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remote-sensing/01.train_ODC.ipynb
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Victor Phan d5f9003dc9 result
2026-03-07 17:14:00 +07:00

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" var timer = setInterval(function(root) {\n",
" if (is_loaded(root)) {\n",
" clearInterval(timer);\n",
" embed_document(root);\n",
" } else if (document.readyState == \"complete\") {\n",
" attempts++;\n",
" if (attempts > 200) {\n",
" clearInterval(timer);\n",
"\t var Bokeh = get_bokeh(root)\n",
"\t if (Bokeh == null || Bokeh.Panel == null) {\n",
" console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
"\t } else {\n",
"\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
"\t embed_document(root)\n",
"\t }\n",
" }\n",
" }\n",
" }, 25, root)\n",
" }\n",
"})(window);</script>"
]
},
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "p1002"
}
},
"output_type": "display_data"
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{
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" font-family: var(--jp-ui-font-family);\n",
" font-size: var(--jp-ui-font-size1);\n",
" color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
"}\n",
"\n",
"/* Override VSCode background color */\n",
".cell-output-ipywidget-background:has(\n",
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".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
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"}\n",
"</style>"
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" const Bokeh = root.Bokeh;\n",
"\n",
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" if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n",
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" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
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" }\n",
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" element.textContent = `\n",
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" window._bokeh_on_load()\n",
" `\n",
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" on_load()\n",
" }\n",
" };\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n",
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" const css_urls = [];\n",
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" Bokeh.set_log_level(\"info\");\n",
" },\n",
"function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
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" }\n",
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" root._bokeh_is_initializing = false;\n",
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" root._bokeh_is_loading = 0\n",
" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
" load_or_wait();\n",
" } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
" setTimeout(load_or_wait, 100);\n",
" } else {\n",
" root._bokeh_is_initializing = true\n",
" root._bokeh_onload_callbacks = []\n",
" const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
" if (!reloading && !bokeh_loaded) {\n",
" if (root.Bokeh) {\n",
" root.Bokeh = undefined;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" }\n",
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" run_inline_js();\n",
" });\n",
" }\n",
" }\n",
" // Give older versions of the autoload script a head-start to ensure\n",
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" setTimeout(load_or_wait, 100)\n",
"}(window));"
],
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i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n 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 for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } 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 }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));"
},
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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 (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n"
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"*[data-root-id] > * {\n",
" box-sizing: border-box;\n",
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"\n",
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".cell-output-ipywidget-background:has(\n",
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" ),\n",
".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
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"</style>"
]
},
"metadata": {},
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" }\n",
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" const reloading = true;\n",
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" callback();\n",
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" delete root._bokeh_onload_callbacks;\n",
" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
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"\n",
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" } else {\n",
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" }\n",
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" element.href = url;\n",
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" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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" }\n",
