examples/radar_processing_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: Radar processing\n",
    "\n",
    "The processing of radar data - often referred to as SAR data, which stands for Synthetice Aperture Radar - is a topic of its own and quite different from optical data that uses Red, Green, Blue, NIR and similar bands.\n",
    "This is the first of a series of jupyter notebooks where example SAR workflows will be explained that can be run on UP42.\n",
    "All of the examples are based on a specific type of analysis which is called polarimetry and processes one image at a time.\n",
    "In comparison to polarimetric there also is interferometric analysis which leverages the information encoded in suitable pairs of radar image. \n",
    "\n",
    "In this notebook we want to have a look at the Sentinel-1 GRD full scene block, followed by SNAP Sentinel-1 Polarimetric Processing. Polarimetric SAR Processing turns a Sentinel-1 GRD image into a multichannel GeoTIFF that is ready for analysis. \n",
    "\n",
    " - Prepare workflow\n",
    " - Define aoi and input parameters\n",
    " - Run Job\n",
    " - Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import up42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:19:56,546 - up42.auth - INFO - Got credentials from config file.\n",
      "2020-05-26 14:19:57,155 - up42.auth - INFO - Authentication with UP42 successful!\n"
     ]
    }
   ],
   "source": [
    "# Authenticate with UP42\n",
    "up42.authenticate(cfg_file=\"config.json\")\n",
    "#up42.authenticate(project_id=12345, project_api_key=12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare UP42 workflows\n",
    "\n",
    "Create a new project on UP42 or use an existing one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:20:10,960 - up42 - INFO - Working on Project with project_id d3ea9123-2b89-4975-be16-e978fed8329c\n"
     ]
    }
   ],
   "source": [
    "S1_SNAP_project = up42.initialize_project()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create workflow and check available blocks and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:20:14,765 - up42.project - INFO - Getting existing workflows in project ...\n",
      "2020-05-26 14:20:15,434 - up42.project - INFO - Got 12 workflows for project d3ea9123-2b89-4975-be16-e978fed8329c.\n",
      "100%|██████████| 12/12 [00:07<00:00,  1.52it/s]\n",
      "2020-05-26 14:20:23,341 - up42.project - INFO - Using existing workflow: S1-GRD_SNAP, 1585a205-3907-4905-bd02-057a17c3cf84.\n",
      "2020-05-26 14:20:24,728 - up42.tools - INFO - Getting blocks name and id, use basic=False for all block details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tiling': 'd350aa0b-ac31-4021-bbe6-fd8da366740a', 'oneatlas-spot-aoiclipped': '0f15e07f-efcc-4598-939b-18aade349c57', 'oneatlas-pleiades-aoiclipped': 'f026874d-e95e-4293-b811-7667130e054d', 'sobloo-s1-grd-fullscene': '4524e2de-c780-488d-9818-fe68dad9f095', 'sobloo-s2-l1c-fullscene': '604988cb-8252-4161-bf28-f6fb63d7371c', 'snap-polarimetric': '320158d6-8f93-4484-a828-e1fb64f677ff', 'sentinelhub-s2-aoiclipped': 'c4758545-4b74-4318-ae1f-d5ba72f234ca', 'sentinelhub-landsat8-aoiclipped': 'e0b133ae-7b9c-435c-99ac-c4527cc8d9cf', 'sobloo-s1-slc-fullscene': 'cf822545-c73c-467b-8f43-5311dbefe03f', 'nasa-modis': '61279eb8-02e1-4b7a-ac3d-1f62d19d3484', 's2-superresolution': '4872fef8-aec8-4dec-adcb-560ee4430a2b', 'oneatlas-pleiades-fullscene': '8487adcd-a4d7-4cb7-b826-75a533e1f330', 'oneatlas-spot-fullscene': 'aa62113f-0dd1-40a3-a004-954c9d087071', 'data-conversion': '470eedda-5f62-433c-8562-98eb8783af87', 'pansharpen': '2f24c662-c129-409f-a7c3-afa16a4c78cb', 'sobloo-s1-grd-aoiclipped': 'a956166f-c0ed-4670-8a43-87bed8d222f3', 'hello-world': '77899bd0-40de-4aa7-b67e-513a5655afcb', 'ndvi': 'aecb81ef-1c92-4b2e-aa25-55ccebe2f90a', 