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CSIROBoeingPhase5-Vietnam/processMask-test.ipynb
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big update
2024-09-09 07:09:26 +00:00
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" \n",
"\n",
"\n",
"var JS_MIME_TYPE = 'application/javascript';\n",
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"\n",
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" var events = require('base/js/events');\n",
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" }\n",
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"\ton_load()\n",
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" require([\"notyf\"], function() {\n",
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" element.rel = \"stylesheet\";\n",
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" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
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" }\n",
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" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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" }\n",
" for (var i = 0; i < js_modules.length; i++) {\n",
" var url = js_modules[i];\n",
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" element.src = url;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
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" var url = js_exports[name];\n",
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" var element = document.createElement('script');\n",
" element.onerror = on_error;\n",
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" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" element.textContent = `\n",
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"\n",
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"\t Bokeh.versions = new Map();\n",
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"\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
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"\troot.Bokeh = Bokeh;\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
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" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" }\n",
" root._bokeh_is_initializing = false\n",
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"\n",
" function load_or_wait() {\n",
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},
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"name": "stdout",
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"text": [
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"Wall time: 10.8 s\n"
]
}
],
"source": [
"%%time\n",
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]
},
{
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"id": "20e98f76-f879-4999-9caa-9698c4c40b0f",
"metadata": {
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},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"An existing cluster was found. Connecting to: easihub.9f2f42f40c134601a68634913d891637\n",
"CPU times: user 45.6 ms, sys: 5.11 ms, total: 50.7 ms\n",
"Wall time: 282 ms\n"
]
},
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"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-3f237e50-6e75-11ef-8269-d24f7674e586</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" </td>\n",
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" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
" <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.9f2f42f40c134601a68634913d891637/status\" }'>\n",
" Launch dashboard in JupyterLab\n",
" </button>\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
" <h3>GatewayCluster</h3>\n",
" <ul>\n",
" <li><b>Name: </b>easihub.9f2f42f40c134601a68634913d891637\n",
" <li><b>Dashboard: </b><a href='https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.9f2f42f40c134601a68634913d891637/status' target='_blank'>https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.9f2f42f40c134601a68634913d891637/status</a>\n",
" </ul>\n",
"</div>\n",
"\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tls://10.0.63.187:8786' processes=4 threads=32, memory=112.00 GiB>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"# Dask gateway\n",
"cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\n",
"dc = datacube.Datacube()\n",
"\n",
"# Configure s3 access\n",
"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
"\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bdd90d49-48ad-4aa3-b2c5-d81c4cfd3127",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## cấu hình thời gian lấy ảnh và tọa độ\n",
"# date_range = ('2022-09-01', '2023-10-01')\n",
"# longtitude_range = (105.86575, 105.94120)\n",
"# latitude_range = (9.65070, 9.69850)\n",
"date_range = ('2022-09-01', '2023-10-01')\n",
"longtitude_range = (105.5, 106.4)\n",
