From 4d5eb307ba5874a6b8d94ccfbd758a63dd27b571 Mon Sep 17 00:00:00 2001
From: Scott Henderson <scottyhq@gmail.com>
Date: Thu, 31 Oct 2024 09:45:02 +0100
Subject: [PATCH] better wesm remote search

---
 .github/workflows/pixi.yml               |    2 +-
 README.md                                |    3 +-
 docs/index.md                            |    8 +
 docs/quickstart.ipynb                    |  626 +++++++
 pixi.lock                                | 2017 +++++++++++++---------
 pyproject.toml                           |   29 +-
 src/coincident/datasets/usgs.py          |    3 +-
 src/coincident/search/main.py            |    2 +-
 src/coincident/search/swath_polygons.csv |  611 +++++++
 src/coincident/search/wesm.py            |  132 +-
 tests/test_search.py                     |   17 +-
 11 files changed, 2567 insertions(+), 883 deletions(-)
 create mode 100644 docs/quickstart.ipynb
 create mode 100644 src/coincident/search/swath_polygons.csv

diff --git a/.github/workflows/pixi.yml b/.github/workflows/pixi.yml
index 79c9340..db93c29 100644
--- a/.github/workflows/pixi.yml
+++ b/.github/workflows/pixi.yml
@@ -19,7 +19,7 @@ jobs:
           environments: dev
           manifest-path: pyproject.toml
           cache: false
-          locked: true
+          locked: false
           #cache-write:
           #  ${{ github.event_name == 'push' && github.ref_name == 'main' }}
 
diff --git a/README.md b/README.md
index c3fc9f3..52a843f 100644
--- a/README.md
+++ b/README.md
@@ -43,8 +43,9 @@ git checkout -b newfeature
 pixi shell --environment dev # type `exit` to deactivate
 pre-commit install
 
-# Or run pre-configured environments and commands
+# Or run pre-configured commands:
 pixi run test
+pixi run precommit # (also runs automatically upon commit)
 pixi run lint
 pixi run docs
 ```
diff --git a/docs/index.md b/docs/index.md
index 96e12a1..e5cecd0 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -13,6 +13,14 @@ introduction
 datasets
 ```
 
+```{toctree}
+:maxdepth: 2
+:hidden:
+:caption: Examples
+
+quickstart
+```
+
 ```{toctree}
 :maxdepth: 2
 :hidden:
diff --git a/docs/quickstart.ipynb b/docs/quickstart.ipynb
new file mode 100644
index 0000000..8ece8f5
--- /dev/null
+++ b/docs/quickstart.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Quickstart\n",
+    "\n",
+    "This library provides convenience functions to hone in on coincident datasets and visualize their coverage. \n",
+    "\n",
+    "The current recommended workflow is to:\n",
+    "1. start with a lidar dataset \n",
+    "1. reduce to region with coincident maxar stereo within an acceptable temporal range\n",
+    "1. optionally reduce further with additional datasets such as icesat-2 and gedi altimetry\n",
+    "\n",
+    "This notebook provides and example starting from USGS 3DEP LiDAR in Colorado, USA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import coincident\n",
+    "\n",
+    "print(coincident.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define an area of interest\n",
+    "\n",
+    "Searches often start with an spatial subset, in this notebook we know we are interested in datasets in Colorado. We recommend restricting searches to areas at the 'State' scale rather than Country or Global scales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aoi = gpd.read_file(\n",
+    "    \"https://raw.githubusercontent.com/unitedstates/districts/refs/heads/gh-pages/states/CO/shape.geojson\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aoi.explore(color=\"black\", style_kwds=dict(fill=False))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "this polygon for the state of Colorado is simple and has only 4 vertices in the corners, but it's good practice good to check the number of vertices you have before searching. The simpler your search polygons are the faster your searches will be!\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Original number of vertices:\", aoi.count_coordinates().iloc[0])\n",
+    "aoi = aoi.simplify(0.01)\n",
+    "print(\"Simplified number of vertices:\", aoi.count_coordinates().iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uniform search method\n",
+    "\n",
+    "the `coincident` package provides a [search()](#coincident.search.search) method that has the same syntax regardless of which dataset you are searching. Behind the scenes, polygons intersecting your area of interest are efficiently located and returned as a geodataframe. \n",
+    "\n",
+    "For 3DEP LiDAR, we start by searching bounding boxes for each 'workunit'. Once we identify a workunit, we load a precise polygon delineating the extent of LiDAR measurements.\n",
+    "\n",
+    "The search function is essentially a [pystac_client.ItemSearch](#pystac_client.ItemSearch) with an extra argument `dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coincident.datasets.aliases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf = coincident.search.search(\n",
+    "    dataset=\"3dep\",\n",
+    "    intersects=aoi,\n",
+    "    datetime=[\"2018\", \"2024\"],\n",
+    ")\n",
+    "gf.explore(column=\"workunit\", popup=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# From this search we identify a specific lidar acquisition of interest\n",
+    "gf = gf[gf.workunit == \"CO_WestCentral_2019\"]\n",
+    "gf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each Workunit has a unique 'Feature ID', or 'FID' that can be used to efficiently retrieve the full-resultion detailed MultiPolygon footprint from the USGS's WESM.gpkg file in AWS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf.index.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### USGS 3DEP Lidar\n",
+    "\n",
+    "Some datasets have additional functions available to load auxiliary data. For example, we can load original high resolution polygon. In addition to loading \"swath\" polygons to understand exact days when acquisitions were made.\n",
+    "\n",
+    "```{warning}\n",
+    "Swath polygons are not available for all LiDAR workunits. They are more likely to exist for acquisitions after 2019.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf_wesm = coincident.search.wesm.load_by_fid(fids=gf.index.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf_wesm.explore(column=\"workunit\", popup=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf_wesm[[\"workunit\", \"start_datetime\", \"end_datetime\", \"duration\"]]  # duration in days"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Within this 'workunit' data was collected over 29 days, in order to see when data was collected within this polygon we need to load a corresponding 'swath' polygon. `coincident` provides a helper function for this, which loads the swath polygon for a given workunit\n",
+    "\n",
+    "```{note}\n",
+    "Swath polygons have detailed timing information for individual LiDAR flight lines composing a given 'workunit'. Not all workunits have swath polygons. They tend to be available for data collected after 2019.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: be patient here, reading shapefiles from s3 can be slow depending on your bandwidth\n",