'sobloo-s2-l1c-aoiclipped': 'a2daaab4-196d-4226-a018-a810444dcad1', 'sobloo-s3': 'fee13ec1-a067-4d6a-95dc-a4fef458f4f4', 'sobloo-s5p': 'cba7c59b-548d-48bf-8920-c20d7d316dfd', 'kmeans-clustering': 'adf21e0a-98bf-41a9-a2cc-a59898a461ba', 'vectorising': '295bf286-0748-474f-aa38-c1ac35151204', 'crs-conversion': '18e6772f-cf33-4955-b7e4-61df8a108fd9', 'sharpening': '4ed70368-d4e1-4462-bef6-14e768049471', 'zonal-statistics': 'a5b8b938-6fd6-4bac-92cd-dffd7f3169aa', 'superresolution': '6c914299-7203-40ad-9b89-de0b4e827bd0', 's5p-lvl3': '9a593e06-eca0-49e0-8b8c-6fe95f699f9d', 'hexagon-aerial-30cm': '0b04d9f7-3a8e-4467-9fb8-1e8343c9469a', 'hexagon-aerial-15cm': '36fe7d3a-4671-424b-bd54-918dd21cfde1', 'ship-identification': '20a3bd1e-3f27-40cf-9915-8fa3d5024ade', 'ais-hvp': '7394287a-2458-4204-be62-36b6d264bcfe', 'ais-hvt': '67eb1763-abeb-4188-b135-f6a0d669d759', 'meteomatics': 'ed0beedb-111b-4285-aa2d-f876a4c16a32', 'oneatlas-pleiades-primary': 'd1e5e0de-71fa-4488-9c0e-3f22ac74a2b6', 'tiled-k-means': '45c0284a-5cd7-4bc0-9d6c-db05f7271036', 'data-conversion-netcdf': 'c358d5af-7819-4ecf-b8b3-629d5d3ba319', 'data-conversion-dimap': '25f42430-d108-4ea4-a81d-2c2b3fff7d11', 'aws-s2-l2a': '73f1b9e0-83de-4cbd-addf-5e0d23ed65d4', 'nextmapone-3m': '56a0ac08-69a5-491a-993d-63e3b9da315c', 'nextmapone-6m': 'f87dd9f3-fc14-48b1-b77d-d695fb65fcc4', 'nextmapone-1m': 'bfd43fbc-b662-4874-9147-658a55bf9edc'}\n"
     ]
    }
   ],
   "source": [
    "workflow = S1_SNAP_project.create_workflow(name=\"S1-GRD_SNAP\", \n",
    "                                   use_existing=True)\n",
    "print(up42.get_blocks(basic=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:21:03,273 - up42.workflow - INFO - Added tasks to workflow: [{'name': 'sobloo-s1-grd-fullscene:1', 'parentName': None, 'blockId': '4524e2de-c780-488d-9818-fe68dad9f095'}, {'name': 'snap-polarimetric:1', 'parentName': 'sobloo-s1-grd-fullscene:1', 'blockId': '320158d6-8f93-4484-a828-e1fb64f677ff'}]\n",
      "2020-05-26 14:21:04,000 - up42.workflow - INFO - Got 2 tasks/blocks in workflow 1585a205-3907-4905-bd02-057a17c3cf84.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'sobloo-s1-grd-fullscene:1': {'ids': {'type': 'array', 'default': None},\n",
       "  'bbox': {'type': 'array', 'default': None},\n",
       "  'time': {'type': 'dateRange',\n",
       "   'default': '2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00'},\n",
       "  'limit': {'type': 'integer', 'default': 1, 'minimum': 1},\n",
       "  'contains': {'type': 'geometry'},\n",
       "  'intersects': {'type': 'geometry'},\n",
       "  'time_series': {'type': 'array', 'default': None},\n",
       "  'orbit_direction': {'type': 'string', 'default': None},\n",
       "  'acquisition_mode': {'type': 'string', 'default': None}},\n",
       " 'snap-polarimetric:1': {'bbox': {'type': 'array', 'default': None},\n",
       "  'mask': {'type': 'array',\n",
       "   'items': {'enum': ['land', 'sea'], 'type': 'string'},\n",
       "   'default': None},\n",
       "  'contains': {'type': 'geometry', 'default': None},\n",
       "  'intersects': {'type': 'geometry', 'default': None},\n",
       "  'clip_to_aoi': {'type': 'boolean', 'default': False},\n",
       "  'tcorrection': {'type': 'boolean', 'default': True},\n",
       "  'linear_to_db': {'type': 'boolean', 'default': True},\n",
       "  'polarisations': {'type': 'array',\n",
       "   'items': {'enum': ['VV', 'VH'], 'type': 'string'},\n",
       "   'default': ['VV'],\n",
       "   'required': False,\n",
       "   'description': 'Requested polarisations for the output'},\n",
       "  'speckle_filter': {'type': 'boolean', 'default': True},\n",
       "  'calibration_band': {'type': 'array', 'default': ['sigma']}}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill the workflow with tasks\n",
