_ned1, DATA_PATH/'ned1.geojson.zip')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dem-stitcher",
+   "language": "python",
+   "name": "dem-stitcher"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/organize_tile_data/1_Missing_glo-30_tiles.ipynb b/notebooks/organize_tile_data/1_Missing_glo-30_tiles.ipynb
new file mode 100644
index 0000000..473f330
--- /dev/null
+++ b/notebooks/organize_tile_data/1_Missing_glo-30_tiles.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c309f51c",
+   "metadata": {},
+   "source": [
+    "This notebook should be run after `glo_30.geojson.zip` and `glo_90.geojson.zip` have been generated in the previous notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5ddd210f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:04.834894Z",
+     "start_time": "2022-06-14T23:20:02.633183Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from dem_stitcher.geojson_io import read_geojson_gzip, to_geojson_gzip\n",
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1c17ffa6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:06.314449Z",
+     "start_time": "2022-06-14T23:20:04.837433Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_glo30 = read_geojson_gzip('data/glo_30.geojson.zip')\n",
+    "df_glo90 = read_geojson_gzip('data/glo_90.geojson.zip')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e140b3fb",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:11.149804Z",
+     "start_time": "2022-06-14T23:20:06.321542Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_glo90.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "da18ae07",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:15.816133Z",
+     "start_time": "2022-06-14T23:20:11.153889Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s6/fbsc26cd0ddffbp3_knts_9c0wcxj1/T/ipykernel_85443/1417027846.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "\n",
+      "  geometry_shrunk = df_glo30.geometry.buffer(-.01).unary_union\n"
+     ]
+    }
+   ],
+   "source": [
+    "geometry_shrunk = df_glo30.geometry.buffer(-.01).unary_union"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b7ffe3b0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:21.442994Z",
+     "start_time": "2022-06-14T23:20:15.820260Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_indices = ~(df_glo90.geometry.intersects(geometry_shrunk))\n",
+    "np.sum(missing_indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d59dbdf",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:21.461636Z",
+     "start_time": "2022-06-14T23:20:21.446541Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>tile_id</th>\n",
+       "      <th>url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((46.00000 38.00000, 46.00000 39.00000...</td>\n",
+       "      <td>Copernicus_DSM_COG_30_N38_00_E045_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((47.00000 38.00000, 47.00000 39.00000...</td>\n",
+       "      <td>Copernicus_DSM_COG_30_N38_00_E046_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((49.00000 38.00000, 49.00000 39.00000...</td>\n",
+       "      <td>Copernicus_DSM_COG_30_N38_00_E048_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POLYGON ((50.00000 38.00000, 50.00000 39.00000...</td>\n",
+       "      <td>Copernicus_DSM_COG_30_N38_00_E049_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POLYGON ((45.00000 39.00000, 45.00000 40.00000...</td>\n",
+       "      <td>Copernicus_DSM_COG_30_N39_00_E044_00_DEM</td>\n",
+       "      <td>https://copernicus-dem-90m.s3.amazonaws.com/20...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            geometry  \\\n",
+       "0  POLYGON ((46.00000 38.00000, 46.00000 39.00000...   \n",
+       "1  POLYGON ((47.00000 38.00000, 47.00000 39.00000...   \n",
+       "2  POLYGON ((49.00000 38.00000, 49.00000 39.00000...   \n",
+       "3  POLYGON ((50.00000 38.00000, 50.00000 39.00000...   \n",
+       "4  POLYGON ((45.00000 39.00000, 45.00000 40.00000...   \n",
+       "\n",
+       "                                    tile_id  \\\n",
+       "0  Copernicus_DSM_COG_30_N38_00_E045_00_DEM   \n",
+       "1  Copernicus_DSM_COG_30_N38_00_E046_00_DEM   \n",
+       "2  Copernicus_DSM_COG_30_N38_00_E048_00_DEM   \n",
+       "3  Copernicus_DSM_COG_30_N38_00_E049_00_DEM   \n",
+       "4  Copernicus_DSM_COG_30_N39_00_E044_00_DEM   \n",
+       "\n",
+       "                                                 url  \n",
+       "0  https://copernicus-dem-90m.s3.amazonaws.com/20...  \n",
+       "1  https://copernicus-dem-90m.s3.amazonaws.com/20...  \n",
+       "2  https://copernicus-dem-90m.s3.amazonaws.com/20...  \n",
+       "3  https://copernicus-dem-90m.s3.amazonaws.com/20...  \n",
+       "4  https://copernicus-dem-90m.s3.amazonaws.com/20...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_glo90_missing = df_glo90[missing_indices].reset_index(drop=True)\n",
+    "df_glo90_missing.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0285a177",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:21.689722Z",
+     "start_time": "2022-06-14T23:20:21.463470Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(36.0, 44.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