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" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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" window.${name} = ${name}\n",
" window._bokeh_on_load()\n",
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" element.appendChild(document.createTextNode(css));\n",
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" }\n",
"\n",
" const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n",
" const js_modules = [];\n",
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" const css_urls = [];\n",
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" Bokeh.set_log_level(\"info\");\n",
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"function(Bokeh) {} // ensure no trailing comma for IE\n",
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" Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
" }\n",
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" }\n",
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" }\n",
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" function load_or_wait() {\n",
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" // before older versions are fully initialized.\n",
" if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
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" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_is_initializing = false;\n",
" root._bokeh_onload_callbacks = undefined;\n",
" root._bokeh_is_loading = 0\n",
" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
" load_or_wait();\n",
" } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
" setTimeout(load_or_wait, 100);\n",
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" root._bokeh_is_initializing = true\n",
" root._bokeh_onload_callbacks = []\n",
" const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
" if (!reloading && !bokeh_loaded) {\n",
" if (root.Bokeh) {\n",
" root.Bokeh = undefined;\n",
" }\n",
" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" }\n",
" load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }\n",
" // Give older versions of the autoload script a head-start to ensure\n",
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"}(window));"
],
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},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ All modules loaded successfully\n",
"CPU times: user 19.6 s, sys: 3.75 s, total: 23.4 s\n",
"Wall time: 14.9 s\n"
]
}
],
"source": [
"%%time\n",
"%matplotlib inline\n",
"\n",
"import importlib\n",
"import new_import_ODC \n",
"\n",
"importlib.reload(new_import_ODC)\n",
"\n",
"from new_import_ODC import *\n",
"\n",
"print(\"✅ All modules loaded successfully\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d824dc4f-994b-4d1c-8d24-ce6674da141c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ AWS credentials loaded from environment variables\n",
"✅ Dask cluster initialized\n",
" Cluster: LocalCluster(6a0afa31, 'tcp://127.0.0.1:38807', workers=4, threads=40, memory=128.00 GiB)\n",
"✅ S3 access configured (requester_pays=True)\n",
"✅ Datacube connected (metadata only)\n",
"\n",
"======================================================================\n",
"CPU times: user 1.35 s, sys: 101 ms, total: 1.45 s\n",
"Wall time: 6.9 s\n"
]
}
],
"source": [
"%%time\n",
"import os\n",
"import sys\n",
"\n",
"print(\"✅ AWS credentials loaded from environment variables\")\n",
"\n",
"# Cấu hình Dask local\n",
"from dask.distributed import Client, LocalCluster\n",
"\n",
"cluster = LocalCluster(n_workers=4)\n",
"client = Client(cluster)\n",
"print(\"✅ Dask cluster initialized\")\n",
"print(f\" Cluster: {cluster}\")\n",
"\n",
"# Cấu hình S3 access cho rasterio/GDAL (bắt buộc để đọc COGs từ S3)\n",
"configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n",
"print(\"✅ S3 access configured (requester_pays=True)\")\n",
"\n",
"# Khai báo Datacube (chỉ để lấy metadata, không dùng load())\n",
"import datacube\n",
"try:\n",
" dc = datacube.Datacube()\n",
" print(\"✅ Datacube connected (metadata only)\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Datacube connection not critical: {e}\")\n",
" dc = None\n",
"\n",
"print(\"\\n\" + \"=\"*70)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1e113730",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"GETTING SENTINEL-2 SCENE METADATA\n",
"======================================================================\n",
"\n",
"[1] Loading metadata from datacube...\n",
" ✅ Found 180337 scenes\n",
"\n",
"[2] Selected scene: S2B_56MNC_20230525_0_L2A\n",
" Date: 2023-05-25 00:17:32.223000+00:00\n",
"\n",
"[3] Available bands:\n",
