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296a29bfba88ade97befb0be8d9908b2bbdb9bf0
CSIROBoeingPhase5-Vietnam/01.train_ODC.ipynb
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nkdiemgithub 296a29bfba update label mapping and accuracy
2024-10-22 07:46:38 +00:00

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" element.rel = \"stylesheet\";\n",
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"\n",
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" register_renderer(events, OutputArea);\n",
" }\n",
" } catch(err) {\n",
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],
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else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var 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window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if 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},
"metadata": {},
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{
"data": {
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" var url = js_exports[name];\n",
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"\t on_load();\n",
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"\t}\n",
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"%%time\n",
"%matplotlib inline\n",
"\n",
"import importlib\n",
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"text": [
"An existing cluster was found. Connecting to: easihub.5da29079abf24b57a75baf959ffd2d88\n",
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" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
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"source": [
"%%time\n",
"# Cấu hình Daskgateway\n",
"cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n",
"# Khai báo 1 Datacube là dc\n",
"dc = datacube.Datacube()\n",
"\n",
"# Cấu hình truy cập dịch vụ S3\n",
"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
"\n",
"client"
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"source": [
"## cấu hình thời gian lấy ảnh và tọa độ\n",
"date_range = (\"2022-09-01\", \"2023-10-01\")\n",
"longtitude_range = (105.5, 106.4)\n",
"latitude_range = (9.2, 10.0)\n",
"\n",
"coordinates = (longtitude_range, latitude_range)"
]
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"execution_count": 4,
"id": "6b90c49b-0665-4478-a23b-d111ef88eb79",
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"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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" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 119GB\n",
"Dimensions: (time: 151, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
" * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 4B 32648\n",
"Data variables:\n",
" red (time, y, x) float32 53GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" nir (time, y, x) float32 53GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" scl (time, y, x) uint8 13GB 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-532de703-b597-44ca-8022-483d395445af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-532de703-b597-44ca-8022-483d395445af' 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-234ea933-9d45-40e8-bb93-1510fadcb3d7' class='xr-section-summary-in' type='checkbox' checked><label for='section-234ea933-9d45-40e8-bb93-1510fadcb3d7' 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-bc453e7a-c72e-4969-ac1c-8a9b56c9c6ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc453e7a-c72e-4969-ac1c-8a9b56c9c6ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127a526a-c7db-41d8-b8e0-646128f317a7' class='xr-var-data-in' type='checkbox'><label for='data-127a526a-c7db-41d8-b8e0-646128f317a7' 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",
" &#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",
" &#x27;2023-01-12T03:25:12.066000000&#x27;, &#x27;2023-01-15T03:35:08.140000000&#x27;,\n",
" &#x27;2023-01-17T03:25:12.454000000&#x27;, &#x27;2023-01-20T03:35:06.593000000&#x27;,\n",
" &#x27;2023-01-22T03:25:14.446000000&#x27;, &#x27;2023-01-25T03:35:09.824000000&#x27;,\n",
" &#x27;2023-01-27T03:25:13.987000000&#x27;, &#x27;2023-01-30T03:35:09.963000000&#x27;,\n",
" &#x27;2023-02-01T03:25:13.595000000&#x27;, &#x27;2023-02-04T03:35:09.008000000&#x27;,\n",
" &#x27;2023-02-06T03:25:15.471000000&#x27;, &#x27;2023-02-09T03:35:10.646000000&#x27;,\n",
" &#x27;2023-02-11T03:25:15.100000000&#x27;, &#x27;2023-02-14T03:35:10.240000000&#x27;,\n",
" &#x27;2023-02-16T03:25:28.099000000&#x27;, &#x27;2023-02-24T03:35:12.118000000&#x27;,\n",
" &#x27;2023-02-26T03:25:14.146000000&#x27;, &#x27;2023-03-01T03:35:09.182000000&#x27;,\n",
" &#x27;2023-03-03T03:25:17.841000000&#x27;, &#x27;2023-03-06T03:35:14.717000000&#x27;,\n",
" &#x27;2023-03-08T03:25:12.032000000&#x27;, &#x27;2023-03-11T03:35:07.230000000&#x27;,\n",
" &#x27;2023-03-13T03:25:20.837000000&#x27;, &#x27;2023-03-16T03:35:15.940000000&#x27;,\n",
" &#x27;2023-03-18T03:25:14.467000000&#x27;, &#x27;2023-03-21T03:35:10.279000000&#x27;,\n",
" &#x27;2023-03-23T03:25:18.795000000&#x27;, &#x27;2023-03-26T03:35:14.570000000&#x27;,\n",
" &#x27;2023-03-28T03:25:14.536000000&#x27;, &#x27;2023-03-31T03:35:10.140000000&#x27;,\n",