+    "\n",
+    "df_glo90_missing.boundary.plot(ax=ax, zorder=2)\n",
+    "world.plot(ax=ax, alpha=.5, zorder=1)\n",
+    "\n",
+    "xmin, ymin, xmax, ymax = df_glo90_missing.total_bounds\n",
+    "ax.set_xlim(xmin - 2, xmax + 2)\n",
+    "ax.set_ylim(ymin - 2, ymax + 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b598b625",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:21.694016Z",
+     "start_time": "2022-06-14T23:20:21.691550Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "DATA_PATH = Path('data')\n",
+    "DATA_PATH.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "933794c5",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-14T23:20:21.703808Z",
+     "start_time": "2022-06-14T23:20:21.695771Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('data/glo_90_missing.geojson.zip')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_geojson_gzip(df_glo90_missing, DATA_PATH/'glo_90_missing.geojson.zip')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dem-stitcher",
+   "language": "python",
+   "name": "dem-stitcher"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/organize_tile_data/Format_and_Organize_Data.ipynb b/notebooks/organize_tile_data/Format_and_Organize_Data.ipynb
deleted file mode 100644
index c9cf75b..0000000
--- a/notebooks/organize_tile_data/Format_and_Organize_Data.ipynb
+++ /dev/null
@@ -1,1429 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "leading-joshua",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:37:04.611830Z",
-     "start_time": "2022-02-02T21:37:03.384950Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import geopandas as gpd\n",
-    "import rasterio\n",
-    "from shapely.geometry import box, Point\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pathlib import Path\n",
-    "from itertools import starmap\n",
-    "import requests\n",
-    "import zipfile\n",
-    "import io\n",
-    "import boto3\n",
-    "from tqdm import tqdm\n",
-    "from rasterio.crs import CRS\n",
-    "\n",
-    "from dem_stitcher.geojson_io import to_geojson_gzip\n",
-    "from dem_stitcher.dem_readers import read_srtm, read_nasadem"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "relevant-terror",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:26:42.686606Z",
-     "start_time": "2022-02-02T21:26:42.683979Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "DATA_PATH = Path('data')\n",
-    "DATA_PATH.mkdir(exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "relevant-liberty",
-   "metadata": {},
-   "source": [
-    "# SRTM\n",
-    "\n",
-    "From here: https://figshare.com/articles/dataset/Vector_grid_of_SRTM_1x1_degree_tiles/1332753"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "verbal-concentration",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:26:48.924786Z",
-     "start_time": "2022-02-02T21:26:44.108646Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "      <th>3</th>\n",
-       "      <td>N00E011</td>\n",
-       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>N00E012</td>\n",
-       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        id                                           geometry\n",
-       "0  N00E006  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....\n",
-       "1  N00E009  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...\n",
-       "2  N00E010  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...\n",
-       "3  N00E011  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...\n",
-       "4  N00E012  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ..."
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_srtm_raw = gpd.read_file('https://ndownloader.figshare.com/files/1944795')\n",
-    "df_srtm_raw.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "greatest-moscow",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:26:48.943563Z",
-     "start_time": "2022-02-02T21:26:48.926865Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tile_id</th>\n",
-       "      <th>url</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>N00E006</td>\n",
-       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
-       "      <td>POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>N00E009</td>\n",
-       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
-       "      <td>POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>N00E010</td>\n",
-       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <td>N00E011</td>\n",
-       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
-       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>N00E012</td>\n",
-       "      <td>http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...</td>\n",
-       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   tile_id                                                url  \\\n",
-       "0  N00E006  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
-       "1  N00E009  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
-       "2  N00E010  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
-       "3  N00E011  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