" - aot: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - nir: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - red: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - scl: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - wvp: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - blue: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - green: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - nir08: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - nir09: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - swir16: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - swir22: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - coastal: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - rededge1: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - rededge2: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
" - rededge3: s3://sentinel-cogs/sentinel-s2-l2a-cogs/56/M/NC/2023/5/S2B_56MNC_20230525_0_L2A/\n",
"======================================================================\n",
"CPU times: user 1min 38s, sys: 15.4 s, total: 1min 54s\n",
"Wall time: 5min 38s\n"
]
}
],
"source": [
"%%time\n",
"# 🔧 Get Sentinel-2 scene metadata from datacube\n",
"print(\"=\"*70)\n",
"print(\"GETTING SENTINEL-2 SCENE METADATA\")\n",
"print(\"=\"*70)\n",
"\n",
"date_range = (\"2023-03-01\", \"2023-12-31\")\n",
"longtitude_range = (105.5, 106.4)\n",
"latitude_range = (9.2, 10.0)\n",
"\n",
"try:\n",
" print(f\"\\n[1] Loading metadata from datacube...\")\n",
" datasets = list(dc.find_datasets(product='s2_l2a', time=date_range))\n",
" print(f\" ✅ Found {len(datasets)} scenes\")\n",
" \n",
" if datasets:\n",
" selected = datasets[0]\n",
" print(f\"\\n[2] Selected scene: {selected.metadata.label}\")\n",
" scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
" print(f\" Date: {scene_datetime}\")\n",
" \n",
" # Display measurement paths\n",
" print(f\"\\n[3] Available bands:\")\n",
" for name, measurement in selected.measurements.items():\n",
" print(f\" - {name}: {measurement['path'][:80]}\")\n",
" \n",
"except Exception as e:\n",
" print(f\"❌ Error: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
"\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3cd69645",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"CHECKING FOR CACHED DATASET\n",
"======================================================================\n",
"\n",
"⏳ Cache file not found: dataset_cache/sentinel2_timeseries_40scenes.nc\n",
" Will download from S3 and save cache\n",
" (Next run will use cache automatically)\n",
"======================================================================\n",
"CPU times: user 1.37 ms, sys: 0 ns, total: 1.37 ms\n",
"Wall time: 13.7 ms\n"
]
}
],
"source": [
"%%time\n",
"# 🔍 CHECK IF DATASET CACHE EXISTS (Skip download if available)\n",
"print(\"=\"*70)\n",
"print(\"CHECKING FOR CACHED DATASET\")\n",
"print(\"=\"*70)\n",
"\n",
"import os\n",
"import xarray as xr\n",
"\n",
"cache_dir = \"dataset_cache\"\n",
"cache_file = f\"{cache_dir}/sentinel2_timeseries_40scenes.nc\"\n",
"\n",
"use_cache = False\n",
"\n",
"if os.path.exists(cache_file):\n",
" print(f\"\\n✅ Cache file found: {cache_file}\")\n",
" \n",
" # Get file info\n",
" file_size_gb = os.path.getsize(cache_file) / (1024**3)\n",
" print(f\" File size: {file_size_gb:.2f} GB\")\n",
" \n",
" # Try to load\n",
" try:\n",
" print(f\"\\n🔄 Loading dataset from cache...\")\n",
" data = xr.open_dataset(cache_file)\n",
" \n",
" print(f\"✅ Dataset loaded from cache!\")\n",
" print(f\" Total scenes: {len(data['time'])}\")\n",
" print(f\" Variables: {len(data.data_vars)}\")\n",
" print(f\" Dimensions: {dict(data.dims)}\")\n",
" print(f\"\\n ⏭️ Skipping S3 download (using cached data)\")\n",
" \n",
" use_cache = True\n",
" \n",
" except Exception as e:\n",
" print(f\"❌ Error loading cache: {e}\")\n",
" print(f\" Will download fresh data from S3\")\n",
" use_cache = False\n",
"else:\n",
" print(f\"\\n⏳ Cache file not found: {cache_file}\")\n",
" print(f\" Will download from S3 and save cache\")\n",
" print(f\" (Next run will use cache automatically)\")\n",
"\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\n",
"======================================================================\n",
"\n",
"📥 Downloading from S3...\n",
"\n",
"📦 Found 180337 available scenes\n",
" Date range: 2023-03-01 to 2023-12-31\n",
"\n",
"[LOADING] Loading ALL 180337 scenes with ALL available bands...\n",
" (Keeping NATIVE resolution - NO upsampling/magnification)\n",
" Available bands: ['aot', 'nir', 'red', 'scl', 'wvp', 'blue', 'green', 'nir08', 'nir09', 'swir16', 'swir22', 'coastal', 'rededge1', 'rededge2', 'rededge3']\n",
"\n",
" [ 1/1] S2B_56MNC_20230525_0_L2A (2023-05-25)\n",
" ⚠️ Error loading aot: VPC endpoints do not support c\n",
" ⚠️ Error loading nir: VPC endpoints do not support c\n",
" ⚠️ Error loading red: VPC endpoints do not support c\n",
" ⚠️ Error loading scl: VPC endpoints do not support c\n",
" ⚠️ Error loading wvp: VPC endpoints do not support c\n",
" ⚠️ Error loading blue: VPC endpoints do not support c\n",
" ⚠️ Error loading green: VPC endpoints do not support c\n",
" ⚠️ Error loading nir08: VPC endpoints do not support c\n",
" ⚠️ Error loading nir09: VPC endpoints do not support c\n",
" ⚠️ Error loading swir16: VPC endpoints do not support c\n",
" ⚠️ Error loading swir22: VPC endpoints do not support c\n",
" ⚠️ Error loading coastal: VPC endpoints do not support c\n",
" ⚠️ Error loading rededge1: VPC endpoints do not support c\n",
" ⚠️ Error loading rededge2: VPC endpoints do not support c\n",