"latitude_range = (9.2, 10.0) "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "307e0fd1-0030-47bd-9a1d-3a42c0c7c9a1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most common native CRS: EPSG:32648\n",
"No datasets require offset correction\n",
"The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n"
]
},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 151, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648\n",
"Data variables:\n",
" blue (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" green (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" red (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" nir (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" scl (time, y, x) uint8 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
"Attributes:\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-da674d15-3013-4ae4-ab07-0f5b6806a0a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da674d15-3013-4ae4-ab07-0f5b6806a0a9' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-351bb29f-b14a-495b-b502-51190856f061' class='xr-section-summary-in' type='checkbox' checked><label for='section-351bb29f-b14a-495b-b502-51190856f061' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-02T03:35:23.960000 ... 2...</div><input id='attrs-46195ef2-7260-4966-a9ed-cab20a5d9cfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46195ef2-7260-4966-a9ed-cab20a5d9cfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-231a2cd0-ada9-4c51-af6a-6915c136a830' class='xr-var-data-in' type='checkbox'><label for='data-231a2cd0-ada9-4c51-af6a-6915c136a830' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-02T03:35:23.960000000&#x27;, &#x27;2022-09-04T03:25:18.037000000&#x27;,\n",
" &#x27;2022-09-07T03:35:13.646000000&#x27;, &#x27;2022-09-09T03:25:27.617000000&#x27;,\n",
" &#x27;2022-09-12T03:35:22.247000000&#x27;, &#x27;2022-09-14T03:25:18.529000000&#x27;,\n",
" &#x27;2022-09-17T03:35:13.648000000&#x27;, &#x27;2022-09-19T03:25:24.773000000&#x27;,\n",
" &#x27;2022-09-22T03:35:18.931000000&#x27;, &#x27;2022-09-24T03:25:17.431000000&#x27;,\n",
" &#x27;2022-09-27T03:35:12.140000000&#x27;, &#x27;2022-09-29T03:25:23.829000000&#x27;,\n",
" &#x27;2022-10-02T03:35:19.048000000&#x27;, &#x27;2022-10-07T03:35:09.133000000&#x27;,\n",
" &#x27;2022-10-09T03:25:22.746000000&#x27;, &#x27;2022-10-12T03:35:17.151000000&#x27;,\n",
" &#x27;2022-10-14T03:25:15.533000000&#x27;, &#x27;2022-10-17T03:35:11.148000000&#x27;,\n",
" &#x27;2022-10-19T03:25:18.808000000&#x27;, &#x27;2022-10-22T03:35:13.746000000&#x27;,\n",
" &#x27;2022-10-24T03:25:15.878000000&#x27;, &#x27;2022-10-27T03:35:10.923000000&#x27;,\n",
" &#x27;2022-10-29T03:25:20.127000000&#x27;, &#x27;2022-11-01T03:35:15.818000000&#x27;,\n",
" &#x27;2022-11-03T03:25:14.204000000&#x27;, &#x27;2022-11-06T03:35:10.096000000&#x27;,\n",
" &#x27;2022-11-11T03:35:15.334000000&#x27;, &#x27;2022-11-13T03:25:16.056000000&#x27;,\n",
" &#x27;2022-11-16T03:35:11.542000000&#x27;, &#x27;2022-11-18T03:25:18.155000000&#x27;,\n",
" &#x27;2022-11-21T03:35:12.396000000&#x27;, &#x27;2022-11-23T03:25:16.067000000&#x27;,\n",
" &#x27;2022-11-26T03:35:11.063000000&#x27;, &#x27;2022-11-28T03:25:16.297000000&#x27;,\n",
" &#x27;2022-12-01T03:35:12.009000000&#x27;, &#x27;2022-12-03T03:25:14.366000000&#x27;,\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</div><input id='attrs-0f81a330-fa76-411c-bc9a-4744aca4be47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f81a330-fa76-411c-bc9a-4744aca4be47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45553ab2-b79b-4646-aa31-3717f5091687' class='xr-var-data-in' type='checkbox'><label for='data-45553ab2-b79b-4646-aa31-3717f5091687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>flags_definition :</span></dt><dd>{&#x27;qa&#x27;: {&#x27;bits&#x27;: [0, 1, 2, 3, 4, 5, 6, 7], &#x27;values&#x27;: {&#x27;0&#x27;: &#x27;no data&#x27;, &#x27;1&#x27;: &#x27;saturated or defective&#x27;, &#x27;2&#x27;: &#x27;dark area pixels&#x27;, &#x27;3&#x27;: &#x27;cloud shadows&#x27;, &#x27;4&#x27;: &#x27;vegetation&#x27;, &#x27;5&#x27;: &#x27;bare soils&#x27;, &#x27;6&#x27;: &#x27;water&#x27;, &#x27;7&#x27;: &#x27;unclassified&#x27;, &#x27;8&#x27;: &#x27;cloud medium probability&#x27;, &#x27;9&#x27;: &#x27;cloud high probability&#x27;, &#x27;10&#x27;: &#x27;thin cirrus&#x27;, &#x27;11&#x27;: &#x27;snow or ice&#x27;}, &#x27;description&#x27;: &#x27;Sen2Cor Scene Classification&#x27;}}</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
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" <th> Chunk </th>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 12.36 GiB </td>\n",
" <td> 4.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (151, 8874, 9902) </td>\n",