-       "4  N00E012  http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.00...   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....  \n",
-       "1  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...  \n",
-       "2  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...  \n",
-       "3  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...  \n",
-       "4  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_srtm = df_srtm_raw.copy()\n",
-    "df_srtm.rename(columns={'id': 'tile_id'}, inplace=True)\n",
-    "df_srtm['url'] = df_srtm['tile_id'].map(lambda tile_id: f'http://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/{tile_id}.SRTMGL1.hgt.zip')\n",
-    "df_srtm = df_srtm[['tile_id', 'url', 'geometry']].copy()\n",
-    "df_srtm.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "developmental-allergy",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:26:50.083568Z",
-     "start_time": "2022-02-02T21:26:48.945264Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/srtm_v3.geojson.zip')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_geojson_gzip(df_srtm, DATA_PATH/'srtm_v3.geojson.zip')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "therapeutic-dating",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:27:39.149495Z",
-     "start_time": "2022-02-02T21:26:50.086300Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "urls = df_srtm.url.to_list()\n",
-    "\n",
-    "X, p = read_srtm(urls[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "center-reduction",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:27:39.715927Z",
-     "start_time": "2022-02-02T21:27:39.151038Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1ce187610>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(X)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "artificial-content",
-   "metadata": {},
-   "source": [
-    "# NASADEM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "liquid-meaning",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:27:39.732405Z",
-     "start_time": "2022-02-02T21:27:39.718329Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tile_id</th>\n",
-       "      <th>url</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>N00E006</td>\n",
-       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
-       "      <td>POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>N00E009</td>\n",
-       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
-       "      <td>POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>N00E010</td>\n",
-       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
-       "      <td>POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>N00E011</td>\n",
-       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
-       "      <td>POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>N00E012</td>\n",
-       "      <td>https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...</td>\n",
-       "      <td>POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   tile_id                                                url  \\\n",
-       "0  N00E006  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
-       "1  N00E009  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
-       "2  N00E010  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
-       "3  N00E011  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
-       "4  N00E012  https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_H...   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((6.00000 0.00000, 6.00000 1.00000, 7....  \n",
-       "1  POLYGON ((9.00000 0.00000, 9.00000 1.00000, 10...  \n",
-       "2  POLYGON ((10.00000 0.00000, 10.00000 1.00000, ...  \n",
-       "3  POLYGON ((11.00000 0.00000, 11.00000 1.00000, ...  \n",
-       "4  POLYGON ((12.00000 0.00000, 12.00000 1.00000, ...  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_nasadem = df_srtm.copy()\n",
-    "URL = 'https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_HGT.001/2000.02.11'\n",
-    "df_nasadem['url'] = df_nasadem.tile_id.map(lambda tile_id: f'{URL}/NASADEM_HGT_{tile_id.lower()}.zip')\n",
-    "df_nasadem.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "expired-operations",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:26.060249Z",
-     "start_time": "2022-02-02T21:27:39.734133Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "urls = df_nasadem.url.to_list()\n",
-    "\n",
-    "X, p = read_nasadem(urls[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "printable-trinity",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:26.632017Z",
-     "start_time": "2022-02-02T21:28:26.061984Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1ce0dc670>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "seeing-salvation",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:27.732435Z",
-     "start_time": "2022-02-02T21:28:26.633688Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/nasadem.geojson.zip')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_geojson_gzip(df_nasadem, DATA_PATH/'nasadem.geojson.zip')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "running-security",
-   "metadata": {},