" ⚠️ Error loading rededge3: VPC endpoints do not support c\n",
"❌ Error: Could not load any bands from any scene\n",
"======================================================================\n",
"CPU times: user 1min 39s, sys: 8.23 s, total: 1min 47s\n",
"Wall time: 2min 15s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<timed exec>\", line 84, in <module>\n",
"ValueError: Could not load any bands from any scene\n"
]
}
],
"source": [
"%%time\n",
"# 💾 LOAD SENTINEL-2 DATA DIRECTLY FROM S3 COGS USING RASTERIO - WITH TEMPORAL FEATURES\n",
"print(\"=\"*70)\n",
"print(\"LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\")\n",
"print(\"=\"*70)\n",
"\n",
"try:\n",
" import rasterio\n",
" import xarray as xr\n",
" import numpy as np\n",
" from scipy import ndimage\n",
" \n",
" # ===== CHECK IF SHOULD SKIP DOWNLOAD =====\n",
" if use_cache and data is not None:\n",
" print(f\"\\n✅ Using cached dataset - skipping download!\")\n",
" print(f\" Variables: {len(data.data_vars)}\")\n",
" print(f\" Shape: {data.dims}\")\n",
" display(data)\n",
" \n",
" else:\n",
" # ===== DOWNLOAD FROM S3 =====\n",
" print(f\"\\n📥 Downloading from S3...\")\n",
" \n",
" # Get all scenes from datacube metadata\n",
" datasets = list(dc.find_datasets(\n",
" product='s2_l2a',\n",
" time=date_range\n",
" ))\n",
" \n",
" if not datasets:\n",
" raise ValueError(\"No datasets found for date range\")\n",
" \n",
" print(f\"\\n📦 Found {len(datasets)} available scenes\")\n",
" print(f\" Date range: {date_range[0]} to {date_range[1]}\")\n",
" \n",
" # ===== LOAD ALL SCENES WITH ALL AVAILABLE BANDS (NO MAGNIFICATION) =====\n",
" print(f\"\\n[LOADING] Loading ALL {len(datasets)} scenes with ALL available bands...\")\n",
" print(f\" (Keeping NATIVE resolution - NO upsampling/magnification)\")\n",
" \n",
" # num_scenes = len(datasets) # Load ALL scenes\n",
" num_scenes = 1 # Load ALL scenes\n",
" all_data_dict = {}\n",
" failed_scenes = []\n",
" scene_dates = []\n",
" \n",
" # Discover all available bands from first scene\n",
" first_scene = datasets[0]\n",
" all_available_bands = list(first_scene.measurements.keys())\n",
" print(f\" Available bands: {all_available_bands}\")\n",
" \n",
" for scene_idx in range(num_scenes):\n",
" selected = datasets[scene_idx]\n",
" scene_label = selected.metadata.label\n",
" scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
" scene_dates.append(scene_datetime)\n",
" \n",
" # Print progress every 5 scenes\n",
" if scene_idx % 5 == 0 or scene_idx == 0 or scene_idx == num_scenes - 1:\n",
" print(f\"\\n [{scene_idx + 1:2d}/{num_scenes}] {scene_label} ({scene_datetime.date()})\")\n",
" \n",
" # Load ALL available bands from S3 COGs\n",
" scene_data_dict = {}\n",
" \n",
" for band_name in all_available_bands:\n",
" if band_name in selected.measurements:\n",
" band_path = selected.measurements[band_name]['path']\n",
" \n",
" try:\n",
" with rasterio.open(band_path) as src:\n",
" data_band = src.read(1)\n",
" scene_data_dict[band_name] = data_band\n",
" except Exception as e:\n",
" if scene_idx % 5 == 0:\n",
" print(f\" ⚠️ Error loading {band_name}: {str(e)[:30]}\")\n",
" failed_scenes.append((scene_idx, scene_label, band_name, str(e)))\n",
" \n",
" if scene_data_dict:\n",
" all_data_dict[scene_idx] = scene_data_dict\n",
" if scene_idx % 5 == 0 or scene_idx == num_scenes - 1:\n",
" print(f\" ✅ {len(scene_data_dict)} bands loaded\")\n",
" else:\n",
" failed_scenes.append((scene_idx, scene_label, \"all\", \"No bands loaded\"))\n",
" \n",
" if not all_data_dict:\n",
" raise ValueError(\"Could not load any bands from any scene\")\n",
" \n",
" print(f\"\\n✅ Successfully loaded {len(all_data_dict)} scenes!\")\n",
" if failed_scenes:\n",
" print(f\"⚠️ Failed to load {len(failed_scenes)} band instances (will be skipped)\")\n",
" \n",
" # ===== NORMALIZE RESOLUTION (No upsampling - just match to highest) =====\n",
" print(f\"\\n[RESOLUTION NORMALIZATION] Aligning all bands to native resolution (NO magnification)...\")\n",
" \n",
" # Find max resolution\n",
" ref_resolution = None\n",
" max_size = 0\n",
" max_band = None\n",
" \n",
" for scene_idx in all_data_dict.keys():\n",
" for band_name, data_band in all_data_dict[scene_idx].items():\n",
" size = data_band.shape[0]\n",
" if size > max_size:\n",
" max_size = size\n",
" ref_resolution = size\n",
" max_band = band_name\n",
" \n",
" print(f\" Reference resolution: {max_size}×{max_size} pixels (native {max_band})\")\n",
" \n",
" # Resample all bands to match reference resolution (both up and down)\n",
" resampled_count = 0\n",
" for scene_idx in all_data_dict.keys():\n",
" for band_name in list(all_data_dict[scene_idx].keys()):\n",
" band_data_arr = all_data_dict[scene_idx][band_name]\n",
" current_size = band_data_arr.shape[0]\n",
" \n",
" if current_size != ref_resolution:\n",
" scale_factor = ref_resolution / current_size\n",
" \n",
" # Resample to match reference resolution (both up and down)\n",
" if band_name == 'scl':\n",
" resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=0)\n",
" else:\n",
" resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=1)\n",