+    "gf_swath = coincident.search.wesm.get_swath_polygons(gf.workunit.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Number of swath polygons:\", len(gf_swath))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot them, simplify first for faster plotting\n",
+    "gf_swath[\"geometry\"] = gf_swath.simplify(0.01)\n",
+    "gf_swath.explore(column=\"dayofyear\", cmap=\"plasma\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see in the above figure, the actual day of observation for any point on the ground can vary in complex ways based on the flight paths taken during the LiDAR collection period."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maxar stereo search\n",
+    "\n",
+    "Now that we understand our lidar collection, let's return to the uniform search function, and now search for Maxar stereo pairs. We are only interested in pairs in the same date range of the lidar. Note that searching by a simplified polygon (in this case back to the convex hull we started with) is recommended as most APIs have limits on the number of polygon vertices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Simplify search polygon and add date range\n",
+    "pad = gpd.pd.Timedelta(days=14)\n",
+    "date_range = [gf.start_datetime.iloc[0] - pad, gf.end_datetime.iloc[0] + pad]\n",
+    "aoi = gf.geometry\n",
+    "\n",
+    "gf_maxar = coincident.search.search(\n",
+    "    dataset=\"maxar\",\n",
+    "    intersects=aoi,\n",
+    "    datetime=date_range,\n",
+    "    filter=\"eo:cloud_cover < 20\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len(gf_maxar))\n",
+    "gf_maxar.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with gpd.pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
+    "    print(gf_maxar.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Group by stereo pair id (NOTE: assumes single id per mono acquisition)\n",
+    "gf_maxar[\"stereo_pair_id\"] = gf_maxar.stereo_pair_identifiers.str[0]\n",
+    "\n",
+    "gf_stereo = gf_maxar.dissolve(by=\"stereo_pair_id\", as_index=False)\n",
+    "gf_stereo.explore(column=\"stereo_pair_identifiers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = gf_wesm.explore(color=\"black\")\n",
+    "gf_maxar.explore(column=\"dayofyear\", cmap=\"plasma\", m=m, popup=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Restrict polygon footprints to area of overlap\n",
+    "\n",
+    "We will make regular use of the GeoPandas overlay() function to shrink original acquisition footprints so that they only overlap an area of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consider area of overlap\n",
+    "m = gf_wesm.explore(color=\"black\")\n",
+    "# NOTE: don't really need wesm attributes in result, so just use geodataframe w/ geometry column\n",
+    "# gf_i = gf_wesm[['geometry']].overlay(gf_stereo, how='intersection')\n",
+    "gf_i = gf_stereo.overlay(gf_wesm[[\"geometry\"]], how=\"intersection\")\n",
+    "gf_i.explore(column=\"dayofyear\", cmap=\"plasma\", m=m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exclude very small overlaps\n",
+    "\n",
+    "Consider further filtering results by a minimum overlap area criteria, or by a more precise estimate of the number of days elapsed for the maxar stereo acquisition relative to lidar estimated from swath polygons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_area_km2 = 20\n",
+    "gf_i = coincident.overlaps.subset_by_minimum_area(gf_i, min_area_km2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Look at intersection with swath polygons for exact date difference\n",
+    "# Since both have 'dayofyear', these cols get expanded to dayofyear_1 and dayofyear_2\n",
+    "stereo_pair = gf_i.iloc[[0]]\n",
+    "gf_dt = gf_swath.overlay(stereo_pair, how=\"intersection\")\n",
+    "print(\"Maxar Stereo Acquisition DOY - Swath Lidar DOY:\")\n",
+    "print(stereo_pair.stereo_pair_id.values[0])\n",
+    "(gf_dt.dayofyear_2 - gf_dt.dayofyear_1).describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GEDI\n",
+    "\n",
+    "Search for Coincident GEDI L2A."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We've refined our search polygon again, so use a new AOI\n",
+    "aoi = gpd.GeoSeries(gf_i.union_all().convex_hull, crs=\"EPSG:4326\")\n",
+    "\n",
+    "gf_gedi = coincident.search.search(\n",
+    "    dataset=\"gedi\",\n",
+    "    intersects=aoi,\n",
+    "    datetime=date_range,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len(gf_gedi))\n",
+    "gf_gedi.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf_gedi.explore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altimeter granules span a large geographic area! So let's again cut results down to the area of intersection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize colormap across both dataframes\n",
+    "vmin, vmax = gpd.pd.concat([gf_i.dayofyear, gf_gedi.dayofyear]).agg([\"min\", \"max\"])\n",
+    "cmap = \"plasma\"\n",
+    "\n",
+    "# NOTE: here we just take the boundary of the union of all maxar+lidar regions to avoid many overlay geometries\n",
+    "union = gpd.GeoDataFrame(geometry=[gf_i.union_all()], crs=\"EPSG:4326\")\n",
+    "m = gf_i.explore(column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax)\n",
+    "gf_gedi = union.overlay(gf_gedi, how=\"intersection\")\n",
+    "gf_gedi.explore(m=m, column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, legend=False)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Immediately we see some potential for close-in time acquisitions. \n",
+    "\n",
+    "```{note}\n",
+    "As an additional step, you might want to add another filtering step to remove tiny polygons\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For each Stereo pair, describe number of altimeter passes and day offset\n",
+    "stereo_pair = gf_i.iloc[[0]]\n",
+    "gf_dt = stereo_pair.overlay(gf_gedi, how=\"intersection\")\n",
+    "m = stereo_pair.explore(\n",
+    "    column=\"dayofyear\", vmin=vmin, vmax=vmax, cmap=cmap, legend=False\n",
+    ")\n",
+    "gf_dt.explore(m=m, column=\"dayofyear_2\", vmin=vmin, vmax=vmax, cmap=cmap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Maxar Stereo Acquisition DOY - GEDI DOY:\")\n",
+    "print(stereo_pair.stereo_pair_identifiers.values[0])\n",
+    "(gf_dt.dayofyear_1 - gf_dt.dayofyear_2).describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Consider just the minimum offset for each stereo pair\n",
+    "# NOTE: probably a fancier way to do this using a multiindex/groupby\n",
+    "print(\"Maxar Stereo Acquisition DOY - GEDI DOY\")\n",
+    "for i in range(len(gf_i)):\n",
+    "    stereo_pair = gf_i.iloc[[i]]\n",
+    "    gf_dt = stereo_pair.overlay(gf_gedi, how=\"intersection\")\n",
+    "    min_dt = (gf_dt.dayofyear_1 - gf_dt.dayofyear_2).min()\n",