-   "source": [
-    "# GLO-30"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "vocational-scroll",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:31.689312Z",
-     "start_time": "2022-02-02T21:28:27.734003Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Copernicus_DSM_COG_10_N85_00_W180_00_DEM</td>\n",
-       "      <td>POLYGON ((-180.00139 85.00014, -180.00139 86.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Copernicus_DSM_COG_10_N85_00_W179_00_DEM</td>\n",
-       "      <td>POLYGON ((-179.00139 85.00014, -179.00139 86.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Copernicus_DSM_COG_10_N85_00_W178_00_DEM</td>\n",
-       "      <td>POLYGON ((-178.00139 85.00014, -178.00139 86.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Copernicus_DSM_COG_10_N85_00_W177_00_DEM</td>\n",
-       "      <td>POLYGON ((-177.00139 85.00014, -177.00139 86.0...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Copernicus_DSM_COG_10_N85_00_W176_00_DEM</td>\n",
-       "      <td>POLYGON ((-176.00139 85.00014, -176.00139 86.0...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                         id  \\\n",
-       "0  Copernicus_DSM_COG_10_N85_00_W180_00_DEM   \n",
-       "1  Copernicus_DSM_COG_10_N85_00_W179_00_DEM   \n",
-       "2  Copernicus_DSM_COG_10_N85_00_W178_00_DEM   \n",
-       "3  Copernicus_DSM_COG_10_N85_00_W177_00_DEM   \n",
-       "4  Copernicus_DSM_COG_10_N85_00_W176_00_DEM   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((-180.00139 85.00014, -180.00139 86.0...  \n",
-       "1  POLYGON ((-179.00139 85.00014, -179.00139 86.0...  \n",
-       "2  POLYGON ((-178.00139 85.00014, -178.00139 86.0...  \n",
-       "3  POLYGON ((-177.00139 85.00014, -177.00139 86.0...  \n",
-       "4  POLYGON ((-176.00139 85.00014, -176.00139 86.0...  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_glo30_raw = gpd.read_file('https://copernicus-dem-30m.s3.amazonaws.com/grid.zip')\n",
-    "df_glo30_raw.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "convinced-triangle",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:31.698779Z",
-     "start_time": "2022-02-02T21:28:31.692722Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_glo30 = df_glo30_raw.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "invisible-return",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:31.730464Z",
-     "start_time": "2022-02-02T21:28:31.700653Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_glo30.rename(columns={'id': 'tile_id'}, inplace=True)\n",
-    "glo30_URL = 'https://copernicus-dem-30m.s3.amazonaws.com'\n",
-    "df_glo30['url'] = df_glo30['tile_id'].map(lambda tile_id: f'{glo30_URL}/{tile_id}/{tile_id}.tif')\n",
-    "df_glo30 = df_glo30[['tile_id', 'url', 'geometry']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "minute-nerve",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:39.723133Z",
-     "start_time": "2022-02-02T21:28:31.732472Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1cf9572e0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "urls = df_glo30.url.tolist()\n",
-    "k = 32589\n",
-    "\n",
-    "with rasterio.open(urls[k]) as ds:\n",
-    "    X = ds.read(1)\n",
-    "plt.imshow(X)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "faced-pension",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:45.316455Z",
-     "start_time": "2022-02-02T21:28:39.724742Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/glo_30.geojson.zip')"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_geojson_gzip(df_glo30, DATA_PATH/'glo_30.geojson.zip')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "maritime-compensation",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2021-06-02T23:53:00.097929Z",
-     "start_time": "2021-06-02T23:53:00.095392Z"
-    }
-   },
-   "source": [
-    "# 3dep\n",
-    "\n",
-    "## Read through S3 Bucket"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "extreme-potter",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:48.559716Z",
-     "start_time": "2022-02-02T21:28:45.318116Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.gpkg',\n",
-       "  'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
-       "  'ETag': '\"c60495b0dcba350365514933a716d586\"',\n",
-       "  'Size': 98304,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.jpg',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 35, tzinfo=tzutc()),\n",
-       "  'ETag': '\"3ccc340f6c4664b0183267239f40cf6e\"',\n",
-       "  'Size': 969,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.tif',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
-       "  'ETag': '\"90d8ba82df12261245aa09cbbaf22589\"',\n",
-       "  'Size': 1542700,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.xml',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
-       "  'ETag': '\"5fe5e0cc9932d4bd9413d1482047977a\"',\n",
-       "  'Size': 11221,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e163/USGS_1_n06e163.gpkg',\n",
-       "  'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
-       "  'ETag': '\"f33e5ede13fe25c7fb5f774d84d278bc\"',\n",
-       "  'Size': 98304,\n",