" \n",
" all_data_dict[scene_idx][band_name] = resampled_data\n",
" new_size = resampled_data.shape[0]\n",
" if scene_idx == 0: # Print for first scene only to reduce clutter\n",
" print(f\" Resampling {band_name}: {current_size}×{current_size} → {new_size}×{new_size}\")\n",
" resampled_count += 1\n",
" \n",
" print(f\"✅ Resolution normalization complete! ({resampled_count} bands resampled)\")\n",
" \n",
" # ===== CALCULATE SPECTRAL INDICES FOR EACH SCENE =====\n",
" print(f\"\\n[SPECTRAL INDICES] Calculating spectral indices for each scene...\")\n",
" \n",
" indices_count = 0\n",
" for scene_idx in all_data_dict.keys():\n",
" scene_data = all_data_dict[scene_idx]\n",
" \n",
" try:\n",
" # NDVI: (NIR - Red) / (NIR + Red)\n",
" if 'nir' in scene_data and 'red' in scene_data:\n",
" nir = scene_data['nir'].astype(float)\n",
" red = scene_data['red'].astype(float)\n",
" ndvi = (nir - red) / (nir + red + 1e-8)\n",
" scene_data['ndvi'] = ndvi.astype(np.float32)\n",
" indices_count += 1\n",
" \n",
" # NDBI: (SWIR - NIR) / (SWIR + NIR)\n",
" if 'b11' in scene_data and 'nir' in scene_data:\n",
" swir = scene_data['b11'].astype(float)\n",
" nir = scene_data['nir'].astype(float)\n",
" ndbi = (swir - nir) / (swir + nir + 1e-8)\n",
" scene_data['ndbi'] = ndbi.astype(np.float32)\n",
" indices_count += 1\n",
" \n",
" # NDWI: (NIR - SWIR) / (NIR + SWIR)\n",
" if 'nir' in scene_data and 'b11' in scene_data:\n",
" nir = scene_data['nir'].astype(float)\n",
" swir = scene_data['b11'].astype(float)\n",
" ndwi = (nir - swir) / (nir + swir + 1e-8)\n",
" scene_data['ndwi'] = ndwi.astype(np.float32)\n",
" indices_count += 1\n",
" \n",
" # EVI: Enhanced Vegetation Index\n",
" if 'nir' in scene_data and 'red' in scene_data and 'blue' in scene_data:\n",
" nir = scene_data['nir'].astype(float)\n",
" red = scene_data['red'].astype(float)\n",
" blue = scene_data['blue'].astype(float)\n",
" evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1)\n",
" scene_data['evi'] = evi.astype(np.float32)\n",
" indices_count += 1\n",
" \n",
" except Exception as e:\n",
" pass\n",
" \n",
" print(f\"✅ Calculated {indices_count} spectral indices per scene\")\n",
" \n",
" # ===== STACK SCENES ALONG TIME DIMENSION =====\n",
" print(f\"\\n[STACKING] Stacking all {len(all_data_dict)} scenes to create time-series...\")\n",
" \n",
" data_vars = {}\n",
" band_names = list(all_data_dict[0].keys())\n",
" \n",
" for band_name in band_names:\n",
" band_data_list = []\n",
" for scene_idx in sorted(all_data_dict.keys()):\n",
" if band_name in all_data_dict[scene_idx]:\n",
" band_data_list.append(all_data_dict[scene_idx][band_name])\n",
" \n",
" if band_data_list:\n",
" stacked = np.stack(band_data_list, axis=0)\n",
" data_vars[band_name] = (['time', 'y', 'x'], stacked)\n",
" \n",
" # Create xarray Dataset with time dimension\n",
" first_band_data = list(all_data_dict[0].values())[0]\n",
" y_size, x_size = first_band_data.shape\n",
" \n",
" data = xr.Dataset(\n",
" data_vars,\n",
" coords={\n",
" 'time': np.arange(len(all_data_dict)),\n",
" 'x': np.arange(x_size),\n",
" 'y': np.arange(y_size)\n",
" }\n",
" )\n",
" \n",
" # ===== CALCULATE TEMPORAL FEATURES FOR ACCURACY =====\n",
" print(f\"\\n[TEMPORAL FEATURES] Computing temporal features from time-series...\")\n",
" \n",
" temporal_features_added = 0\n",
" \n",
" # For NDVI: temporal statistics\n",
" if 'ndvi' in data.data_vars:\n",
" ndvi_ts = data['ndvi']\n",
" \n",
" # Min NDVI (vegetation stress indicator)\n",
" data['ndvi_min'] = ndvi_ts.min(dim='time')\n",
" temporal_features_added += 1\n",
" \n",
" # Max NDVI (peak vegetation)\n",
" data['ndvi_max'] = ndvi_ts.max(dim='time')\n",
" temporal_features_added += 1\n",
" \n",
" # Mean NDVI\n",
" data['ndvi_mean'] = ndvi_ts.mean(dim='time')\n",
" temporal_features_added += 1\n",
" \n",
" # NDVI range (variability)\n",
" data['ndvi_range'] = data['ndvi_max'] - data['ndvi_min']\n",
" temporal_features_added += 1\n",
" \n",
" # NDVI std (temporal consistency)\n",
" data['ndvi_std'] = ndvi_ts.std(dim='time')\n",
" temporal_features_added += 1\n",
" \n",
" # For all indices: mean values (aggregate features)\n",
" for band_name in ['ndbi', 'ndwi', 'evi']:\n",
" if band_name in data.data_vars:\n",
" band_ts = data[band_name]\n",
" data[f'{band_name}_mean'] = band_ts.mean(dim='time')\n",
" temporal_features_added += 1\n",
" \n",
" print(f\"✅ Added {temporal_features_added} temporal/aggregate features\")\n",
" \n",
" # ===== SAVE TO CACHE =====\n",
" print(f\"\\n[CACHE] Saving dataset to cache...\")\n",
" try:\n",
" data.to_netcdf(cache_file, engine='netcdf4')\n",
" cache_size = os.path.getsize(cache_file) / (1024**3)\n",
" print(f\"✅ Dataset saved to cache: {cache_file}\")\n",
" print(f\" Cache size: {cache_size:.2f} GB\")\n",
" except Exception as e:\n",
" print(f\"⚠️ Error saving cache: {e}\")\n",
" \n",
" print(f\"\\n✅ OPTIMAL Dataset with native resolution + temporal features created!\")\n",
" print(f\" {'='*70}\")\n",
" print(f\" 🎬 Total scenes (time steps): {len(all_data_dict)}\")\n",
" print(f\" 📊 Total bands/variables: {len(data.data_vars)}\")\n",