" <td> (1, 2048, 2048) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 3775 chunks in 1 graph layer </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-76876aff-502e-451d-a2f0-13ea8164e853' class='xr-section-summary-in' type='checkbox' ><label for='section-76876aff-502e-451d-a2f0-13ea8164e853' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8d415d7a-c41a-4dad-9c56-5dc97bc0325a' class='xr-index-data-in' type='checkbox'/><label for='index-8d415d7a-c41a-4dad-9c56-5dc97bc0325a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-02 03:35:23.960000&#x27;, &#x27;2022-09-04 03:25:18.037000&#x27;,\n",
" &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
" &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
" &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
" &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
" ...\n",
" &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
" &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
" &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
" &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
" &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-575c2317-530e-47b5-8ace-917f99d56274' class='xr-index-data-in' type='checkbox'/><label for='index-575c2317-530e-47b5-8ace-917f99d56274' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
" ...\n",
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd2e0bb5-a936-4da6-be52-f209cf4a1de0' class='xr-index-data-in' type='checkbox'/><label for='index-dd2e0bb5-a936-4da6-be52-f209cf4a1de0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
" 554865.0, 554875.0, 554885.0,\n",
" ...\n",
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
" 653785.0, 653795.0, 653805.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1b22f4a-8cd8-4942-9bbc-ab29aaa718b9' class='xr-section-summary-in' type='checkbox' checked><label for='section-d1b22f4a-8cd8-4942-9bbc-ab29aaa718b9' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 151, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648\n",
"Data variables:\n",
" blue (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" green (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" red (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" nir (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" scl (time, y, x) uint8 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
"Attributes:\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## truy vấn ảnh vệ tinh sen2\n",
"data = load_data(dc, date_range, longtitude_range, latitude_range)\n",
"notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
"display(data)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "56f9b785-21cb-4e37-ab3b-52d51cbcb782",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Specify the start and end times \n",
"min_date = '2022-09-01' # Thời gian bắt đầu lấy data cho quá trình train\n",
"max_date = '2023-10-01' # Thời gian kết thúc lấy data cho quá trình train\n",
"# Just do 1 month for testing\n",
"# max_date = '2022-10-01' # Thời gian kết thúc lấy data cho quá trình train\n",
"\n",
"# Specify a spatail region to search using latitude/longitude cooridinates\n",
"min_longitude, max_longitude = (105.5, 106.4)\n",
"min_latitude, max_latitude = (9.2, 10.0)\n",
"\n",
"# Specify the product. In this case we want to use Sentinel-2 Level-2A data\n",
"product = 's2_l2a'\n",
"\n",
"# Construct the search query dictionary\n",
"query = {\n",
" 'product': product, # Product name\n",
" 'x': (min_longitude, max_longitude), # \"x\" axis bounds\n",
" 'y': (min_latitude, max_latitude), # \"y\" axis bounds\n",
" 'time': (min_date, max_date), # Any parsable date strings\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "23ec5035-6989-40ae-acf0-f0917e45f0e1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Most common native CRS: EPSG:32648\n"
]
}
],
"source": [
"# Most common CRS\n",
"native_crs = notebook_utils.mostcommon_crs(dc, query)\n",
"print(f'Most common native CRS: {native_crs}')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "317db083-3549-40d9-b0b4-fec5bec8a8bf",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Specify the spectral band measurements we want to use for a classification algorithm\n",
"measurements = ['red', 'nir', 'scl']\n",
"\n",
"load_params = {\n",
" 'measurements': measurements, # Selected measurement or alias names\n",
" 'output_crs': native_crs, # Target EPSG code\n",
" 'resolution': (-10, 10), # Target resolution\n",
" 'group_by': 'solar_day', # Scene grouping\n",
" 'dask_chunks': {'x': 2048, 'y': 2048}, # Dask chunks\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "d23f9e0b-54fc-4993-a5ad-ed36624e2221",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No datasets require offset correction\n",
"The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n"
]
},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 151, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648\n",
"Data variables:\n",
" red (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" nir (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" scl (time, y, x) uint8 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