-       "  'StorageClass': 'STANDARD'}]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s3client = boto3.client('s3')\n",
-    "BUCKET_NAME = 'prd-tnm'\n",
-    "objects = s3client.list_objects(Bucket=BUCKET_NAME,\n",
-    "                                Prefix='StagedProducts/Elevation/1/TIFF/')\n",
-    "objects['Contents'][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "running-melbourne",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:28:48.596019Z",
-     "start_time": "2022-02-02T21:28:48.561249Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "paginator = s3client.get_paginator('list_objects_v2')\n",
-    "pages = paginator.paginate(Bucket=BUCKET_NAME, \n",
-    "                           Prefix='StagedProducts/Elevation/1/TIFF/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "geological-journal",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:03.551392Z",
-     "start_time": "2022-02-02T21:28:48.597577Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "55it [00:14,  3.68it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.gpkg',\n",
-       " 'LastModified': datetime.datetime(2021, 12, 14, 2, 16, 4, tzinfo=tzutc()),\n",
-       " 'ETag': '\"c60495b0dcba350365514933a716d586\"',\n",
-       " 'Size': 98304,\n",
-       " 'StorageClass': 'STANDARD'}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_contents = []\n",
-    "for page in tqdm(pages):\n",
-    "    all_contents += page['Contents']\n",
-    "\n",
-    "all_contents[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "wanted-organization",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:03.569770Z",
-     "start_time": "2022-02-02T21:29:03.553226Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e162/USGS_1_n06e162.tif',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
-       "  'ETag': '\"90d8ba82df12261245aa09cbbaf22589\"',\n",
-       "  'Size': 1542700,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n06e163/USGS_1_n06e163.tif',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 41, tzinfo=tzutc()),\n",
-       "  'ETag': '\"f2799568910a5217bafdcc6f122d5729\"',\n",
-       "  'Size': 1253435,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/TIFF/current/n07e134/USGS_1_n07e134.tif',\n",
-       "  'LastModified': datetime.datetime(2021, 11, 17, 2, 16, 35, tzinfo=tzutc()),\n",
-       "  'ETag': '\"1fbc41c777e38a20b20f5a222439dca2\"',\n",
-       "  'Size': 1125374,\n",
-       "  'StorageClass': 'STANDARD'}]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tif_obs = list(filter(lambda ob: '.tif' in ob['Key'], all_contents))\n",
-    "tif_obs[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "specialized-workshop",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:03.575664Z",
-     "start_time": "2022-02-02T21:29:03.571489Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_bbox(tile_id):\n",
-    "    lat_sign = tile_id[0]\n",
-    "    lat_num = int(tile_id[1:3])\n",
-    "    lon_sign = tile_id[3]\n",
-    "    lon_num = int(tile_id[4:7])\n",
-    "    \n",
-    "    assert((lat_sign in ['n', 's']) and (lon_sign in ['e', 'w']))\n",
-    "    lon = lon_num if lon_sign == 'e' else -lon_num\n",
-    "    lat = lat_num if lat_sign == 'n' else -lat_num\n",
-    "    \n",
-    "    return box(lon, lat - 1, lon + 1, lat)\n",
-    "\n",
-    "def format_data(data):\n",
-    "    out_data = {}\n",
-    "    key = data['Key']\n",
-    "    out_data['tile_id'] = key.split('/')[-2]\n",
-    "    out_data['url'] = f'https://prd-tnm.s3.amazonaws.com/{key}'\n",
-    "    out_data['last_modified'] = data['LastModified']\n",
-    "    out_data['geometry'] = get_bbox(out_data['tile_id'])\n",
-    "    return out_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "compound-discipline",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:03.854848Z",
-     "start_time": "2022-02-02T21:29:03.577160Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tile_id</th>\n",
-       "      <th>url</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3811</th>\n",
-       "      <td>n06e162</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((163.00000 5.00000, 163.00000 6.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3812</th>\n",
-       "      <td>n06e163</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((164.00000 5.00000, 164.00000 6.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3813</th>\n",
-       "      <td>n07e134</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((135.00000 6.00000, 135.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3814</th>\n",
-       "      <td>n07e151</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((152.00000 6.00000, 152.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3815</th>\n",
-       "      <td>n07e152</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((153.00000 6.00000, 153.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      tile_id                                                url  \\\n",
-       "3811  n06e162  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "3812  n06e163  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "3813  n07e134  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "3814  n07e151  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "3815  n07e152  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "\n",