" print(f\" 🖼️ Spatial size: {x_size} × {y_size} pixels (NATIVE resolution)\")\n",
" print(f\" 📏 Native resolution: 10m (Sentinel-2 L2A)\")\n",
" print(f\" ⏰ Temporal range: {scene_dates[0].date()} to {scene_dates[-1].date()}\")\n",
" print(f\" 💾 Total dataset size: {notebook_utils.xarray_object_size(data)}\")\n",
" print(f\" 💿 Cached at: {cache_file}\")\n",
" print(f\" {'='*70}\")\n",
" \n",
" print(f\"\\n Dataset dimensions:\")\n",
" for dim, size in data.dims.items():\n",
" print(f\" {dim}: {size}\")\n",
" \n",
" print(f\"\\n Variables ({len(data.data_vars)}):\")\n",
" spatial_vars = []\n",
" temporal_vars = []\n",
" for var_name in sorted(data.data_vars):\n",
" if len(data[var_name].shape) == 3:\n",
" spatial_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
" else:\n",
" temporal_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
" \n",
" print(f\" Spatial time-series ({len(spatial_vars)}):\")\n",
" for v in spatial_vars:\n",
" print(f\" - {v}\")\n",
" print(f\" Temporal aggregates ({len(temporal_vars)}):\")\n",
" for v in temporal_vars:\n",
" print(f\" - {v}\")\n",
" \n",
" print(f\" {'='*70}\")\n",
" \n",
" display(data)\n",
" \n",
" # ===== EXTRACT NDVI FOR TRAINING =====\n",
" print(f\"\\n[NDVI EXTRACTION] Extracting NDVI for model training...\")\n",
" if 'ndvi_mean' in data.data_vars:\n",
" # Use mean NDVI across time\n",
" ndvi = data['ndvi_mean']\n",
" print(f\"✅ NDVI extracted (mean across time)\")\n",
" print(f\" Shape: {ndvi.shape}\")\n",
" elif 'ndvi' in data.data_vars:\n",
" # Use first time step if mean not available\n",
" ndvi = data['ndvi'].isel(time=0)\n",
" print(f\"✅ NDVI extracted (first time step)\")\n",
" print(f\" Shape: {ndvi.shape}\")\n",
" else:\n",
" print(f\"❌ NDVI not found in dataset\")\n",
" ndvi = None\n",
" \n",
"except Exception as e:\n",
" print(f\"❌ Error: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
" data = None\n",
" ndvi = None\n",
"\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"TRAINING DATA SETUP\n",
"======================================================================\n",
"\n",
"[1] Loading training data: train/ST_training data_updated_1130points_new.shp\n",
" ❌ Error: train/ST_training data_updated_1130points_new.shp: No such file or directory\n",
"\n",
"[2] Label mapping:\n",
" 0: Lua tom\n",
" 1: Lua\n",
" 2: CHN\n",
" 3: CLN\n",
" 4: TS\n",
" 5: Song\n",
" 6: Dat xay dung\n",
" 7: Rung\n",
"\n",
"======================================================================\n"
]
}
],
"source": [
"# 🎯 LOAD TRAINING DATA & EXTRACT FEATURES\n",
"print(\"=\"*70)\n",
"print(\"TRAINING DATA SETUP\")\n",
"print(\"=\"*70)\n",
"\n",
"# Load training points\n",
"train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
"print(f\"\\n[1] Loading training data: {train_path}\")\n",
"\n",
"try:\n",
" train = load_train_data(train_path)\n",
" print(f\" ✅ Loaded {len(train)} training points\")\n",
" print(f\" Columns: {list(train.columns)}\")\n",
" train.head()\n",
"except Exception as e:\n",
" print(f\" ❌ Error: {e}\")\n",
" train = None\n",
"\n",
"# Label mapping\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",
"print(f\"\\n[2] Label mapping:\")\n",
"for label, code in label_mapping.items():\n",
" print(f\" {code}: {label}\")\n",
"\n",
"print(\"\\n\" + \"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2e955884-d4af-422d-a8e6-d436199540e0",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"LAND USE CLASSIFICATION TRAINING\n",
"======================================================================\n",
"\n",
"🎯 Mục tiêu: Dự đoán phân loại sử dụng đất (8 lớp)\n",
" - NDVI/NDWI/NDBI/EVI là INPUT FEATURES\n",
" - Sau khi predict xong → có thể hiển thị NDVI map như chỉ số phụ\n",
"======================================================================\n",
"❌ Missing training data or satellite data\n",
"\n",
"======================================================================\n",
"📝 NOTE: Model này dự đoán PHÂN LOẠI SỬ DỤNG ĐẤT (8 lớp)\n",
" NDVI là một trong các features đầu vào, không phải mục tiêu dự đoán\n",
" Sau khi predict → có thể hiển thị NDVI map như chỉ số phụ\n",
"======================================================================\n",
"CPU times: user 201 μs, sys: 0 ns, total: 201 μs\n",
"Wall time: 168 μs\n"
]
}
],
"source": [
"%%time\n",
"# 🤖 LAND USE CLASSIFICATION MODEL TRAINING (MỤC TIÊU CHÍNH)\n",
"print(\"=\"*70)\n",
"print(\"LAND USE CLASSIFICATION TRAINING\")\n",
"print(\"=\"*70)\n",
"print(\"\\n🎯 Mục tiêu: Dự đoán phân loại sử dụng đất (8 lớp)\")\n",
"print(\" - NDVI/NDWI/NDBI/EVI là INPUT FEATURES\")\n",
"print(\" - Sau khi predict xong → có thể hiển thị NDVI map như chỉ số phụ\")\n",
"print(\"=\"*70)\n",
"\n",
"if train is not None and data is not None:\n",
" print(\"\\n[1] Extracting MULTIPLE features from satellite data...\")\n",
" print(\" (Sử dụng nhiều spectral indices để cải thiện accuracy)\")\n",
" \n",
" try:\n",
" # Extract features at training point locations\n",
" X = []\n",
" y = []\n",
" \n",
" # Available features from data\n",
" available_features = ['ndvi_mean', 'ndvi_min', 'ndvi_max', 'ndvi_std', 'ndvi_range',\n",
" 'ndwi_mean', 'ndbi_mean', 'evi_mean']\n",