"Attributes:\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-41503c4a-ed9a-47fd-8fab-030966939697' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41503c4a-ed9a-47fd-8fab-030966939697' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ea84ce6c-e63a-4a65-b887-90bbbf6add94' class='xr-section-summary-in' type='checkbox' checked><label for='section-ea84ce6c-e63a-4a65-b887-90bbbf6add94' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-02T03:35:23.960000 ... 2...</div><input id='attrs-6bfbdba2-9eab-495f-a621-402c8797567b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bfbdba2-9eab-495f-a621-402c8797567b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f54d0aa5-6d93-4648-8f8e-53fd1739c1d1' class='xr-var-data-in' type='checkbox'><label for='data-f54d0aa5-6d93-4648-8f8e-53fd1739c1d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-02T03:35:23.960000000&#x27;, &#x27;2022-09-04T03:25:18.037000000&#x27;,\n",
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" &#x27;2022-10-24T03:25:15.878000000&#x27;, &#x27;2022-10-27T03:35:10.923000000&#x27;,\n",
" &#x27;2022-10-29T03:25:20.127000000&#x27;, &#x27;2022-11-01T03:35:15.818000000&#x27;,\n",
" &#x27;2022-11-03T03:25:14.204000000&#x27;, &#x27;2022-11-06T03:35:10.096000000&#x27;,\n",
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" &#x27;2022-11-26T03:35:11.063000000&#x27;, &#x27;2022-11-28T03:25:16.297000000&#x27;,\n",
" &#x27;2022-12-01T03:35:12.009000000&#x27;, &#x27;2022-12-03T03:25:14.366000000&#x27;,\n",
" &#x27;2022-12-06T03:35:10.293000000&#x27;, &#x27;2022-12-08T03:25:17.072000000&#x27;,\n",
" &#x27;2022-12-11T03:35:12.184000000&#x27;, &#x27;2022-12-16T03:35:10.130000000&#x27;,\n",
" &#x27;2022-12-18T03:25:15.584000000&#x27;, &#x27;2022-12-21T03:35:09.988000000&#x27;,\n",
" &#x27;2022-12-23T03:25:15.763000000&#x27;, &#x27;2022-12-26T03:35:11.128000000&#x27;,\n",
" &#x27;2022-12-28T03:25:15.151000000&#x27;, &#x27;2022-12-31T03:35:10.753000000&#x27;,\n",
" &#x27;2023-01-02T03:25:15.152000000&#x27;, &#x27;2023-01-05T03:35:09.844000000&#x27;,\n",
" &#x27;2023-01-07T03:25:15.343000000&#x27;, &#x27;2023-01-10T03:35:10.149000000&#x27;,\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</div><input id='attrs-3016cf27-1ad1-4479-92a4-f2e7cee029b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3016cf27-1ad1-4479-92a4-f2e7cee029b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebd073a7-1c15-4b70-8d4a-f1899268ac95' class='xr-var-data-in' type='checkbox'><label for='data-ebd073a7-1c15-4b70-8d4a-f1899268ac95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>nodata :</span></dt><dd>0</dd><dt><span>flags_definition :</span></dt><dd>{&#x27;qa&#x27;: {&#x27;bits&#x27;: [0, 1, 2, 3, 4, 5, 6, 7], &#x27;values&#x27;: {&#x27;0&#x27;: &#x27;no data&#x27;, &#x27;1&#x27;: &#x27;saturated or defective&#x27;, &#x27;2&#x27;: &#x27;dark area pixels&#x27;, &#x27;3&#x27;: &#x27;cloud shadows&#x27;, &#x27;4&#x27;: &#x27;vegetation&#x27;, &#x27;5&#x27;: &#x27;bare soils&#x27;, &#x27;6&#x27;: &#x27;water&#x27;, &#x27;7&#x27;: &#x27;unclassified&#x27;, &#x27;8&#x27;: &#x27;cloud medium probability&#x27;, &#x27;9&#x27;: &#x27;cloud high probability&#x27;, &#x27;10&#x27;: &#x27;thin cirrus&#x27;, &#x27;11&#x27;: &#x27;snow or ice&#x27;}, &#x27;description&#x27;: &#x27;Sen2Cor Scene Classification&#x27;}}</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 12.36 GiB </td>\n",
" <td> 4.00 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (151, 8874, 9902) </td>\n",
" <td> (1, 2048, 2048) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 3775 chunks in 1 graph layer </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-37fa4e13-ae84-4c2f-865a-01a499950fb2' class='xr-section-summary-in' type='checkbox' ><label for='section-37fa4e13-ae84-4c2f-865a-01a499950fb2' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a94742d1-5e00-40b6-bab2-2e8f295598ec' class='xr-index-data-in' type='checkbox'/><label for='index-a94742d1-5e00-40b6-bab2-2e8f295598ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-02 03:35:23.960000&#x27;, &#x27;2022-09-04 03:25:18.037000&#x27;,\n",
" &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
" &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
" &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
" &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
" ...\n",
" &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
" &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
" &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
" &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
" &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b52d41aa-fd66-4296-aadc-f87d94926074' class='xr-index-data-in' type='checkbox'/><label for='index-b52d41aa-fd66-4296-aadc-f87d94926074' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
" ...\n",