-       "                                               geometry  \n",
-       "3811  POLYGON ((163.00000 5.00000, 163.00000 6.00000...  \n",
-       "3812  POLYGON ((164.00000 5.00000, 164.00000 6.00000...  \n",
-       "3813  POLYGON ((135.00000 6.00000, 135.00000 7.00000...  \n",
-       "3814  POLYGON ((152.00000 6.00000, 152.00000 7.00000...  \n",
-       "3815  POLYGON ((153.00000 6.00000, 153.00000 7.00000...  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tile_data = list(map(format_data, tif_obs))\n",
-    "\n",
-    "df_3dep = pd.DataFrame(tile_data)\n",
-    "df_3dep = df_3dep.sort_values(by=['tile_id', 'last_modified'], ascending=True)\n",
-    "df_3dep.drop_duplicates(subset='tile_id', keep='last', inplace=True)\n",
-    "df_3dep = gpd.GeoDataFrame(df_3dep, geometry=df_3dep.geometry, crs=CRS.from_epsg(4326))\n",
-    "df_3dep = df_3dep[['tile_id', 'url', 'geometry']].copy()\n",
-    "df_3dep.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "southern-feeling",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:03.859955Z",
-     "start_time": "2022-02-02T21:29:03.856435Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/1/TIFF/historical/n06e162/USGS_1_n06e162_20130911.tif'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_3dep.url.tolist()[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "quick-basin",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:04.142852Z",
-     "start_time": "2022-02-02T21:29:03.861434Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/3dep.geojson.zip')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#df_3dep.to_file('3dep.geojson', driver='GeoJSON')\n",
-    "to_geojson_gzip(df_3dep, DATA_PATH/'3dep.geojson.zip')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "contemporary-antique",
-   "metadata": {},
-   "source": [
-    "# Ned1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "existing-possible",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:07.310341Z",
-     "start_time": "2022-02-02T21:29:04.144424Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'Key': 'StagedProducts/Elevation/1/IMG/Thumbs (2).db',\n",
-       "  'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 53, tzinfo=tzutc()),\n",
-       "  'ETag': '\"35a3ffdeaa2e01bc650852ba3b148076\"',\n",
-       "  'Size': 10240,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/Thumbs.db',\n",
-       "  'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 54, tzinfo=tzutc()),\n",
-       "  'ETag': '\"94e7b4e49dd2eaba551bb7f9c3f5fb66\"',\n",
-       "  'Size': 15872,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG.zip',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
-       "  'ETag': '\"afec7651ba529aa7946beaa4bdd55585\"',\n",
-       "  'Size': 325350,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG_thumb.jpg',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 29, tzinfo=tzutc()),\n",
-       "  'ETag': '\"1a5a97eb77835437042e550f70014f63\"',\n",
-       "  'Size': 870,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w067_IMG.zip',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
-       "  'ETag': '\"fb0273eabf422c0488571586c50f243a\"',\n",
-       "  'Size': 2472963,\n",
-       "  'StorageClass': 'STANDARD'}]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s3client = boto3.client('s3')\n",
-    "BUCKET_NAME = 'prd-tnm'\n",
-    "objects = s3client.list_objects(Bucket=BUCKET_NAME,\n",
-    "                                Prefix='StagedProducts/Elevation/1/IMG/')\n",
-    "objects['Contents'][:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "monthly-interaction",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:07.314750Z",
-     "start_time": "2022-02-02T21:29:07.312020Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "paginator = s3client.get_paginator('list_objects_v2')\n",
-    "pages_ned1 = paginator.paginate(Bucket=BUCKET_NAME, \n",
-    "                                Prefix='StagedProducts/Elevation/1/IMG/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "rapid-federation",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:09.648598Z",
-     "start_time": "2022-02-02T21:29:07.316455Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "9it [00:02,  3.87it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'Key': 'StagedProducts/Elevation/1/IMG/Thumbs (2).db',\n",
-       " 'LastModified': datetime.datetime(2019, 8, 21, 20, 3, 53, tzinfo=tzutc()),\n",
-       " 'ETag': '\"35a3ffdeaa2e01bc650852ba3b148076\"',\n",
-       " 'Size': 10240,\n",
-       " 'StorageClass': 'STANDARD'}"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_contents_ned1 = []\n",
-    "for page in tqdm(pages_ned1):\n",
-    "    all_contents_ned1 += page['Contents']\n",
-    "\n",
-    "all_contents_ned1[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "fabulous-sapphire",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:09.659311Z",
-     "start_time": "2022-02-02T21:29:09.653740Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w066_IMG.zip',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