+    "    print(stereo_pair.stereo_pair_identifiers.values[0], min_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "In this case, we have some altimeter acquisitions that were just 3 days after a maxar stereo acquisition!\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ICESat-2\n",
+    "\n",
+    "Search for Coincident ICESat-2 Altimetry (ATL03)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gf_is2 = coincident.search.search(\n",
+    "    dataset=\"icesat-2\",\n",
+    "    intersects=aoi,\n",
+    "    datetime=date_range,\n",
+    ")\n",
+    "print(len(gf_is2))\n",
+    "gf_is2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize colormap across both dataframes\n",
+    "vmin, vmax = gpd.pd.concat([gf_i.dayofyear, gf_is2.dayofyear]).agg([\"min\", \"max\"])\n",
+    "cmap = \"plasma\"\n",
+    "\n",
+    "# NOTE: here we just take the boundary of the union of all maxar+lidar regions to avoid many overlay geometries\n",
+    "union = gpd.GeoDataFrame(geometry=[gf_i.union_all()], crs=\"EPSG:4326\")\n",
+    "m = gf_i.explore(\n",
+    "    column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, style_kwds=dict(color=\"black\")\n",
+    ")\n",
+    "gf_is2 = union.overlay(gf_is2, how=\"intersection\")\n",
+    "gf_is2.explore(m=m, column=\"dayofyear\", cmap=cmap, vmin=vmin, vmax=vmax, legend=False)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For each Stereo pair, describe number of altimeter passes and day offset\n",
+    "stereo_pair = gf_i.iloc[[0]]\n",
+    "gf_dt = stereo_pair.overlay(gf_is2, how=\"intersection\")\n",
+    "m = stereo_pair.explore(\n",
+    "    column=\"dayofyear\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    "    cmap=cmap,\n",
+    "    legend=False,\n",
+    "    style_kwds=dict(color=\"black\"),\n",
+    ")\n",
+    "gf_dt.explore(m=m, column=\"dayofyear_2\", vmin=vmin, vmax=vmax, cmap=cmap)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Maxar Stereo Acquisition DOY - ICESat-2 DOY\")\n",
+    "for i in range(len(gf_i)):\n",
+    "    stereo_pair = gf_i.iloc[[i]]\n",
+    "    gf_dt = stereo_pair.overlay(gf_is2, how=\"intersection\")\n",
+    "    min_dt = (gf_dt.dayofyear_1 - gf_dt.dayofyear_2).min()\n",
+    "    print(stereo_pair.stereo_pair_identifiers.values[0], min_dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "- Any of these Maxar stereo pairs could be worth ordering. But if the goal is to have coincident acquisitions as close as possible in time '11a11283-6661-4f91-9bcb-5dab3c6a5d02-inv' seems like a good bet:\n",
+    "    - There is an overlapping ICESat-2 track 2 days later\n",
+    "    - There is an overlapping GEDI track 3 days earlier\n",
+    "    - 3DEP Lidar was acquired 27 to 36 days earlier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can save this dataframe, or just note STAC ids to work with later ('102001008EC5AC00','102001008BE9BB00')\n",
+    "pair_id = \"11a11283-6661-4f91-9bcb-5dab3c6a5d02-inv\"\n",
+    "full_frame = gf_maxar[gf_maxar.stereo_pair_id == pair_id]\n",
+    "subset = gf_i[gf_i.stereo_pair_id == pair_id]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save the original STAC metadata and polygon\n",
+    "full_frame.to_parquet(\"/tmp/maxar-mono-images.parquet\")\n",
+    "# Also save the subsetted region for this stereo pair\n",
+    "subset.to_parquet(\"/tmp/stereo-subset.parquet\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pixi.lock b/pixi.lock
index eec2dec..fcae374 100644
--- a/pixi.lock
+++ b/pixi.lock
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-  url: https://files.pythonhosted.org/packages/b8/ad/fc82be4eaceb8d444cb6fc1956ce972b3a0795104279de05e0e4131d0a47/rpds_py-0.20.0-cp312-cp312-macosx_11_0_arm64.whl
-  sha256: 56e27147a5a4c2c21633ff8475d185734c0e4befd1c989b5b95a5d0db699b21b
-  requires_python: '>=3.8'
+  build: py312he431725_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.20.0-py312he431725_1.conda
+  sha256: 2d9a484f01c15644d2ae22d7ccc3f77697f0b0cfb35f3caa03ae36dda9e7ad9d
+  md5: 50ee5ea6188046514d7ad107c6bbd4ef
+  depends:
+  - __osx >=11.0
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/rpds-py?source=hash-mapping
+  size: 291984
+  timestamp: 1725327553881
 - kind: conda
   name: s2n
   version: 1.5.5
@@ -9161,26 +9556,27 @@ packages:
   purls: []
   size: 353081
   timestamp: 1728534228471
-- kind: conda
+- kind: pypi
   name: s3fs
   version: 2024.10.0
-  build: pyhd8ed1ab_0
-  subdir: noarch
-  noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/s3fs-2024.10.0-pyhd8ed1ab_0.conda
-  sha256: b5a8e8fb68087166abef44c185dfb8c6ad1e4b54c56e82c03a3e3b306e0ca757
-  md5: 18cc1c4f5b104400d75be999b56486a1
-  depends:
-  - aiobotocore >=2.5.4,<3.0.0
-  - aiohttp
-  - fsspec 2024.10.0
-  - python >=3.8
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/s3fs?source=hash-mapping
-  size: 32521
-  timestamp: 1729650409739
+  url: https://files.pythonhosted.org/packages/99/44/bb9ff095ae7b1b6908480f683b6ca6b71c2105d343a5e5cb25334b01f5fa/s3fs-2024.10.0-py3-none-any.whl
+  sha256: 7a2025d60d5b1a6025726b3a5e292a8e5aa713abc3b16fd1f81735181f7bb282
+  requires_dist:
+  - aiobotocore<3.0.0,>=2.5.4
+  - fsspec==2024.10.0.*
+  - aiohttp!=4.0.0a0,!=4.0.0a1
+  - aiobotocore[awscli]<3.0.0,>=2.5.4 ; extra == 'awscli'
+  - aiobotocore[boto3]<3.0.0,>=2.5.4 ; extra == 'boto3'
+  requires_python: '>=3.8'
+- kind: pypi
+  name: s3transfer
+  version: 0.10.3
+  url: https://files.pythonhosted.org/packages/e5/c0/b0fba8259b61c938c9733da9346b9f93e00881a9db22aafdd72f6ae0ec05/s3transfer-0.10.3-py3-none-any.whl
+  sha256: 263ed587a5803c6c708d3ce44dc4dfedaab4c1a32e8329bab818933d79ddcf5d
+  requires_dist:
+  - botocore<2.0a0,>=1.33.2
+  - botocore[crt]<2.0a0,>=1.33.2 ; extra == 'crt'
+  requires_python: '>=3.8'
 - kind: conda
   name: scikit-learn
   version: 1.5.2
@@ -9426,12 +9822,24 @@ packages:
   - pkg:pypi/snuggs?source=hash-mapping
   size: 11131
   timestamp: 1722610712753
-- kind: pypi
+- kind: conda
   name: soupsieve
-  version: '2.6'
-  url: https://files.pythonhosted.org/packages/d1/c2/fe97d779f3ef3b15f05c94a2f1e3d21732574ed441687474db9d342a7315/soupsieve-2.6-py3-none-any.whl
-  sha256: e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9
-  requires_python: '>=3.8'
+  version: '2.5'
+  build: pyhd8ed1ab_1
+  build_number: 1
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda
+  sha256: 54ae221033db8fbcd4998ccb07f3c3828b4d77e73b0c72b18c1d6a507059059c
+  md5: 3f144b2c34f8cb5a9abd9ed23a39c561
+  depends:
+  - python >=3.8
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/soupsieve?source=hash-mapping
+  size: 36754
+  timestamp: 1693929424267
 - kind: pypi
   name: sphinx
   version: 8.1.3
@@ -9817,6 +10225,24 @@ packages:
   requires_dist:
   - python-dateutil
   requires_python: '>=3.9'
+- kind: conda
+  name: tinycss2
+  version: 1.4.0
+  build: pyhd8ed1ab_0
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda
+  sha256: cad582d6f978276522f84bd209a5ddac824742fe2d452af6acf900f8650a73a2
+  md5: f1acf5fdefa8300de697982bcb1761c9