    "input_tasks= ['sobloo-s1-grd-fullscene', 'snap-polarimetric']\n",
    "workflow.add_workflow_tasks(input_tasks=input_tasks)\n",
    "workflow.get_parameters_info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define aoi and input parameters\n",
    "\n",
    "The S1 GRD block always delivers the complete images as they are delivered in SAFE format which cannot be clipped.\n",
    "The image can be clipped though by the SNAP polarimetric processing blocks. For this it is necessary to supply the same geometry (in this case a bbox) to that block as well. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_parameters = {\n",
    "  \"sobloo-s1-grd-fullscene:1\": {\n",
    "    \"bbox\": [13.371037, 52.512799, 13.382624, 52.518747],\n",
    "    \"ids\": None,\n",
    "    \"time\": \"2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00\",\n",
    "    \"limit\": 1,\n",
    "    \"zoom_level\": 14,\n",
    "  },\n",
    "    \"snap-polarimetric:1\": {\n",
    "    \"bbox\": [13.371037, 52.512799, 13.382624, 52.518747],\n",
    "    \"mask\": None,\n",
    "    \"contains\": None,\n",
    "    \"intersects\": None,\n",
    "    \"clip_to_aoi\": True,\n",
    "    \"tcorrection\": True,\n",
    "    \"linear_to_db\": True,\n",
    "    \"polarisations\": [\n",
    "        \"VV\"\n",
    "   ],\n",
    "   \"speckle_filter\": True,\n",
    "   \"calibration_band\": [\n",
    "         \"sigma\"\n",
    "   ]\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run test query\n",
    "Run a test job to query data availability and check the configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:21:14,155 - up42.workflow - INFO - +++++++++++++++++++++++++++++++++\n",
      "2020-05-26 14:21:14,157 - up42.workflow - INFO - Running this job as Test Query...\n",
      "2020-05-26 14:21:14,157 - up42.workflow - INFO - +++++++++++++++++++++++++++++++++\n",
      "2020-05-26 14:21:14,158 - up42.workflow - INFO - Selected input_parameters: {'sobloo-s1-grd-fullscene:1': {'bbox': [13.371037, 52.512799, 13.382624, 52.518747], 'ids': None, 'time': '2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00', 'limit': 1, 'zoom_level': 14}, 'snap-polarimetric:1': {'bbox': [13.371037, 52.512799, 13.382624, 52.518747], 'mask': None, 'contains': None, 'intersects': None, 'clip_to_aoi': True, 'tcorrection': True, 'linear_to_db': True, 'polarisations': ['VV'], 'speckle_filter': True, 'calibration_band': ['sigma']}, 'config': {'mode': 'DRY_RUN'}}.\n",
      "2020-05-26 14:21:15,659 - up42.workflow - INFO - Created and running new job: 28c72c74-a5dc-46e3-bb70-f6efad3f910c.\n",
      "2020-05-26 14:21:16,144 - up42.job - INFO - Tracking job status continuously, reporting every 30 seconds...\n",
      "2020-05-26 14:21:39,235 - up42.job - INFO - Job finished successfully! - 28c72c74-a5dc-46e3-bb70-f6efad3f910c\n",
      "2020-05-26 14:21:44,956 - up42.job - INFO - Retrieved 1 features.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'bbox': [12.306028, 52.168652, 16.66515, 54.078037], 'id': '5a0bcc6b-8316-45c9-9106-8de8e27e5753', 'geometry': {'type': 'Polygon', 'coordinates': [[[16.151102, 52.168652], [16.66515, 53.660217], [12.685362, 54.078037], [12.306028, 52.5835], [16.151102, 52.168652]]]}, 'properties': {'attachments': [], 'visibility': {'deleted': False}, 'illumination': {}, 'production': {'levelCode': 'L1', 'ongoing': False, 'timeliness': 'Fast-24h'}, 'archive': {'offLine': False, 'filename': 'S1A_IW_GRDH_1SDV_20200525T051659_20200525T051724_032717_03CA2F_DB50.SAFE', 'size': 1689, 'format': 'SAFE', 'onLine': False}, 'spatialCoverage': {'verticality': {}, 'geometry': {'geographicBoundingPolygon': {'coordinates': [[[16.151102, 52.168652], [16.66515, 53.660217], [12.685362, 54.078037], [12.306028, 52.5835], [16.151102, 52.168652]]], 'type': 'Polygon'}, 'global': False, 