-       "  'ETag': '\"afec7651ba529aa7946beaa4bdd55585\"',\n",
-       "  'Size': 325350,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w067_IMG.zip',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 23, tzinfo=tzutc()),\n",
-       "  'ETag': '\"fb0273eabf422c0488571586c50f243a\"',\n",
-       "  'Size': 2472963,\n",
-       "  'StorageClass': 'STANDARD'},\n",
-       " {'Key': 'StagedProducts/Elevation/1/IMG/USGS_NED_1_n18w068_IMG.zip',\n",
-       "  'LastModified': datetime.datetime(2018, 10, 27, 1, 32, 24, tzinfo=tzutc()),\n",
-       "  'ETag': '\"2be372ac1356440c568f671f4ebd5271\"',\n",
-       "  'Size': 669595,\n",
-       "  'StorageClass': 'STANDARD'}]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zip_ob = list(filter(lambda ob: '.zip' in ob['Key'], all_contents_ned1))\n",
-    "zip_ob[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "theoretical-sacrifice",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:09.665788Z",
-     "start_time": "2022-02-02T21:29:09.660984Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def get_bbox(tile_id):\n",
-    "    lat_sign = tile_id[0]\n",
-    "    lat_num = int(tile_id[1:3])\n",
-    "    lon_sign = tile_id[3]\n",
-    "    lon_num = int(tile_id[4:7])\n",
-    "    \n",
-    "    assert((lat_sign in ['n', 's']) and (lon_sign in ['e', 'w']))\n",
-    "    lon = lon_num if lon_sign == 'e' else -lon_num\n",
-    "    lat = lat_num if lat_sign == 'n' else -lat_num\n",
-    "    \n",
-    "    return box(lon, lat - 1, lon + 1, lat)\n",
-    "\n",
-    "def format_data(data):\n",
-    "    out_data = {}\n",
-    "    key = data['Key']\n",
-    "    tile_id = key.split('/')[-1]\n",
-    "    tile_id_tokens = tile_id.split('_')\n",
-    "    tile_id_tokens = list(filter(lambda x: x[0] in ['n', 's'], tile_id_tokens))\n",
-    "    assert(len(tile_id_tokens) == 1)\n",
-    "    out_data['tile_id'] = tile_id_tokens[0].replace('.zip', '')\n",
-    "    out_data['url'] = f'https://prd-tnm.s3.amazonaws.com/{key}'\n",
-    "    out_data['last_modified'] = data['LastModified']\n",
-    "    out_data['geometry'] = get_bbox(out_data['tile_id'])\n",
-    "    return out_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "applicable-steering",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:09.736776Z",
-     "start_time": "2022-02-02T21:29:09.667330Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tile_id</th>\n",
-       "      <th>url</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1017</th>\n",
-       "      <td>n06e162</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((163.00000 5.00000, 163.00000 6.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1018</th>\n",
-       "      <td>n06e163</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((164.00000 5.00000, 164.00000 6.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1019</th>\n",
-       "      <td>n07e134</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((135.00000 6.00000, 135.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1020</th>\n",
-       "      <td>n07e151</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((152.00000 6.00000, 152.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1021</th>\n",
-       "      <td>n07e152</td>\n",
-       "      <td>https://prd-tnm.s3.amazonaws.com/StagedProduct...</td>\n",
-       "      <td>POLYGON ((153.00000 6.00000, 153.00000 7.00000...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      tile_id                                                url  \\\n",
-       "1017  n06e162  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "1018  n06e163  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "1019  n07e134  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "1020  n07e151  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "1021  n07e152  https://prd-tnm.s3.amazonaws.com/StagedProduct...   \n",
-       "\n",
-       "                                               geometry  \n",
-       "1017  POLYGON ((163.00000 5.00000, 163.00000 6.00000...  \n",
-       "1018  POLYGON ((164.00000 5.00000, 164.00000 6.00000...  \n",
-       "1019  POLYGON ((135.00000 6.00000, 135.00000 7.00000...  \n",
-       "1020  POLYGON ((152.00000 6.00000, 152.00000 7.00000...  \n",
-       "1021  POLYGON ((153.00000 6.00000, 153.00000 7.00000...  "
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tile_data_ned1 = list(map(format_data, zip_ob))\n",
-    "\n",
-    "df_ned1 = pd.DataFrame(tile_data_ned1)\n",
-    "df_ned1 = df_ned1.sort_values(by=['tile_id', 'last_modified'], ascending=True)\n",
-    "df_ned1.drop_duplicates(subset='tile_id', keep='last', inplace=True)\n",
-    "df_ned1 = gpd.GeoDataFrame(df_ned1, geometry=df_ned1.geometry, crs=CRS.from_epsg(4326))\n",
-    "df_ned1 = df_ned1[['tile_id', 'url', 'geometry']].copy()\n",
-    "df_ned1.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "separate-textbook",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-02-02T21:29:10.010145Z",
-     "start_time": "2022-02-02T21:29:09.738259Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('data/ned1.geojson.zip')"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#df_ned1.to_file('ned1.geojson', driver='GeoJSON')\n",