+  depends:
+  - python >=3.5
+  - webencodings >=0.4
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/tinycss2?source=hash-mapping
+  size: 28285
+  timestamp: 1729802975370
 - kind: conda
   name: tk
   version: 8.6.13
@@ -10140,24 +10566,23 @@ packages:
   timestamp: 1726496531769
 - kind: conda
   name: virtualenv
-  version: 20.27.0
+  version: 20.27.1
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.0-pyhd8ed1ab_0.conda
-  sha256: 18bae5ff9f02793ca56d295f0a5f1d4443623ee3be09a6805eb7d4b18245968c
-  md5: a6ed1227ba6ec37cfc2b25e6512f729f
+  url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.27.1-pyhd8ed1ab_0.conda
+  sha256: 189b935224732267df10dc116bce0835bd76fcdb20c30f560591c92028d513b0
+  md5: dae21509d62aa7bf676279ced3edcb3f
   depends:
   - distlib <1,>=0.3.7
   - filelock <4,>=3.12.2
   - platformdirs <5,>=3.9.1
   - python >=3.8
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 2952166
-  timestamp: 1729243861344
+  size: 2965442
+  timestamp: 1730204927840
 - kind: conda
   name: wcwidth
   version: 0.2.13
@@ -10176,45 +10601,35 @@ packages:
   size: 32709
   timestamp: 1704731373922
 - kind: conda
-  name: wrapt
-  version: 1.16.0
-  build: py312h024a12e_1
-  build_number: 1
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/wrapt-1.16.0-py312h024a12e_1.conda
-  sha256: 54a5d3d9e1b45022b28c5ca3ceaa7ec2db4a40968b2b556804becfdff98f4efe
-  md5: f97c9abfeb8292f5f8353607ca8a1127
+  name: webencodings
+  version: 0.5.1
+  build: pyhd8ed1ab_2
+  build_number: 2
+  subdir: noarch
+  noarch: python
+  url: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_2.conda
+  sha256: 2adf9bd5482802837bc8814cbe28d7b2a4cbd2e2c52e381329eaa283b3ed1944
+  md5: daf5160ff9cde3a468556965329085b9
   depends:
-  - __osx >=11.0
-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
-  license: BSD-2-Clause
+  - python >=2.6
+  license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/wrapt?source=hash-mapping
-  size: 59642
-  timestamp: 1724958200454
-- kind: conda
+  - pkg:pypi/webencodings?source=hash-mapping
+  size: 15600
+  timestamp: 1694681458271
+- kind: pypi
   name: wrapt
   version: 1.16.0
-  build: py312h66e93f0_1
-  build_number: 1
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/wrapt-1.16.0-py312h66e93f0_1.conda
-  sha256: 3a15a399eb61a999f0f14b4d243acc14e2dff1ead92ef52fcff30c84be89b21c
-  md5: 2eebcffe80e2a7bb2f0a77e621a7f124
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - libgcc >=13
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  license: BSD-2-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/wrapt?source=hash-mapping
-  size: 62624
-  timestamp: 1724958046744
+  url: https://files.pythonhosted.org/packages/62/62/30ca2405de6a20448ee557ab2cd61ab9c5900be7cbd18a2639db595f0b98/wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 98b5e1f498a8ca1858a1cdbffb023bfd954da4e3fa2c0cb5853d40014557248b
+  requires_python: '>=3.6'
+- kind: pypi
+  name: wrapt
+  version: 1.16.0
+  url: https://files.pythonhosted.org/packages/6a/d7/cfcd73e8f4858079ac59d9db1ec5a1349bc486ae8e9ba55698cc1f4a1dff/wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 9090c9e676d5236a6948330e83cb89969f433b1943a558968f659ead07cb3b36
+  requires_python: '>=3.6'
 - kind: conda
   name: xarray
   version: 2024.10.0
@@ -10431,50 +10846,26 @@ packages:
   purls: []
   size: 89141
   timestamp: 1641346969816
-- kind: conda
+- kind: pypi
   name: yarl
-  version: 1.16.0
-  build: py312h0bf5046_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/yarl-1.16.0-py312h0bf5046_0.conda
-  sha256: 2485912fa1c1acf51501519cd4d0253234f7e5b243093985b26ce167fdd67407
-  md5: 81e954d5e6d3465d00f040b8ac1a8f67
-  depends:
-  - __osx >=11.0
-  - idna >=2.0
-  - multidict >=4.0
-  - propcache >=0.2.0
-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
-  license: Apache-2.0
-  license_family: Apache
-  purls:
-  - pkg:pypi/yarl?source=hash-mapping
-  size: 138281
-  timestamp: 1729798786022
-- kind: conda
+  version: 1.17.0
+  url: https://files.pythonhosted.org/packages/5a/f4/52be40fc0a8811a18a2b2ae99c6233e769fe391b52fae95a23a4db45e82c/yarl-1.17.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+  sha256: 8d522f390686acb6bab2b917dd9ca06740c5080cd2eaa5aef8827b97e967319d
+  requires_dist:
+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.0
+  requires_python: '>=3.9'
+- kind: pypi
   name: yarl
-  version: 1.16.0
-  build: py312h66e93f0_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/yarl-1.16.0-py312h66e93f0_0.conda
-  sha256: e9718b1f67f7359dee66995164ff734890066ad2eecb483b08f3f35b3f813c2d
-  md5: c3f4a6b56026c22319bf31514662b283
-  depends:
-  - __glibc >=2.17,<3.0.a0
-  - idna >=2.0
-  - libgcc >=13
-  - multidict >=4.0
-  - propcache >=0.2.0
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  license: Apache-2.0
-  license_family: Apache
-  purls:
-  - pkg:pypi/yarl?source=hash-mapping
-  size: 147820
-  timestamp: 1729798523861
+  version: 1.17.0
+  url: https://files.pythonhosted.org/packages/d1/b3/808461c3c3d4c32ff8783364a8673bd785ce887b7421e0ea8d758357d874/yarl-1.17.0-cp312-cp312-macosx_11_0_arm64.whl
+  sha256: 13468d291fe8c12162b7cf2cdb406fe85881c53c9e03053ecb8c5d3523822cd9
+  requires_dist:
+  - idna>=2.0
+  - multidict>=4.0
+  - propcache>=0.2.0
+  requires_python: '>=3.9'
 - kind: conda
   name: zeromq
   version: 4.3.5
diff --git a/pyproject.toml b/pyproject.toml
index 7149c30..ee27a21 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,18 +31,19 @@ classifiers = [
 dynamic = ["version"]
 
 dependencies = [
-  "aiohttp>=3.10.6,<4",
-  "fsspec>=2024.9.0,<2025",
+  #"aiohttp>=3.10.6,<4",
+  "cloudpathlib[s3]>=0.20.0,<0.21",
+  #"fsspec>=2024.9.0,<2025",
   "geopandas>=1.0.1,<2",
-  "maxar-platform==1.0.2a2",
+  "maxar-platform>=1.0.2,<2",
   "planetary-computer>=1.0.0,<2",
+  "pyarrow>=18.0.0,<19",
   "pystac-client>=0.8.3,<0.9",
-  "pyarrow>=17",
   "requests>=2.32.3,<3",
   "rioxarray>=0.17.0,<0.18",
   "stac-asset>=0.4.3,<0.5",
   "stac-geoparquet>=0.6.0,<0.7",
-  "s3fs>=2024.9.0,<2025",
+  #"s3fs>=2024.9.0,<2025",
 ]
 
 [project.optional-dependencies]
@@ -162,7 +163,11 @@ ignore = [
   "PLR09",    # Too many <...>
   "PLR2004",  # Magic value used in comparison
   "ISC001",   # Conflicts with formatter
-  "PLC0414"   # explicit re-export
+  "PLC0414",  # explicit re-export
+  "PD901",    # df for DataFrame
+]
+exclude = [
+    "docs/*.ipynb",
 ]
 isort.required-imports = ["from __future__ import annotations"]
 # Uncomment if using a _compat.typing backport
@@ -198,17 +203,20 @@ docs = { features = ["docs"], solve-group = "default" }
 # NOTE: pixi will match project.dependencies versions above but get them from conda-forge
 # If a package is listed in project.dependencies but not repeated here, it is installed from pypi
 [tool.pixi.dependencies]
-aiohttp = "*"
-fsspec = "*"
+python = "<3.13" # https://github.com/stac-utils/stac-geoparquet/issues/81
+#aiohttp = "*"
+#fsspec = "*"
 geopandas = "*"
 planetary-computer = "*"
-pyarrow = "*"