'centerPoint': {'lon': 14.45556570632881, 'lat': 53.12652491313295}}}, 'quality': {'qualified': False}, 'target': {}, 'timeStamp': 1590383819131, 'uid': '5a0bcc6b-8316-45c9-9106-8de8e27e5753', 'identification': {'profile': 'Image', 'externalId': 'S1A_IW_GRDH_1SDV_20200525T051659_20200525T051724_032717_03CA2F_DB50', 'collection': 'Sentinel-1', 'type': 'GRD', 'dataset': {}}, 'transmission': {}, 'contentDescription': {}, 'provider': {}, 'acquisition': {'endViewingDate': 1590383844129, 'mission': 'Sentinel-1', 'missionId': 'A', 'missionCode': 'S1A', 'beginViewingDate': 1590383819131, 'missionName': 'Sentinel-1A', 'polarization': 'VV VH', 'type': 'NOMINAL', 'sensorMode': 'IW', 'sensorId': 'SAR-C SAR'}, 'orbit': {'relativeNumber': 95, 'lastRelativeNumber': 95, 'lastNumber': 32717, 'direction': 'DESCENDING'}, 'state': {'resources': {'thumbnail': True, 'quicklook': True}, 'services': {'wmts': False, 'download': 'internal', 'wcs': False, 'wms': False}, 'insertionDate': 1590395388355}, 'attitude': {}}}]}\n"
     ]
    }
   ],
   "source": [
    "test_job = workflow.test_job(input_parameters=input_parameters, track_status=True)\n",
    "test_results = test_job.get_results_json()\n",
    "print(test_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-26 14:21:44,963 - up42.workflow - INFO - Selected input_parameters: {'sobloo-s1-grd-fullscene:1': {'bbox': [13.371037, 52.512799, 13.382624, 52.518747], 'ids': None, 'time': '2018-01-01T00:00:00+00:00/2020-12-31T23:59:59+00:00', 'limit': 1, 'zoom_level': 14}, 'snap-polarimetric:1': {'bbox': [13.371037, 52.512799, 13.382624, 52.518747], 'mask': None, 'contains': None, 'intersects': None, 'clip_to_aoi': True, 'tcorrection': True, 'linear_to_db': True, 'polarisations': ['VV'], 'speckle_filter': True, 'calibration_band': ['sigma']}}.\n",
      "2020-05-26 14:21:46,310 - up42.workflow - INFO - Created and running new job: 2e0e9cf0-8d80-439a-bc50-179543641ea9.\n",
      "2020-05-26 14:21:46,734 - up42.job - INFO - Tracking job status continuously, reporting every 30 seconds...\n",
      "2020-05-26 14:22:21,006 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:23:06,480 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:23:40,185 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:24:13,848 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:24:47,455 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:25:21,147 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:25:54,825 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:26:28,527 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:27:02,484 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:27:36,284 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:28:09,935 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:28:42,827 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:29:15,409 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:29:48,015 - up42.job - INFO - Job is RUNNING! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n",
      "2020-05-26 14:30:20,694 - up42.job - INFO - Job finished successfully! - 2e0e9cf0-8d80-439a-bc50-179543641ea9\n"
     ]
    }
   ],
   "source": [
    "job = workflow.run_job(input_parameters=input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download results:\n",
    "job.download_results()\n",
    "\n",
    "# Visualize downloaded results\n",
    "job.plot_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next processing steps\n",
    "The output of the workflow is a GeoTIFF file which can be processed by different algorithms. It is for example possible to apply raster tiling and then do some machine learning based analysis at the end.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "up42-py-test",
   "language": "python",
   "name": "up42-py-test"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}