-    "to_geojson_gzip(df_ned1, DATA_PATH/'ned1.geojson.zip')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "dem-stitcher",
-   "language": "python",
-   "name": "dem-stitcher"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}


Test patch:
diff --git a/tests/data/missing/glo_30_left.tif b/tests/data/missing/glo_30_left.tif
new file mode 100644
index 0000000..ab49cde
Binary files /dev/null and b/tests/data/missing/glo_30_left.tif differ
diff --git a/tests/data/missing/glo_90_right.tif b/tests/data/missing/glo_90_right.tif
new file mode 100644
index 0000000..662ee38
Binary files /dev/null and b/tests/data/missing/glo_90_right.tif differ
diff --git a/tests/data/missing/glo_merged.tif b/tests/data/missing/glo_merged.tif
new file mode 100644
index 0000000..d0d9a03
Binary files /dev/null and b/tests/data/missing/glo_merged.tif differ
diff --git a/tests/test_exceptions.py b/tests/test_exceptions.py
new file mode 100644
index 0000000..cdc0630
--- /dev/null
+++ b/tests/test_exceptions.py
@@ -0,0 +1,28 @@
+import pytest
+
+from dem_stitcher import stitch_dem
+from dem_stitcher.exceptions import DEMNotSupported, NoDEMCoverage
+
+
+def test_dem_not_supported():
+    with pytest.raises(DEMNotSupported):
+        bounds = [-120, 34, -119, 35]
+
+        X, p = stitch_dem(bounds,
+                          dem_name='dem-not-supported')
+
+
+def test_no_coverage():
+    with pytest.raises(NoDEMCoverage):
+        # Middle of the Atlantic
+        bounds = [-46, 22, -45, 23]
+        X, p = stitch_dem(bounds,
+                          dem_name='glo_30')
+
+
+def test_bad_extents():
+    with pytest.raises(ValueError):
+        bounds = [-119, 34, -120, 35]
+
+        X, p = stitch_dem(bounds,
+                          dem_name='glo_30')
diff --git a/tests/test_missing.py b/tests/test_missing.py
new file mode 100644
index 0000000..2e93987
--- /dev/null
+++ b/tests/test_missing.py
@@ -0,0 +1,72 @@
+import pytest
+import rasterio
+from numpy.testing import assert_almost_equal
+
+from dem_stitcher import stitch_dem
+from dem_stitcher.glo_30_missing import merge_glo_30_and_90_dems
+from dem_stitcher.stitcher import intersects_missing_glo_30_tiles
+
+extents = [
+           # On boundary of missing tiles (both glo-90, glo-30)
+           [42.95, 40.45, 43.05, 40.55],
+           # Contained within missing tiles
+           [43.95, 40.45, 44.05, 40.55],
+           # Outside
+           [41.95, 40.45, 42.05, 40.55],
+           ]
+
+containment = [True, True, False]
+
+
+def _open_one(path):
+    with rasterio.open(path) as ds:
+        X = ds.read(1)
+        p = ds.profile
+    return X, p
+
+
+@pytest.mark.parametrize('extent, containment', zip(extents, containment))
+def test_intersects_missing_tiles(extent, containment):
+    assert(intersects_missing_glo_30_tiles(extent) == containment)
+
+
+def test_merge_glo30_and_glo_90(test_data_dir):
+    data_dir = test_data_dir / 'missing'
+    glo_30_left_path = data_dir / 'glo_30_left.tif'
+    glo_90_right_path = data_dir / 'glo_90_right.tif'
+    glo_merged_path = data_dir / 'glo_merged.tif'
+
+    X_glo_30, p_glo_30 = _open_one(glo_30_left_path)
+    X_glo_90, p_glo_90 = _open_one(glo_90_right_path)
+    X_merged, p_merged = _open_one(glo_merged_path)
+
+    X_merged_out, p_merged_out = merge_glo_30_and_90_dems(X_glo_30, p_glo_30,
+                                                          X_glo_90, p_glo_90)
+
+    assert_almost_equal(X_merged, X_merged_out, decimal=6)
+    assert(p_merged_out['transform'] == p_merged['transform'])
+
+
+@pytest.mark.integration
+def test_glo_90_filling(test_data_dir):
+
+    data_dir = test_data_dir / 'missing'
+    glo_merged_path = data_dir / 'glo_merged.tif'
+
+    # Parameters for stitching
+    dst_area_or_point = 'Point'
+    dst_ellipsoidal_height = False
+    dem_name = 'glo_30'
+
+    bounds = [42.95, 40.45, 43.05, 40.55]
+
+    X_filled, p_filled = stitch_dem(bounds,
+                                    dem_name=dem_name,
+                                    dst_ellipsoidal_height=dst_ellipsoidal_height,
+                                    dst_area_or_point=dst_area_or_point,
+                                    fill_in_glo_30=True)
+
+    X_merged, p_merged = _open_one(glo_merged_path)
+
+    assert_almost_equal(X_merged, X_filled, decimal=6)
+    assert(p_filled['transform'] == p_merged['transform'])
diff --git a/tests/test_stitcher.py b/tests/test_stitcher.py
index 2e8dd4b..e612161 100644
--- a/tests/test_stitcher.py
+++ b/tests/test_stitcher.py
@@ -180,8 +180,12 @@ def test_no_change_when_no_transformations_to_tile(get_los_angeles_tile_dataset,
 @pytest.mark.integration
 @pytest.mark.parametrize("dem_name", DATASETS)
 def test_download_dem(dem_name):
-    # Within the Los Angeles tile
-    bounds = [-118.8, 34.6, -118.5, 34.8]
+    if dem_name == 'glo_90_missing':
+        # Missing area
+        bounds = [45.5, 39.5, 46.5, 40.5]
+    else:
+        # Within the Los Angeles tile
+        bounds = [-118.8, 34.6, -118.5, 34.8]
 
     dem_arr, _ = stitch_dem(bounds,
                             dem_name,