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Qberto · Oct 24, 2018 · 54cf797 · 54cf797
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diff --git a/TODOs.txt b/TODOs.txt
@@ -0,0 +1,14 @@
+Run as admin 
+proenv.bat
+
+Get GitHub
+
+pip install jupyter notebook
+
+broken link on kycafos shapefile - Repair data source
+
+Bring CAFO notebook over
+
+Bring CAFO frozen graph over
+
+Bring Label map
diff --git a/doc/.ipynb_checkpoints/Step1_DataPreparation-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step1_DataPreparation-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     " # <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/python.png\"></img>\n",
+    "<img src=\"img/python.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 1: Data Preparation</span>"
    ]
@@ -114,7 +114,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Set reference to our map, cafos layer, a layout object, and an output directory\n",
@@ -148,7 +150,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Main iteration loop\n",
@@ -192,6 +196,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -224,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step2_Annotation_and_Labeling-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step2_Annotation_and_Labeling-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/python.png\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 2: Annotation and Labeling of the Images</span>"
    ]
@@ -76,7 +76,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -100,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step3_TrainTestSplit-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step3_TrainTestSplit-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/python.png\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 3: Split Labeled Images into Train/Test Samples</span>"
    ]
@@ -40,48 +40,59 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "SpaceNet\n",
+    "## Other Sources of Labeled Training Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also leverage a wealth of pre-annotated imagery and labeled training data for a variety of purposes. The following resources can help:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### SpaceNet\n",
     "A Corpus of commercial satellite imagery and labeled training data – could be used to expand on our land classification work:\n",
     "\n",
     "https://aws.amazon.com/public-datasets/spacenet/\n",
     "\n",
-    "SAT-4 & SAT-6\n",
+    "#### SAT-4 & SAT-6\n",
     "Barren land, trees, grassland, roads, buildings and water bodies\n",
     "\n",
     "http://csc.lsu.edu/~saikat/deepsat/\n",
     "\n",
-    "DSTL\n",
+    "#### DSTL\n",
     "Buildings, Manmade structures, Road, Track, Trees, Crops, Waterway, Standing water, Vehicle Large, Vehicle Small\n",
     "\n",
     "https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/data\n",
     "\n",
-    "Cars Overhead With Context (COWC)\n",
+    "#### Cars Overhead With Context (COWC)\n",
     "Annotated cars from overhead\n",
     "\n",
     "https://gdo-datasci.ucllnl.org/cowc/\n",
     "\n",
-    "Slums and informal settlements\n",
+    "#### Slums and informal settlements\n",
     "\n",
     "https://www.kaggle.com/fedebayle/slums-argentina\n",
     "\n",
-    "PlanesNet\n",
+    "#### PlanesNet\n",
     "Planes in Satellite Imagery\n",
     "\n",
     "https://www.kaggle.com/rhammell/planesnet"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Contribute Your Own!"
+   ]
   }
  ],
  "metadata": {
@@ -100,7 +111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step4_CreateTFRecords-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step4_CreateTFRecords-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/robo.jpg\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 4: Generate TF Records from the train/test splits</span>"
    ]
@@ -29,7 +29,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Conversion from XML files to singular CSVs"
+    "## 1. Conversion from XML files to singular CSVs"
    ]
   },
   {
@@ -80,7 +80,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "os.chdir(\"\")  # Set workspace path\n",
@@ -105,7 +107,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Conversion of singular CSVs to TFRecords"
+    "## 2. Conversion of singular CSVs to TFRecords"
    ]
   },
   {
@@ -151,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step5_SetConfigFiles-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step5_SetConfigFiles-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/robo.jpg\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 5: Set up a configuration file containing CNN hyperparameters and a label file containing your object classes</span>"
    ]
@@ -174,7 +174,9 @@
    "metadata": {},
    "source": [
     "#### Activation function\n",
-    "The function used to determine if a neuron will \"fire\" or not. "
+    "The function used to determine if a neuron will \"fire\" or not. \n",
+    "\n",
+    "<img src=\"img/relu.png\" style=\"width:50%\"></img>"
    ]
   },
   {
@@ -473,7 +475,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step6_Training-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step6_Training-checkpoint.ipynb
@@ -6,8 +6,6 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/robo.jpg\"></img>\n",
-    "\n",
     "## <span style=\"color:blue\">Step 6: Neural Network Training!</span>"
    ]
   },
@@ -119,7 +117,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step7_ExportInferenceGraph-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step7_ExportInferenceGraph-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/robo.jpg\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 7: Export an inference graph from the new trained model</span>"
    ]
@@ -51,7 +51,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Very easy!"
+    "### Quite straightforward."
    ]
   }
  ],
@@ -71,7 +71,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/.ipynb_checkpoints/Step8_RealTimeDetection-checkpoint.ipynb b/doc/.ipynb_checkpoints/Step8_RealTimeDetection-checkpoint.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/robo.jpg\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 8: Detect CAFO sites!</span>"
    ]
@@ -321,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

diff --git a/doc/Step1_DataPreparation.ipynb b/doc/Step1_DataPreparation.ipynb
@@ -6,7 +6,7 @@
    "source": [
     " # <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/python.png\"></img>\n",
+    "<img src=\"img/python.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 1: Data Preparation</span>"
    ]
@@ -114,7 +114,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Set reference to our map, cafos layer, a layout object, and an output directory\n",
@@ -148,7 +150,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Main iteration loop\n",
@@ -192,6 +196,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -224,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

diff --git a/doc/Step2_Annotation_and_Labeling.ipynb b/doc/Step2_Annotation_and_Labeling.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"color:purple\">GIS and Machine Learning for Object Detection in Satellite Imagery</span>\n",
     "\n",
-    "<img src=\"img/python.png\"></img>\n",
+    "<img src=\"img/robot.jpg\"></img>\n",
     "\n",
     "## <span style=\"color:blue\">Step 2: Annotation and Labeling of the Images</span>"
    ]
@@ -99,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,