" \n",
" # Check which features are actually available\n",
" features_to_use = [f for f in available_features if f in data.data_vars]\n",
" \n",
" if not features_to_use:\n",
" print(\" ❌ No spectral features found in dataset!\")\n",
" print(\" Available variables:\", list(data.data_vars))\n",
" model = None\n",
" else:\n",
" print(f\" Using {len(features_to_use)} features: {features_to_use}\")\n",
" \n",
" for idx, point in train.iterrows():\n",
" try:\n",
" # Extract all available features at this point\n",
" feature_vec = []\n",
" for feat_name in features_to_use:\n",
" feat_val = float(data[feat_name].sel(\n",
" x=point.geometry.x, \n",
" y=point.geometry.y, \n",
" method='nearest'\n",
" ).values)\n",
" feature_vec.append(feat_val)\n",
" \n",
" # Get label\n",
" label = label_mapping[point.Hientrang]\n",
" \n",
" # Only add if no NaN values\n",
" if not np.isnan(feature_vec).any():\n",
" X.append(feature_vec)\n",
" y.append(int(label))\n",
" except Exception as e:\n",
" # Skip points with errors\n",
" continue\n",
" \n",
" if len(X) > 0:\n",
" X = np.array(X)\n",
" y = np.array(y)\n",
" print(f\" ✅ Extracted {len(X)} samples with {X.shape[1]} features each\")\n",
" \n",
" # Show feature statistics\n",
" print(f\"\\n Feature statistics:\")\n",
" for i, feat_name in enumerate(features_to_use):\n",
" print(f\" {feat_name:15s}: mean={X[:,i].mean():.3f}, std={X[:,i].std():.3f}\")\n",
" \n",
" # Split data\n",
" print(f\"\\n[2] Splitting data (80-20)...\")\n",
" from sklearn.model_selection import train_test_split\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42, stratify=y\n",
" )\n",
" print(f\" Train: {len(X_train)}, Test: {len(X_test)}\")\n",
" \n",
" # Show class distribution\n",
" unique, counts = np.unique(y_train, return_counts=True)\n",
" print(f\"\\n Class distribution in training set:\")\n",
" for cls, count in zip(unique, counts):\n",
" cls_name = [k for k, v in label_mapping.items() if v == str(cls)][0]\n",
" print(f\" {cls}: {cls_name:15s} - {count:4d} samples ({count/len(y_train)*100:.1f}%)\")\n",
" \n",
" # Train model\n",
" print(f\"\\n[3] Training Random Forest for LAND USE CLASSIFICATION...\")\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" from sklearn.metrics import accuracy_score, classification_report\n",
" \n",
" model = RandomForestClassifier(\n",
" n_estimators=200, # More trees for better accuracy\n",
" max_depth=30,\n",
" min_samples_split=5,\n",
" random_state=42,\n",
" n_jobs=-1,\n",
" verbose=1\n",
" )\n",
" model.fit(X_train, y_train)\n",
" \n",
" # Evaluate\n",
" y_pred = model.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" \n",
" print(f\"\\n ✅ Model trained!\")\n",
" print(f\" Training accuracy: {model.score(X_train, y_train)*100:.2f}%\")\n",
" print(f\" Testing accuracy: {accuracy*100:.2f}%\")\n",
" \n",
" # Show feature importance\n",
" print(f\"\\n Feature importance:\")\n",
" importances = model.feature_importances_\n",
" indices = np.argsort(importances)[::-1]\n",
" for i, idx in enumerate(indices):\n",
" print(f\" {i+1}. {features_to_use[idx]:15s}: {importances[idx]:.4f}\")\n",
" \n",
" # Classification report\n",
" print(f\"\\n[4] Classification Report:\")\n",
" class_names = [k for k, v in sorted(label_mapping.items(), key=lambda x: x[1])]\n",
" print(classification_report(y_test, y_pred, target_names=class_names, zero_division=0))\n",
" \n",
" else:\n",
" print(f\" ❌ No samples extracted\")\n",
" model = None\n",
" \n",
" except Exception as e:\n",
" print(f\" ❌ Error: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
" model = None\n",
"else:\n",
" print(\"❌ Missing training data or satellite data\")\n",
" model = None\n",
"\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"📝 NOTE: Model này dự đoán PHÂN LOẠI SỬ DỤNG ĐẤT (8 lớp)\")\n",
"print(\" NDVI là một trong các features đầu vào, không phải mục tiêu dự đoán\")\n",
"print(\" Sau khi predict → có thể hiển thị NDVI map như chỉ số phụ\")\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"MODEL SAVING\n",
"======================================================================\n",
"❌ No model to save\n",
"======================================================================\n"
]
}
],
"source": [
"# 💾 SAVE MODEL WITH METADATA\n",
"print(\"=\"*70)\n",
"print(\"MODEL SAVING\")\n",
"print(\"=\"*70)\n",
"\n",
"if model is not None:\n",
" print(\"\\n🔄 Saving trained LAND USE CLASSIFICATION model with metadata...\")\n",
" try:\n",
" from datetime import datetime\n",
" \n",
" # Prepare metadata for ModelManager\n",
" metadata = {\n",
" \"timestamp\": datetime.now().isoformat(),\n",
" \"data_source\": \"Local S3 ODC (Open Data Cube)\",\n",
" \"collections\": [\"sentinel-2-l2a\"],\n",
" \"features\": features_to_use, # All features used\n",
" \"feature_mode\": \"extended\", # Using extended aggregate features\n",
" \"training_samples\": len(X_train),\n",
" \"testing_samples\": len(X_test),\n",
" \"test_size\": 0.2,\n",
" \"train_accuracy\": float(model.score(X_train, y_train)),\n",
" \"test_accuracy\": float(accuracy),\n",
" \"model_type\": \"random_forest\",\n",
" \"device\": \"cpu\",\n",