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0f365e2-f4a4-42dc-a684-6df39c7aadc3' class='xr-index-data-in' type='checkbox'/><label for='index-a0f365e2-f4a4-42dc-a684-6df39c7aadc3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
" 554865.0, 554875.0, 554885.0,\n",
" ...\n",
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
" 653785.0, 653795.0, 653805.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77319c5a-a28c-4878-9870-45d92744d1c1' class='xr-section-summary-in' type='checkbox' checked><label for='section-77319c5a-a28c-4878-9870-45d92744d1c1' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
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"%%time\n",
"# The replacement \"dc.load()\" function for this product\n",
"data = load_s2l2a_with_offset(\n",
" dc,\n",
" query | load_params # Combine the two dicts that contain our search and load parameters\n",
")\n",
"\n",
"# This line prints the total size of the dataset hat was loaded\n",
"notebook_utils.heading(notebook_utils.xarray_object_size(data))\n",
"\n",
"display(data)"
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" &#x27;2023-09-19T03:25:24.089000000&#x27;, &#x27;2023-09-22T03:35:15.439000000&#x27;,\n",
" &#x27;2023-09-24T03:25:18.524000000&#x27;, &#x27;2023-09-27T03:35:14.112000000&#x27;,\n",
" &#x27;2023-09-29T03:25:19.046000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-dd899c7b-68a6-4e7e-bfcc-dc305c331689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd899c7b-68a6-4e7e-bfcc-dc305c331689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bfd3b38-5c0f-47bf-80d4-5183be4ed179' class='xr-var-data-in' type='checkbox'><label for='data-4bfd3b38-5c0f-47bf-80d4-5183be4ed179' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-551122ed-9c99-431a-aaaa-1002cfb34d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-551122ed-9c99-431a-aaaa-1002cfb34d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-395f2be5-9ae5-451c-ab70-af36ccdf6526' class='xr-var-data-in' type='checkbox'><label for='data-395f2be5-9ae5-451c-ab70-af36ccdf6526' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32648</div><input id='attrs-a3f7d960-9fa6-445b-b2af-08a8db920bc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3f7d960-9fa6-445b-b2af-08a8db920bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bd4690a-894c-47e3-a8f1-fcc5f116f02b' class='xr-var-data-in' type='checkbox'><label for='data-6bd4690a-894c-47e3-a8f1-fcc5f116f02b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr
" &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
" &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
" &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
" &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
" ...\n",
" &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
" &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
" &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
" &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
" &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01fb69ed-39df-4867-abf7-e845a04898fa' class='xr-index-data-in' type='checkbox'/><label for='index-01fb69ed-39df-4867-abf7-e845a04898fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
" ...\n",
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9aa25ac5-134b-41a2-8d8f-b30b9b7fe816' class='xr-index-data-in' type='checkbox'/><label for='index-9aa25ac5-134b-41a2-8d8f-b30b9b7fe816' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
" 554865.0, 554875.0, 554885.0,\n",
" ...\n",
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
" 653785.0, 653795.0, 653805.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c65fd67-18c6-4b00-806a-766e8dcb5c69' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c65fd67-18c6-4b00-806a-766e8dcb5c69' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)>\n",
"dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Tiến hành tính toán NDVI\n",
"ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n",
"ndvi = ds1[\"NDVI\"]\n",
"display(ndvi)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "bb7a152b-89e5-4c18-9103-6a9f6708bd80",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 370 ms, sys: 485 µs, total: 371 ms\n",
"Wall time: 370 ms\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9923c2810c0b47d68fd0363a6657a57a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%time\n",
"## tính ndvi theo tháng\n",
"average_ndvi = ndvi.resample(time='1M').mean().persist()\n",
"progress(average_ndvi)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "47fc921b-bcb6-463f-bdaf-a5a82970a73e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# compute average_ndvi\n",
"average_ndvi = average_ndvi.compute()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "9b989a28-1524-471b-9a2b-51df2319620e",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
"Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
"Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
"Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
"Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
"Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n"
]
}
],
"source": [
"# cấu hình vh vv file\n",
"# name_vh = \"ThuanHoa/ThuanHoa_VH.tif\"\n",
"# name_vv = \"ThuanHoa/ThuanHoa_VV.tif\"\n",
"\n",
"# load dữ liệu sen1\n",
"# dsvh, dsvv = load_sen1(name_vh, name_vv)\n",
"\n",
"name_vh = \"vh-0922_0923-full_ST.tif\"\n",
"name_vv = \"vv-0922_0923-full_ST.tif\"\n",
"\n",
"if not os.path.exists(name_vh):\n",
" !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif\n",
"if not os.path.exists(name_vv):\n",
" !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif\n",
" \n",
"dsvh, dsvv = load_sen1(name_vh, name_vv)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "03c47481-7ef9-4efe-ae0a-7845595c2671",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "07acfddc-ab36-4ef4-9e72-d984f2180eb0",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(13, 7680, 8687)\n",
"(13, 7680, 8687)\n",
"(13, 7680, 8687)\n",
"(13, 7680, 8687)\n"
]
}
],
"source": [
"average_ndvi = average_ndvi[:, :7680, :8687]\n",
"mask = ~np.isnan(average_ndvi)\n",
"print(average_ndvi.shape)\n",
"print(dsvh.shape)\n",
"print(dsvv.shape)\n",
"print(mask.shape)\n",
"X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)\n",
"y_train = average_ndvi.values[mask]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "876d87da-9121-4ef2-a41b-f647e0175689",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression()\n",
"model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "854abc28-a490-4858-8951-03fd257c92e6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)\n",
"average_ndvi.values[~mask] = model.predict(X_pred)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "86f42685-0db7-4253-b02a-41df237ae4df",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"average_ndvi_filled = xr.DataArray(average_ndvi, dims=average_ndvi.dims)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "4222959a-ea43-4cc8-a958-6c8cf19c2118",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f4c7546ac80>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(average_ndvi_filled.isel(time=6))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "182e5d3f-5d1b-4476-90a7-8aff95c7c873",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f1e59b20f40>"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(average_ndvi.isel(time=6))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "7322812d-ba2a-4a82-a43d-cfd5f46a164d",
"metadata": {},
"outputs": [],
"source": [
"train_path = \"train/ST_training data_updated_1130points.shp\""
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "e0d607f3-bcee-4158-b22e-2e6e2f9053ca",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"train = load_train_data(train_path)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "0ab4448b-5bdb-41d1-b90d-1b88b0cbf188",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"datasets = get_data_sen1_and_sen2(train, average_ndvi_filled, dsvh, dsvv)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "94789967-36d7-488c-a61e-2f8fd341581f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# cấu hình nhãn dữ liệu\n",
"label_mapping = {\n",
" \"Lua tom\": \"0\",\n",
" \"Lua\": \"1\",\n",
" \"CHN\": \"2\",\n",
" \"CLN\": \"3\",\n",
" \"TS\": \"4\",\n",
" \"Song\": \"5\",\n",
" \"Dat xay dung\": \"6\",\n",
" \"Rung\": \"7\"\n",
"}\n",
"\n",
"# chia tập dữ liệu train, val, test\n",
"X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "a2fcf472-aff0-44ea-a62a-c308b6e56638",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters: {'classifier__criterion': 'gini', 'classifier__max_depth': 15, 'classifier__n_estimators': 500}\n",
"Accuracy: 88.0 %\n"
]
}
],
"source": [
"# Huấn luyện mô hình\n",
"grid_search = train_with_rf(X_train, X_val, y_train, y_val)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "a25e21e5-d5b0-43b5-a2f5-3ce04eee060d",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy for test data 92.0 %\n"
]
}
],
"source": [
"# kiểm tra độ chính xác với tập test\n",
"y_pred_test = grid_search.predict(X_test)\n",
"test_accuracy = accuracy_score(y_test, y_pred_test)\n",
"print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "06b92a29-0784-4143-a835-f4a1cb21390f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done!\n"
]
}
],
"source": [
"# Lưu mô hình huấn luyện\n",
"save_model(\"model_new.joblib\", grid_search)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "cbf5d0a8-f762-4277-920d-85d31caf2abf",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# đóng client, cluster\n",
"client.close()\n",
"cluster.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "72eaa4ed-5453-427c-965d-2a6e3a84e2bb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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