 pystac-client = "*"
 requests = "*"
 rioxarray = "*"
 # stac-asset = "*" # not on conda-forge
+#s3fs = "*"
 stac-geoparquet = "*"
-s3fs = "*"
+pyarrow = "*"
+#nbconvert = ">=7.16.4,<8"
+#cloudpathlib-s3 = ">=0.20.0,<0.21"
 #matplotlib-base = ">=3.9.2,<4"
 #sliderule = ">=4.7.1,<5"
 
@@ -226,6 +234,7 @@ mypy = "*"
 
 [tool.pixi.pypi-dependencies]
 coincident = { path = ".", editable = false }
+#cloudpathlib = { version = "0.20.0,<0.21", extras = ["s3"]}
 
 [tool.pixi.feature.dev.pypi-dependencies]
 coincident = { path = ".", editable = true }
diff --git a/src/coincident/datasets/usgs.py b/src/coincident/datasets/usgs.py
index 7be3e89..977b5a0 100644
--- a/src/coincident/datasets/usgs.py
+++ b/src/coincident/datasets/usgs.py
@@ -18,7 +18,8 @@ class ThreeDEP(Dataset):
 
     has_stac_api: bool = False
     search: str = (
-        "https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/WESM.gpkg"
+        # "https://prd-tnm.s3.amazonaws.com/StagedProducts/Elevation/metadata/WESM.gpkg"
+        "s3://prd-tnm/StagedProducts/Elevation/metadata/WESM.gpkg"
     )
     start: str = "2000-12-01"
     end: str | None = None
diff --git a/src/coincident/search/main.py b/src/coincident/search/main.py
index 8f06cac..76a0691 100644
--- a/src/coincident/search/main.py
+++ b/src/coincident/search/main.py
@@ -116,7 +116,7 @@ def search(
 
     # Non-STAC Searches
     elif dataset.alias == "3dep":
-        gf = wesm.search_convex_hulls(
+        gf = wesm.search_bboxes(
             intersects=intersects,
             search_start=search_start,
             search_end=search_end,
diff --git a/src/coincident/search/swath_polygons.csv b/src/coincident/search/swath_polygons.csv
new file mode 100644
index 0000000..5a7556f
--- /dev/null
+++ b/src/coincident/search/swath_polygons.csv
@@ -0,0 +1,611 @@
+workunit,swathpolygon_link
+VA_Shenandoah_2014,s3://prd-tnm/StagedProducts/Elevation/metadata/NRCS_Shenandoah_VA_LiDAR/VA_Shenandoah_2014/spatial_metadata/USGS/swath_pcs/cal_unclass_tiled_pcs.shp
+OR_HarneySilver_1_2020,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_HarneySilver_2020_A20/OR_HarneySilver_1_2020/spatial_metadata/USGS/USGS_OR_HarneySilver_1_2020_SwathPoly/USGS_OR_HarneySilver_1_2020_SwathPoly.shp
+OR_HarneySilver_2_2020,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_HarneySilver_2020_A20/OR_HarneySilver_2_2020/spatial_metadata/USGS/USGS_OR_HarneySilver_2_2020_SwathPoly/USGS_OR_HarneySilver_2_2020_SwathPoly.shp
+VT_Eastern_Vermont_3_2014,s3://prd-tnm/StagedProducts/Elevation/metadata/Eastern_Vermont_LiDAR_FY2014/VT_Eastern_Vermont_3_2014/spatial_metadata/USGS/USGS_VT_Eastern_Vermont_3_2014_SWATH_POLY.shp
+TN_WestTN_B1_2019,s3://prd-tnm/StagedProducts/Elevation/metadata/TN_WestTN_2019_B19/TN_WestTN_B1_2019/spatial_metadata/USGS/USGS_TN_WestTN_B1_2019_Swath_Poly.shp
+ND_3DEPProcessing_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/ND_3DEPProcessing_D22/ND_3DEPProcessing_2_D22/spatial_metadata/USGS/USGS_ND_3DEPProcessing_2_D22_Swath_Poly/USGS_ND_3DEPProcessing_2_D22_Swath_Poly.shp
+ND_3DEPProcessing_4_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/ND_3DEPProcessing_D22/ND_3DEPProcessing_4_D22/spatial_metadata/USGS/USGS_ND_3DEPProcessing_4_D22_Swath_Poly.shp
+IN_Statewide_Opt1_B2_2017,s3://prd-tnm/StagedProducts/Elevation/metadata/IN_Indiana_Statewide_LiDAR_2017_B17/IN_Statewide_Opt1_B2_2017/spatial_metadata/USGS/USGS_IN_Statewide_Opt1_B2_2017_SwathPoly.shp
+IN_Statewide_B2_2017,s3://prd-tnm/StagedProducts/Elevation/metadata/IN_Indiana_Statewide_LiDAR_2017_B17/IN_Statewide_B2_2017/spatial_metadata/USGS/USGS_IN_Statewide_B2_2017_SwathPoly.shp
+IN_Statewide_Opt2_B3_2017,s3://prd-tnm/StagedProducts/Elevation/metadata/IN_Indiana_Statewide_LiDAR_2017_B17/IN_Statewide_Opt2_B3_2017/spatial_metadata/USGS/USGS_IN_Statewide_Opt2_B3_2017_SwathPoly.shp
+IN_Statewide_Opt1_B3_2017,s3://prd-tnm/StagedProducts/Elevation/metadata/IN_Indiana_Statewide_LiDAR_2017_B17/IN_Statewide_Opt1_B3_2017/spatial_metadata/USGS/USGS_IN_Statewide_Opt1_B3_2017_SwathPoly.shp
+NJ_NW6Co_2017,s3://prd-tnm/StagedProducts/Elevation/metadata/NJ_NW_New_Jersey_6_County_Lidar_2017_B17/NJ_NW6Co_2017/spatial_metadata/USGS/USGS_NJ_NW6Co_2017_SWATH_POLY.shp
+OR_HarneySilver_3_2020,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_HarneySilver_2020_A20/OR_HarneySilver_3_2020/spatial_metadata/USGS/USGS_OR_HarneySilver_3_2020_SwathPoly/USGS_OR_HarneySilver_3_2020_SwathPoly.shp
+KS_Statewide_B12_2018,s3://prd-tnm/StagedProducts/Elevation/metadata/KS_Statewide_2018_A18/KS_Statewide_B12_2018/spatial_metadata/USGS/USGS_KS_Statewide_B12_2018_SWATH_POLY.shp
+KS_Statewide_B17_2018,s3://prd-tnm/StagedProducts/Elevation/metadata/KS_Statewide_2018_A18/KS_Statewide_B17_2018/spatial_metadata/USGS/USGS_KS_B17_Swath_Poly/KS_Stwide_B17_Swath_Poly_Blk1A.shp
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+WI_12County_12_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/WI_12County_B22/WI_12County_12_B22/spatial_metadata/USGS/USGS_WI_12County_12_B22_Swath_Poly.shp
+MN_BeckerCo_1_2021,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_BeckerCounty_2021_D21/MN_BeckerCo_1_2021/spatial_metadata/USGS/USGS_MN_BeckerCo_1_2021_Swath_Poly.shp
+NV_EastCentral_4_D21,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_EastCentral_2021_D21/NV_EastCentral_4_D21/spatial_metadata/USGS/USGS_NV_EastCentral_4_D21_SwathPoly/USGS_NV_EastCentral_4_D21_SwathPoly.shp
+NV_EastCentral_3_D21,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_EastCentral_2021_D21/NV_EastCentral_3_D21/spatial_metadata/USGS/USGS_NV_EastCentral_3_D21_Swath_Poly.shp
+MN_UpperMissRiver_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_UpperMissRiver_B22/MN_UpperMissRiver_1_B22/spatial_metadata/USGS/USGS_MN_UpperMSRiver_1_B22_SwathPoly.shp
+MN_UpperMissRiver_3_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_UpperMissRiver_B22/MN_UpperMissRiver_3_B22/spatial_metadata/USGS/USGS_MN_UpperMissRiver_3_B22_SWATH_POLY.shp
+MN_CentralMissRiver_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_CentralMissRiver_B22/MN_CentralMissRiver_2_B22/spatial_metadata/USGS/USGS_MN_CentralMissRiver_2_B22_SwathPoly.shp
+MN_UpperMissRiver_6_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_UpperMissRiver_B22/MN_UpperMissRiver_6_B22/spatial_metadata/USGS/USGS_MN_UpperMissRiver_6_B22_Swath_Poly.shp
+MN_UpperMissRiver_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_UpperMissRiver_B22/MN_UpperMissRiver_2_B22/spatial_metadata/USGS/USGS_MN_UpperMissRiver_2_B22_Swath_Poly.shp
+MN_CentralMissRiver_3_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_CentralMissRiver_B22/MN_CentralMissRiver_3_B22/spatial_metadata/USGS/USGS_MN_CentralMissRiver_3_B22_SwathPolygon.shp
+CA_SierraNevada_11_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/CA_SierraNevada_B22/CA_SierraNevada_11_B22/spatial_metadata/USGS/USGS_CA_SierraNevada_11_B22_SwathPolygon.shp
+UT_WestEast_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_2_B22/spatial_metadata/USGS/USGS_UT_WestEast_2_B22_SwathIndex.shp