" &#x27;2023-04-02T03:25:19.945000000&#x27;, &#x27;2023-04-05T03:35:14.925000000&#x27;,\n",
" &#x27;2023-04-07T03:25:16.518000000&#x27;, &#x27;2023-04-10T03:35:12.000000000&#x27;,\n",
" &#x27;2023-04-15T03:35:12.743000000&#x27;, &#x27;2023-04-20T03:35:10.328000000&#x27;,\n",
" &#x27;2023-04-22T03:25:18.572000000&#x27;, &#x27;2023-04-25T03:35:13.748000000&#x27;,\n",
" &#x27;2023-04-27T03:25:16.567000000&#x27;, &#x27;2023-04-30T03:35:12.151000000&#x27;,\n",
" &#x27;2023-05-02T03:25:18.866000000&#x27;, &#x27;2023-05-05T03:35:14.433000000&#x27;,\n",
" &#x27;2023-05-07T03:25:16.583000000&#x27;, &#x27;2023-05-10T03:35:11.284000000&#x27;,\n",
" &#x27;2023-05-12T03:25:19.200000000&#x27;, &#x27;2023-05-15T03:35:15.683000000&#x27;,\n",
" &#x27;2023-05-17T03:25:17.911000000&#x27;, &#x27;2023-05-20T03:35:14.193000000&#x27;,\n",
" &#x27;2023-05-22T03:25:36.378000000&#x27;, &#x27;2023-05-25T03:35:16.657000000&#x27;,\n",
" &#x27;2023-05-27T03:25:20.078000000&#x27;, &#x27;2023-05-30T03:35:15.642000000&#x27;,\n",
" &#x27;2023-06-01T03:25:20.268000000&#x27;, &#x27;2023-06-04T03:35:16.047000000&#x27;,\n",
" &#x27;2023-06-06T03:25:20.180000000&#x27;, &#x27;2023-06-09T03:35:15.534000000&#x27;,\n",
" &#x27;2023-06-11T03:25:21.573000000&#x27;, &#x27;2023-06-14T03:35:16.859000000&#x27;,\n",
" &#x27;2023-06-16T03:25:20.769000000&#x27;, &#x27;2023-06-19T03:35:15.908000000&#x27;,\n",
" &#x27;2023-06-21T03:25:20.640000000&#x27;, &#x27;2023-06-24T03:35:15.972000000&#x27;,\n",
" &#x27;2023-06-26T03:25:19.927000000&#x27;, &#x27;2023-06-29T03:35:15.752000000&#x27;,\n",
" &#x27;2023-07-01T03:25:21.763000000&#x27;, &#x27;2023-07-04T03:35:17.227000000&#x27;,\n",
" &#x27;2023-07-06T03:25:21.702000000&#x27;, &#x27;2023-07-09T03:35:17.102000000&#x27;,\n",
" &#x27;2023-07-11T03:25:21.515000000&#x27;, &#x27;2023-07-14T03:35:16.582000000&#x27;,\n",
" &#x27;2023-07-16T03:25:21.326000000&#x27;, &#x27;2023-07-19T03:35:16.050000000&#x27;,\n",
" &#x27;2023-07-21T03:25:22.444000000&#x27;, &#x27;2023-07-24T03:35:17.845000000&#x27;,\n",
" &#x27;2023-07-26T03:25:21.503000000&#x27;, &#x27;2023-07-29T03:35:17.083000000&#x27;,\n",
" &#x27;2023-07-31T03:25:36.753000000&#x27;, &#x27;2023-08-03T03:35:16.494000000&#x27;,\n",
" &#x27;2023-08-05T03:25:21.570000000&#x27;, &#x27;2023-08-08T03:35:16.313000000&#x27;,\n",
" &#x27;2023-08-10T03:25:22.024000000&#x27;, &#x27;2023-08-13T03:35:17.448000000&#x27;,\n",
" &#x27;2023-08-15T03:25:22.075000000&#x27;, &#x27;2023-08-18T03:35:17.812000000&#x27;,\n",
" &#x27;2023-08-20T03:25:21.568000000&#x27;, &#x27;2023-08-23T03:35:16.224000000&#x27;,\n",
" &#x27;2023-08-25T03:25:22.592000000&#x27;, &#x27;2023-08-28T03:35:17.571000000&#x27;,\n",
" &#x27;2023-08-30T03:25:21.212000000&#x27;, &#x27;2023-09-02T03:35:16.627000000&#x27;,\n",
" &#x27;2023-09-04T03:25:21.856000000&#x27;, &#x27;2023-09-07T03:35:17.134000000&#x27;,\n",
" &#x27;2023-09-09T03:25:20.648000000&#x27;, &#x27;2023-09-12T03:35:15.326000000&#x27;,\n",
" &#x27;2023-09-14T03:25:35.755000000&#x27;, &#x27;2023-09-17T03:35:15.269000000&#x27;,\n",
" &#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-debbf210-d984-467b-b87c-ab4a4dfc6046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-debbf210-d984-467b-b87c-ab4a4dfc6046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-011a6490-bf2d-49fd-8ac3-2dedf79110c2' class='xr-var-data-in' type='checkbox'><label for='data-011a6490-bf2d-49fd-8ac3-2dedf79110c2' 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-4612fe71-2c49-45be-b7cd-2b368b04eadd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4612fe71-2c49-45be-b7cd-2b368b04eadd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df34b2a6-7d59-45bb-9262-8cecc2473a73' class='xr-var-data-in' type='checkbox'><label for='data-df34b2a6-7d59-45bb-9262-8cecc2473a73' 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-bf886095-b229-49c6-911d-379ef6d46ddb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf886095-b229-49c6-911d-379ef6d46ddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-886ef4dc-b048-478b-81f8-66bef46f93ee' class='xr-var-data-in' type='checkbox'><label for='data-886ef4dc-b048-478b-81f8-66bef46f93ee' 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-var-data'><pre>array(32648, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-93f5f719-aa69-4eeb-ba5c-769072548432' class='xr-section-summary-in' type='checkbox' checked><label for='section-93f5f719-aa69-4eeb-ba5c-769072548432' class='xr-section-summary' >Data variables: <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-var-name'><span>red</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</div><input id='attrs-cc1c4ce3-9d08-4617-b6fb-a53731cb124c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc1c4ce3-9d08-4617-b6fb-a53731cb124c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7d18290-9de2-436f-a2bc-522e679edadb' class='xr-var-data-in' type='checkbox'><label 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" \n",