" \"n_estimators\": 200,\n",
" \"max_depth\": 30,\n",
" \"learning_rate\": None,\n",
" \"cnn_epochs\": None,\n",
" \"n_features\": X_train.shape[1],\n",
" \"n_classes\": len(np.unique(y)),\n",
" \"class_names\": list(label_mapping.keys()),\n",
" \"classification_report\": classification_report(y_test, y_pred, \n",
" target_names=class_names, \n",
" output_dict=True,\n",
" zero_division=0),\n",
" \"bbox\": None,\n",
" \"time_range\": f\"{date_range[0]}/{date_range[1]}\",\n",
" \"resolution\": 10,\n",
" \"notes\": \"LAND USE CLASSIFICATION model trained from 01.train_ODC.ipynb. Predicts 8 land use classes using multiple spectral indices. NDVI is one of the input features, not the prediction target.\"\n",
" }\n",
" \n",
" # Save model with metadata using updated save_model function\n",
" save_model(\"model_land_use_odc.joblib\", model, metadata=metadata, label_encoder=None)\n",
" \n",
" print(\"✅ Model saved to model_train/model_land_use_odc.joblib\")\n",
" print(f\" - Purpose: Land Use Classification (8 classes)\")\n",
" print(f\" - Features: {len(features_to_use)} ({', '.join(features_to_use[:3])}...)\")\n",
" print(f\" - Train Accuracy: {metadata['train_accuracy']*100:.2f}%\")\n",
" print(f\" - Test Accuracy: {metadata['test_accuracy']*100:.2f}%\")\n",
" print(f\" - Classes: {metadata['n_classes']}\")\n",
" print(f\"\\n📝 NDVI là một trong các features, không phải prediction target\")\n",
" print(f\" Sau khi predict → có thể tính NDVI map riêng để hiển thị\")\n",
" except Exception as e:\n",
" print(f\"❌ Error saving model: {e}\")\n",
" import traceback\n",
" traceback.print_exc()\n",
"else:\n",
" print(\"❌ No model to save\")\n",
"\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "markdown",
"id": "4a8579f4",
"metadata": {},
"source": [
"# 📖 Hướng dẫn sử dụng Model\n",
"\n",
"## Mục đích của Model\n",
"\n",
"Model này được train để **DỰ ĐOÁN PHÂN LOẠI SỬ DỤNG ĐẤT** với 8 lớp:\n",
"\n",
"1. **Lua tom** (0) - Lúa tôm\n",
"2. **Lua** (1) - Lúa\n",
"3. **CHN** (2) - Cây hàng năm\n",
"4. **CLN** (3) - Cây lâu năm \n",
"5. **TS** (4) - Thủy sản\n",
"6. **Song** (5) - Sông\n",
"7. **Dat xay dung** (6) - Đất xây dựng\n",
"8. **Rung** (7) - Rừng\n",
"\n",
"## Features đầu vào\n",
"\n",
"Model sử dụng **nhiều spectral indices** làm features:\n",
"- NDVI (mean, min, max, std, range)\n",
"- NDWI (mean)\n",
"- NDBI (mean)\n",
"- EVI (mean)\n",
"\n",
"## NDVI là gì trong hệ thống này?\n",
"\n",
"⚠️ **QUAN TRỌNG**: NDVI **KHÔNG PHẢI** là mục tiêu dự đoán!\n",
"\n",
"- **NDVI là INPUT FEATURE**: Một trong các chỉ số dùng để train model\n",
"- **Mục tiêu dự đoán**: Phân loại sử dụng đất (8 lớp)\n",
"- **NDVI map**: Có thể hiển thị NDVI map như chỉ số phụ sau khi predict xong\n",
"\n",
"## Workflow Prediction\n",
"\n",
"```python\n",
"# 1. Load model\n",
"model, label_encoder, metadata = model_manager.load_model(\"model_land_use_odc.joblib\")\n",
"\n",
"# 2. Extract features từ satellite data\n",
"features = extract_features(satellite_data) # NDVI, NDWI, NDBI, EVI\n",
"\n",
"# 3. Predict land use classification\n",
"land_use_prediction = model.predict(features)\n",
"# → Kết quả: Mảng với giá trị 0-7 (8 lớp sử dụng đất)\n",
"\n",
"# 4. (Optional) Tính NDVI map riêng để hiển thị\n",
"ndvi_map = (NIR - Red) / (NIR + Red)\n",
"# → NDVI map chỉ để visualize, không phải prediction target\n",
"```\n",
"\n",
"## So sánh với approach cũ\n",
"\n",
"| Approach | Features | Target | NDVI Role |\n",
"|----------|----------|--------|-----------|\n",
"| ❌ Cũ (sai) | Chỉ NDVI | 8 lớp đất | Input duy nhất |\n",
"| ✅ Mới (đúng) | NDVI + NDWI + NDBI + EVI | 8 lớp đất | Một trong nhiều features |\n",
"\n",
"## Test Model\n",
"\n",
"```python\n",
"# Test với website\n",
"# 1. Upload model_land_use_odc.joblib lên server\n",
"# 2. Chọn model trong prediction interface\n",
"# 3. Chọn vùng và thời gian\n",
"# 4. System sẽ tự động:\n",
"# - Extract features (NDVI, NDWI, NDBI, EVI)\n",
"# - Predict land use classification\n",
"# - (Optional) Generate NDVI visualization map\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "33dd516d-9824-499e-96b9-5cd9224c194c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======================================================================\n",
"CLEANUP\n",
"======================================================================\n",
"\n",
"🔄 Closing Dask client and cluster...\n",
"✅ Cleanup complete\n",
"\n",
"======================================================================\n",
"✅ PIPELINE COMPLETE\n",
"======================================================================\n"
]
}
],
"source": [
"# 🛑 CLEANUP\n",
"print(\"=\"*70)\n",
"print(\"CLEANUP\")\n",
"print(\"=\"*70)\n",
"\n",
"print(\"\\n🔄 Closing Dask client and cluster...\")\n",
"try:\n",
" client.close()\n",
" cluster.close()\n",
" print(\"✅ Cleanup complete\")\n",
"except Exception as e:\n",
" print(f\"⚠️ Error during cleanup: {e}\")\n",
"\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"✅ PIPELINE COMPLETE\")\n",
"print(\"=\"*70)"
]
}
],
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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