+MT_StatewideP4_8_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_8_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_8_B22_SWATH_POLY.shp
+CA_SierraNevada_9_2022,s3://prd-tnm/StagedProducts/Elevation/metadata/CA_SierraNevada_B22/CA_SierraNevada_9_2022/spatial_metadata/USGS/USGS_CA_SierraNevada_9_2022_Swath_Poly.shp
+CA_SierraNevada_10_2022,s3://prd-tnm/StagedProducts/Elevation/metadata/CA_SierraNevada_B22/CA_SierraNevada_10_2022/spatial_metadata/USGS/USGS_CA_SierraNevada_10_2022_Swath_Polygon/USGS_CA_SierraNevada_10_2022_Swath_Polygon.shp
+UT_WestEast_7_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_7_B22/spatial_metadata/USGS/USGS_UT_WestEast_7_Swath_Poly/USGS_UT_WestEast_7_Swath_Poly.shp
+UT_WestEast_8_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_8_B22/spatial_metadata/USGS/USGS_UT_WestEast_8_B22_Swath_Poly.shp
+AZ_CenCoconino_3_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_CentralCoconino_B22/AZ_CenCoconino_3_B22/spatial_metadata/USGS/USGS_AZ_CenCoconino_3_B22_Swath_Poly/USGS_AZ_CenCoconino_3_B22_Swath_Poly.shp
+AZ_CenCoconino_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_CentralCoconino_B22/AZ_CenCoconino_1_B22/spatial_metadata/USGS/USGS_AZ_CenCoconino_1_B22_Swath_Polygons.shp
+NV_ClarkCo_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_ClarkCounty_B22/NV_ClarkCo_1_B22/spatial_metadata/USGS/USGS_NV_ClarkCo_1_B22_Swath_Poly.shp
+NV_ClarkCo_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_ClarkCounty_B22/NV_ClarkCo_2_B22/spatial_metadata/USGS/USGS_NV_ClarkCo_2_B22_Swath_Poly/NV_ClarkCo_2_B22_Swath_Poly.shp
+WA_NorthEast_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/WA_NorthEast_B22/WA_NorthEast_1_B22/spatial_metadata/USGS/USGS_WA_NorthEast_1_B22_Swath_Poly.shp
+MT_StatewideP4_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_2_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_2_B22_Swath_Poly.shp
+AZ_CenCoconino_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_CentralCoconino_B22/AZ_CenCoconino_2_B22/spatial_metadata/USGS/Swath_Polygon/USGS_AZ_CenCoconino_2_Swath_Poly.shp
+MT_StatewideP4_6_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_6_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_6_B22_Swath_Poly.shp
+MT_StatewideP4_5_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_5_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_5_B22_SWATH_POLY.shp
+OR_SWCentralSycan_4_B21,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_SouthwestCentralSycan_2021_B21/OR_SWCentralSycan_4_B21/spatial_metadata/USGS/USGS_OR_SWCentralSycan_4_B21_Swath_Poly.shp
+UT_WestEast_3_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_3_B22/spatial_metadata/USGS/USGS_UT_WestEast_3_B22_Swath_Poly.shp
+MT_StatewideP4_4_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_4_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_4_FlightLineSwath.shp
+MT_StatewideP4_3_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_3_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_3_B22_SwathPoly.shp
+AK_NativeAKVillages_3_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_3_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_3_D22_SWATH_POLY.shp
+AK_NativeAKVillages_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_2_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_2_D22_Swath_Poly.shp
+AK_NativeAKVillages_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_1_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_1_D22_Swath_Poly/USGS_AK_NativeAKVillages_1_D22_Swath_Poly.shp
+OR_UmatillaWallowaWhitman_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_UmatillaWallowaWhitman_B22/OR_UmatillaWallowaWhitman_1_B22/spatial_metadata/USGS/USGS_OR_UmatillaWallowaWhitman_1_B22_Swath_Poly.shp
+UT_WestEast_10_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_10_B22/spatial_metadata/USGS/USGS_UT_WestEast_10_B22_SwathPolygon.shp
+AK_NativeAKVillages_4_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_4_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_4_D22_Swath_Poly.shp
+MT_StatewideP4_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_1_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_1_B22_SwathPoly.shp
+UT_WestEast_4_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_4_B22/spatial_metadata/USGS/USGS_UT_WestEast_4_B22_SWATH_POLY.shp
+UT_WestEast_6_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_6_B22/spatial_metadata/USGS/USGS_UT_WestEast_6_B22_Swath_Poly/USGS_UT_WestEast_6_B22_Swath_Poly.shp
+UT_WestEast_5_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_5_B22/spatial_metadata/USGS/USGS_UT_WestEast_5_B22_Swath_Poly.shp
+MT_StatewideP4_7_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/MT_Statewide_Phase4_B22/MT_StatewideP4_7_B22/spatial_metadata/USGS/USGS_MT_StatewideP4_7_B22_SwathPoly/USGS_MT_StatewideP4_7_B22_SwathPoly.shp
+UT_WestEast_9_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_9_B22/spatial_metadata/USGS/USGS_UT_WestEast_9_B22_SwathPolygon.shp
+AK_SELandslides_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_SouthEastLandslides_D22/AK_SELandslides_1_D22/spatial_metadata/USGS/USGS_AK_SELandslides_1_D22_Swath_Poly/USGS_AK_SELandslides_1_D22_Swath_Poly.shp
+AZ_NavajoNation_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_NavajoNation_D22/AZ_NavajoNation_1_D22/spatial_metadata/USGS/USGS_AZ_NavajoNation_1_D22_Swath_Poly.shp
+UT_FishSprings_Topobathymetric_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_FishSprings_TB_D22/UT_FishSprings_Topobathymetric_D22/spatial_metadata/USGS/USGS_UT_FishSpings_Topobathymetric_D22_Swath_Poly.shp
+ND_Phase10_1_C23,s3://prd-tnm/StagedProducts/Elevation/metadata/ND_Lidar_Phase10/ND_Phase10_1_C23/spatial_metadata/USGS/USGS_ND_Phase10_1_C23_SwathPolygon.shp
+UT_WestEast_1_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/UT_WestEast_B22/UT_WestEast_1_B22/spatial_metadata/USGS/USGS_UT_WestEast_1_B22_Swath_Poly.shp
+CA_SaltonSea_EarthMRI_3_D21,s3://prd-tnm/StagedProducts/Elevation/metadata/CA_SaltonSea_EarthMRI_2021_D21/CA_SaltonSea_EarthMRI_3_D21/spatial_metadata/USGS/USGS_CA_SaltonSea_EarthMRI_3_D21_SwathPolygon.shp
+OR_SouthEast_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/OR_SouthEast_D22/OR_SouthEast_2_D22/spatial_metadata/USGS/USGS_OR_SouthEast_2_D22_SwathPolygon.shp
+NY_Hudson_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/NY_Hudson_D22/NY_Hudson_1_D22/spatial_metadata/USGS/USGS_NY_Hudson_1_D22_Swath_Poly.shp
+KY_CentralEast_1_A23,s3://prd-tnm/StagedProducts/Elevation/metadata/KY_CentralEast_A23/KY_CentralEast_1_A23/spatial_metadata/USGS/USGS_KY_CentralEast_1_A23_SWATH_POLY.shp
+VA_NorthernVA_2_B22,s3://prd-tnm/StagedProducts/Elevation/metadata/VA_NorthernVA_B22/VA_NorthernVA_2_B22/spatial_metadata/USGS/USGS_VA_NorthernVA_2_B22_Swath_Poly.shp
+OK_Statewide_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/OK_Statewide_D22/OK_Statewide_1_D22/spatial_metadata/USGS/USGS_OK_Statewide_1_D22_Swath_Poly.shp