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" &#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-05049d36-e5ee-4b0c-bb92-ff32306e09b5' class='xr-index-data-in' type='checkbox'/><label for='index-05049d36-e5ee-4b0c-bb92-ff32306e09b5' 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(Float64Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
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" 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-720cddb6-1e56-48aa-b13c-b53fd6675615' class='xr-index-data-in' type='checkbox'/><label for='index-720cddb6-1e56-48aa-b13c-b53fd6675615' 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(Float64Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0,\n",
" 554855.0, 554865.0, 554875.0, 554885.0,\n",
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" 653775.0, 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-5467aa8f-c682-42cf-b88c-38433fd55e44' class='xr-section-summary-in' type='checkbox' checked><label for='section-5467aa8f-c682-42cf-b88c-38433fd55e44' 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> Size: 119GB\n",
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" spatial_ref int32 4B 32648\n",
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"Attributes:\n",
" crs: EPSG:32648\n",
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]
},
"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": 5,
"id": "2f6938b6-82e2-4916-bc1d-719c169e25e4",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" bits \\\n",
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{
"data": {
"text/plain": [
"{'0': 'no data',\n",
" '1': 'saturated or defective',\n",
" '2': 'dark area pixels',\n",
" '3': 'cloud shadows',\n",
" '4': 'vegetation',\n",
" '5': 'bare soils',\n",
" '6': 'water',\n",
" '7': 'unclassified',\n",
" '8': 'cloud medium probability',\n",
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" '11': 'snow or ice'}"
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{
"name": "stdout",
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"text": [
"CPU times: user 1.56 s, sys: 40.3 ms, total: 1.6 s\n",
"Wall time: 1.59 s\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6cb6d83d6dd14fc182a817559af7b71f",
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"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%time\n",
"# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n",
"result = mask_clean(data)\n",
"progress(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
"metadata": {
"tags": []
},
"outputs": [],
"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": null,
"id": "84992d28-8e3f-468e-be08-ded511f2c662",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## Hiển thị ảnh NDVI chưa fill nan\n",
"plt.imshow(ndvi.isel(time=6))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "72318b60-532a-4f08-a5f7-94762d08a42c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n",
"time_split = [\n",
" slice(\"2022-09-01\", \"2023-01-01\"),\n",
" slice(\"2023-01-01\", \"2023-05-01\"),\n",
" slice(\"2023-05-01\", \"2023-07-01\"),\n",
" slice(\"2023-07-01\", \"2023-10-01\"),\n",
"]\n",
"\n",
"# Điền mây ở các vị trí mang giá trị nan (fill nan)\n",
"fill_nan_ndvi = fill_nan(ndvi, time_split)\n",
"\n",
"# In kết quả ảnh ndvi đã điền mây (đã fill nan)\n",
"plt.imshow(fill_nan_ndvi.isel(time=6))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%time\n",
"## tính ndvi theo tháng\n",
"average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n",
"progress(average_ndvi)\n",
"\n",
"# compute average_ndvi\n",
"average_ndvi = average_ndvi.compute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#Load dữ liệu ảnh Sentinel 1\n",
"dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n",
"average_vv = calculate_average(dsvv, time_pattern='1M')\n",
"average_vh = calculate_average(dsvh, time_pattern='1M')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n",
"train_path = \"train/ST_training data_updated_1130points_new.shp\" # đường dẫn shp file train\n",
"\n",
"## load dữ liệu điểm huấn luyện mô hình (train file)\n",
"train = load_train_data(train_path)\n",
"train.head()\n",
"\n",
"# 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",
"# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n",
"datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n",
"\n",
"# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n",
"X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n",
" train, label_mapping, datasets\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e955884-d4af-422d-a8e6-d436199540e0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Huấn luyện mô hình\n",
"grid_search = train_with_rf(X_train, X_val, y_train, y_val)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f",
"metadata": {
"tags": []
},
"outputs": [],
"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": null,
"id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Lưu mô hình huấn luyện\n",
"save_model(\"model_odc.joblib\", grid_search)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33dd516d-9824-499e-96b9-5cd9224c194c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# đóng client, cluster\n",
"client.close()\n",
"cluster.close()"
]
}
],
"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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