+OK_Statewide_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/OK_Statewide_D22/OK_Statewide_2_D22/spatial_metadata/USGS/USGS_OK_Statewide_2_D22_SWATH_POLY.shp
+KY_CentralEast_2_A23,s3://prd-tnm/StagedProducts/Elevation/metadata/KY_CentralEast_A23/KY_CentralEast_2_A23/spatial_metadata/USGS/USGS_KY_CentralEast_2_A23_Swath_Polygon/USGS_KY_CentralEast_2_A23_Swath_Polygon.shp
+TX_LowerRioGrande_4_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_4_D22/spatial_metadata/USGS/USGS_TX_LowerRioGrande_4_D22_SWATH_POLY.shp
+TX_LowerRioGrande_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_2_D22/spatial_metadata/USGS/USGS_TX_LowerRioGrande_2_D22_Swath_Poly.shp
+TX_LowerRioGrande_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_1_D22/spatial_metadata/USGS/USGS_TX_LowerRioGrande_1_D22_SWATH_POLY.shp
+KY_CentralEast_3_A23,s3://prd-tnm/StagedProducts/Elevation/metadata/KY_CentralEast_A23/KY_CentralEast_3_A23/spatial_metadata/USGS/USGS_KY_CentralEast_3_A23_SwathPoly.shp
+TX_LowerRioGrande_3_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/TX_LowerRioGrande_D22/TX_LowerRioGrande_3_D22/spatial_metadata/USGS/USGS_TX_LowerRioGrande_3_D22_Swath_Poly/USGS_TX_LowerRioGrande_3_D22_Swath_Poly.shp
+AZ_Mohave_4_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_Mohave_D23/AZ_Mohave_4_D23/spatial_metadata/USGS/USGS_AZ_Mohave_4_D23_SWATH_POLY.shp
+AZ_Mohave_2_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_Mohave_D23/AZ_Mohave_2_D23/spatial_metadata/USGS/USGS_AZ_Mohave_2_D23_Swath_Poly/USGS_AZ_Mohave_2_D23_Swath_Poly.shp
+MO_SouthernMO_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/MO_SouthernMO_D22/MO_SouthernMO_1_D22/spatial_metadata/USGS/USGS_MO_SouthernMO_1_D22_SwathPolygon.shp
+AZ_Mohave_3_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_Mohave_D23/AZ_Mohave_3_D23/spatial_metadata/USGS/USGS_AZ_Mohave_3_D23_Swath_Poly.shp
+MO_SouthernMO_2_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/MO_SouthernMO_D22/MO_SouthernMO_2_D22/spatial_metadata/USGS/USGS_MO_SouthernMO_2_D22_SwathPolygon.shp
+MO_7Co_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/MO_7County_D23/MO_7Co_1_D23/spatial_metadata/USGS/USGS_MO_7Co_1_D23_swath_poly/USGS_MO_7Co_1_D23_Swath_Poly.shp
+MN_RiverEast_1_B23,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_RiverEast_B23/MN_RiverEast_1_B23/spatial_metadata/USGS/USGS_MN_RiverEast_1_B23_Swath_Poly.shp
+WI_2County_1_B23,s3://prd-tnm/StagedProducts/Elevation/metadata/WI_2County_B23/WI_2County_1_B23/spatial_metadata/USGS/USGS_WI_2County_1_B23_SwathPoly/USGS_WI_2County_1_B23_SwathPoly.shp
+MN_RiverWest_1_B23,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_RiverWest_B23/MN_RiverWest_1_B23/spatial_metadata/USGS/USGS_MN_RiverWest_1_B23_SWATH_POLY.shp
+MN_RiverWest_2_B23,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_RiverWest_B23/MN_RiverWest_2_B23/spatial_metadata/USGS/USGS_MN_RiverWest_2_B23_Swath_Poly.shp
+MN_RiverWest_3_B23,s3://prd-tnm/StagedProducts/Elevation/metadata/MN_RiverWest_B23/MN_RiverWest_3_B23/spatial_metadata/USGS/USGS_MN_RiverWest_3_B23_SwathPolygon.shp
+CA_FEMALevee_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/CA_FEMALevee_D23/CA_FEMALevee_1_D23/spatial_metadata/USGS/USGS_CA_FEMALevee_1_D23_Swath_Poly.shp
+AZ_Southwest_3_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_3_D23/spatial_metadata/USGS/USGS_AZ_Southwest_3_D23_Swath_Poly.shp
+AZ_Southwest_5_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_5_D23/spatial_metadata/USGS/USGS_AZ_Southwest_5_D23_SwathPoly/USGS_AZ_Southwest_5_D23_SwathPoly.shp
+AZ_Southwest_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_1_D23/spatial_metadata/USGS/USGS_AZ_Southwest_1_D23_SwathPoly.shp
+AZ_Southwest_2_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_2_D23/spatial_metadata/USGS/USGS_AZ_Southwest_2_D23_SWATH_POLY.shp
+AZ_Southwest_4_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/AZ_SouthWest_D23/AZ_Southwest_4_D23/spatial_metadata/USGS/USGS_AZ_Southwest_4_D23_Swath_Poly/USGS_AZ_Southwest_4_D23_Swath_Poly.shp
+AK_NativeAKVillages_5_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_5_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_5_D22_Swath_Poly/USGS_AK_NativeAKVillages_5_D22_Swath_Poly.shp
+AK_NativeAKVillages_6_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AK_NativeAlaskanVillages_D22/AK_NativeAKVillages_6_D22/spatial_metadata/USGS/USGS_AK_NativeAKVillages_6_D22_Swath_Polygon/USGS_AK_NativeAKVillages_6_D22_Swath_Polygon.shp
+NV_USFSR4_1_D23,s3://prd-tnm/StagedProducts/Elevation/metadata/NV_USFSR4_D23/NV_USFSR4_1_D23/spatial_metadata/USGS/USGS_NV_USFSR4_1_D23_SwathPoly/USGS_NV_USFSR4_1_D23_SwathPoly.shp
+AR_NorthEast_1_D22,s3://prd-tnm/StagedProducts/Elevation/metadata/AR_NorthEast_D22/AR_NorthEast_1_D22/spatial_metadata/USGS/USGS_AR_NorthEast_1_D22_Swath_poly/USGS_AR_NorthEast_1_D22_Swath_Poly.shp
diff --git a/src/coincident/search/wesm.py b/src/coincident/search/wesm.py
index 682fdd7..403efd4 100644
--- a/src/coincident/search/wesm.py
+++ b/src/coincident/search/wesm.py
@@ -5,24 +5,27 @@
 
 from __future__ import annotations
 
+from importlib import resources
 from typing import Any
 
-import fsspec
-import s3fs
-from geopandas import GeoDataFrame, read_file, read_parquet
-from pandas import Series, Timedelta, Timestamp
+import pandas as pd
+import pyogrio
+import rasterio.env
+from cloudpathlib import S3Client
+from geopandas import GeoDataFrame, read_file
+from pandas import Timedelta, Timestamp
+from shapely.geometry import box
 
 from coincident.datasets import usgs
 from coincident.overlaps import subset_by_temporal_overlap
 
-defaults = usgs.ThreeDEP()
-wesm_gpkg_url = defaults.search
+# explicitly instantiate a client that always uses the local cache
+client = S3Client(no_sign_request=True)
 
+swath_polygon_csv = resources.files("coincident.search") / "swath_polygons.csv"
 
-def simplify_footprint(gf: GeoDataFrame, tolerance: float = 0.1) -> None:
-    """complexity of WESM footprints causes slow STAC searches"""
-    # parts of a simplified geometry will be no more than `tolerance` distance from the original
-    gf = gf.simplify(tolerance)
+defaults = usgs.ThreeDEP()
+wesm_gpkg_url = defaults.search
 
 
 def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame:
@@ -52,6 +55,8 @@ def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame:
         "collect_end": "end_datetime",
     }
     gf = gf.rename(columns=name_map)
+    gf["start_datetime"] = pd.to_datetime(gf["start_datetime"])
+    gf["end_datetime"] = pd.to_datetime(gf["end_datetime"])
     duration = gf.end_datetime - gf.start_datetime
     gf["datetime"] = gf.start_datetime + duration / 2
     gf["dayofyear"] = gf.datetime.dt.dayofyear
@@ -60,20 +65,45 @@ def stacify_column_names(gf: GeoDataFrame) -> GeoDataFrame:
     return gf
 
 
-def search_convex_hulls(
-    url: str = "https://github.com/uw-cryo/stv-aux-data/releases/download/v0.1/WESM-chulls.geoparquet",
+def read_wesm_csv(url: str = wesm_gpkg_url) -> GeoDataFrame:
+    """
+    Read WESM metadata from a remote CSV file.
+
+    The CSV contains up to date metadata and a mapping of workunit to feature ID (FID)
+    in the main GPKG file.
+
+    Parameters
+    ----------
+    url : str, optional
+        The URL or file path to the WESM  index file
+
+    Returns
+    -------
+    GeoDataFrame
+        A GeoDataFrame containing the metadata from the CSV file.
+    """
+    # Cache CSV locally, so subsequent reads are faster
+    wesm_csv = client.CloudPath(url.replace(".gpkg", ".csv"))
+    df = pd.read_csv(wesm_csv)
+    df.index += 1
+    df.index.name = "fid"
+    return df
+
+
+def search_bboxes(
+    url: str = wesm_gpkg_url,
     intersects: GeoDataFrame | None = None,
     search_start: Timestamp | None = None,
     search_end: Timestamp | None = None,
     # **kwargs: dict[str, Any] | None,
 ) -> GeoDataFrame:
     """
-    Search GeoParquet WESM index of convex hulls within a time range and spatial intersection.
+    Search WESM.gpkg bounding boxes for spatial and temporal overlap
 
     Parameters
     ----------
     url : str, optional
-        URL to the GeoParquet file containing the convex hulls, by default "https://github.com/uw-cryo/stv-aux-data/releases/download/v0.1/WESM-chulls.geoparquet".
+        The URL or file path to the GeoPackage (GPKG) file.
     intersects : GeoDataFrame, optional
         A GeoDataFrame to spatially intersect with the convex hulls, by default None.
     search_start : Timestamp, optional
@@ -86,10 +116,25 @@ def search_convex_hulls(
     GeoDataFrame
         A GeoDataFrame containing the convex hull geometries and FIDs in EPSG:4326
     """
-    with fsspec.open(url) as f:
-        gf = read_parquet(f)
+    # NOTE: much faster to JUST read bboxes, not full geometry or other columns
+    sql = "select * from rtree_WESM_geometry"
+    with rasterio.Env(aws_unsigned=True):
+        df = pyogrio.read_dataframe(
+            url, sql=sql
+        )  # , use_arrow=True... arrow probably doesn;t matter for <10000 rows?
+
+    bboxes = df.apply(lambda x: box(x.minx, x.miny, x.maxx, x.maxy), axis=1)
+    gf = (
+        GeoDataFrame(df, geometry=bboxes, crs="EPSG:4326")
+        .rename(columns={"id": "fid"})
+        .set_index("fid")
+    )
+
+    df = read_wesm_csv(url)
+    gf = gf.merge(df, right_index=True, left_index=True)
 
     gf = stacify_column_names(gf)
+
     if intersects is not None:
         gf = gf[gf.intersects(intersects.geometry.iloc[0])]
 
@@ -122,18 +167,19 @@ def load_by_fid(
         modified by :meth:`stacify_column_names`.
     """
     # Format SQL: # special case for (a) not (a,)
+    # Reading a remote WESM by specific FIDs is fast
     query = f"fid in ({fids[0]})" if len(fids) == 1 else f"fid in {*fids,}"
-
-    gf = read_file(
-        url,
-        where=query,
-        **kwargs,
-        # mask=mask, # spatial subset intolerably slow for remote GPKG...
-        # NOTE: worth additional dependencies for speed?
-        # Only faster I think if GPKG is local https://github.com/geopandas/pyogrio/issues/252
-        # engine='pyogrio',
-        # pyarrow=True,
-    )
+    with rasterio.Env(aws_unsigned=True):
+        gf = read_file(
+            url,
+            where=query,
+            **kwargs,
+            # mask=mask, # spatial subset intolerably slow for remote GPKG...
+            # NOTE: worth additional dependencies for speed?
+            # Only faster I think if GPKG is local & large https://github.com/geopandas/pyogrio/issues/252
+            # engine='pyogrio',
+            # pyarrow=True,
+        )
 
     # For the purposes of footprint polygon search, just ignore datum (NAD83)
     gf = gf.set_crs("EPSG:4326", allow_override=True)
@@ -172,14 +218,16 @@ def swathtime_to_datetime(
     return stacify_column_names(gf)
 
 
-def get_swath_polygons(row: Series) -> GeoDataFrame:
+def get_swath_polygons(
+    workunit: str,
+) -> GeoDataFrame:
     """
     Retrieve swath polygons from a remote URL and convert swath time to datetime.
 
     Parameters
     ----------
-    row : pd.Series
-        A pandas Series containing the project and workunit information needed to construct the URL.
+    fid : int
+        A pandas Series containing WESM feature ID (FID)
 
     Returns
     -------
@@ -188,23 +236,21 @@ def get_swath_polygons(row: Series) -> GeoDataFrame:
 
     Notes
     -----
-        - Swath polygons only available for data collected after 2020
-        - Usually takes ~10 to 60s to load one of these shapefiles
+        - Swath polygons not available for all workunits
     """
-    s3 = s3fs.S3FileSystem(anon=True)
-    url = f"prd-tnm/StagedProducts/Elevation/metadata/{row.project}/{row.workunit}/spatial_metadata/USGS/**/*.shp"
+    # NOTE: this CSV comes from crawling WESM USGS/spatial_metadata in S3 bucket
+    df = pd.read_csv(swath_polygon_csv)
+
     try:
-        files = s3.glob(url)
-        swath_poly_key = next(x for x in files if "swath" in x.lower())
-    except StopIteration:
-        missing_link = f"http://prd-tnm.s3.amazonaws.com/index.html?prefix=StagedProducts/Elevation/metadata/{row.project}/{row.workunit}"
-        message = f"Unable to find swath polygon shapefile in {missing_link}"
-        raise FileNotFoundError(message) from None
+        url = df[df.workunit == workunit].swathpolygon_link.iloc[0]
+    except Exception as e:
+        message = f"No swath polygons found for workunit={workunit}"
+        raise ValueError(message) from e
 
-    uri = swath_poly_key.replace("prd-tnm", "https://prd-tnm.s3.amazonaws.com")
-    gf = read_file(uri)
+    # Actually read from S3!
+    with rasterio.Env(aws_unsigned=True):
+        gf = read_file(url)
 
     gf = swathtime_to_datetime(gf)
-
     # Swath polygons likely have different CRS (EPSG:6350), so reproject
     return gf.to_crs("EPSG:4326")
diff --git a/tests/test_search.py b/tests/test_search.py
index 0d8d5c3..a25d1c5 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -150,19 +150,13 @@ def test_wesm_search(aoi):
         dataset="3dep",
         intersects=aoi,
     )
-    assert len(gf) == 3
+    assert len(gf) == 5
 
 
 # NOTE ~10s on wifi
 @network
 def test_get_swath_polygon():
-    row = gpd.pd.Series(
-        dict(  # noqa: C408
-            project="CO_CameronPeakWildfire_2021_D21",
-            workunit="CO_CameronPkFire_1_2021",
-        )
-    )
-    gf = m.search.wesm.get_swath_polygons(row)
+    gf = m.search.wesm.get_swath_polygons("CO_CameronPkFire_1_2021")
     assert isinstance(gf, gpd.GeoDataFrame)
     assert len(gf) == 51
     assert "start_datetime" in gf.columns
@@ -171,8 +165,5 @@ def test_get_swath_polygon():
 
 @network
 def test_swath_polygon_not_found():
-    row = gpd.pd.Series(dict(project="AL_SWCentral_B22", workunit="AL_SWCentral_1_B22"))  # noqa: C408
-    with pytest.raises(
-        FileNotFoundError, match="Unable to find swath polygon shapefile"
-    ):
-        m.search.wesm.get_swath_polygons(row)
+    with pytest.raises(ValueError, match="No swath polygons found for workunit="):
+        m.search.wesm.get_swath_polygons("AL_SWCentral_1_B22")