diff --git a/Source/0_DataGeneration/placeholder b/Source/0_DataGeneration/placeholder
deleted file mode 100644
index e69de29..0000000
diff --git a/Source/0_ModelTraining/CrossValidation.ipynb b/Source/0_ModelTraining/CrossValidation.ipynb
new file mode 100644
index 0000000..5bef7be
--- /dev/null
+++ b/Source/0_ModelTraining/CrossValidation.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "54891470-b955-49af-b7bd-07be107712e6",
+   "metadata": {},
+   "source": [
+    "# Training and Cross Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3428d2c8-984e-42c3-81c5-220bd647d1d5",
+   "metadata": {},
+   "source": [
+    "## Load all required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "579799e9-3336-453c-b933-2d35cbffbf18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "336ea0f2-c8f8-4406-bb68-7153f38df3bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.7.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "import fastai\n",
+    "print(fastai.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "elementary-vault",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eba296f3-c3c1-40a4-b782-fe5e143b81ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import rasterio\n",
+    "import rasterio.plot\n",
+    "import json\n",
+    "from py_linq import Enumerable\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "495478f3-e482-411c-a674-a74bd6ce8cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rasterio import logging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3995a92f-be4d-4acb-bf1d-651a572affc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "log = logging.getLogger()\n",
+    "log.setLevel(logging.ERROR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "obvious-extra",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1c1b20fe-6d44-44dc-abca-3f540c0acfc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "622643a9-5863-4cb4-a9ae-f8bb97821b51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import Helpers.MODIS8DaysHelper as mh\n",
+    "import Helpers.GEEHelpers as GEEHelpers\n",
+    "import Helpers.StaticFeaturesHelper as StaticFeaturesHelper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3328d6d7-f636-4293-a08e-9a9fd095137c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'Helpers.StaticFeaturesHelper' from '/home/jgiezendanner/UA/cvpr23-earthvision-CNN-LSTM-Inundation/Source/Helpers/StaticFeaturesHelper.py'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(mh)\n",
+    "importlib.reload(GEEHelpers)\n",
+    "importlib.reload(StaticFeaturesHelper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6478233c-514d-4979-b311-f012570a82d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ModelClasses.Model import CNNLSTM as CNNLSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e8482fb-145a-4279-99e9-77200734b12e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2bf89542-32bd-4c7b-93b9-7e7d2e077dfa",
+   "metadata": {},
+   "source": [
+    "## Define Data Path and load data references"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "098dbc28-c469-43e3-a0c4-d23761faffe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataPath = Path('../../Data/ModelData/Data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "064b639c-fb0f-467f-97c6-4f65140b27b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstmDF = pd.read_json(dataPath/'lstmFiles.json') # dataframe for LSTM with corresponding data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bb6688b-7ed1-4f67-9403-b3f35107fc45",
+   "metadata": {},
+   "source": [
+    "## Define number of time steps for LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "87963ade-b336-4521-98f7-004c6af18b94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeSteps = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aeb3db09-fff1-4b1a-93ea-3aeb3b14fe4a",
+   "metadata": {},
+   "source": [
+    "## Define functions to access file path and open files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1540353d-c714-4e16-a17a-9407a983b02a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModisFileFromLabel(filePath):\n",
+    "    fileDir = filePath.parent.parent/\"MOD09A1.061\"\n",
+    "    return [fileDir/item for item in lstmDF[lstmDF.File == filePath.name].FeatureFiles.values[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bbfdc095-58b4-439a-9d01-bd9d01d3dbd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getStaticFeaturesFromLabel(filePath):\n",
+    "    elevation = np.expand_dims(StaticFeaturesHelper.getScaledElevation(filePath.parent.parent/'Elevation'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    slopeFile = np.expand_dims(StaticFeaturesHelper.getScaledHAND(filePath.parent.parent/'Slope'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    hand = np.expand_dims(StaticFeaturesHelper.getSlope(filePath.parent.parent/'HAND'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    return np.concatenate((elevation, slopeFile, hand))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "db502cd6-576b-4b35-bf5f-b006efeaa478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def readImage(file, bandsToUse):\n",
+    "    return mh.getScaledModisFileBands(file, bandsToUse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9634c24d-05bf-48b0-a8d1-570593e5c24c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Open MODIS files and indices\n",
+    "def open_features(fn, chnls=None):\n",
+    "    # Stack MODIS time steps\n",
+    "    bandsToUse = ['sur_refl_b03', 'sur_refl_b02', 'sur_refl_b01', 'sur_refl_b04', 'sur_refl_b05', 'sur_refl_b06', 'sur_refl_b07']\n",
+    "    files = getModisFileFromLabel(fn)[0:timeSteps]\n",
+    "    \n",
+    "    staticFeatures = getStaticFeaturesFromLabel(fn)\n",
+    "    \n",
+    "    img = np.empty((0,32,32))\n",
+    "    for file in files:\n",
+    "        try:\n",
+    "            newimg = readImage(file, bandsToUse)\n",
+    "        except:\n",
+    "            newimg = readImage(file, bandsToUse)\n",
+    "        \n",
+    "        newimg = np.concatenate((newimg, staticFeatures))\n",
+    "        img = np.concatenate((newimg, img))\n",
+    "    \n",
+    "    img = img.astype(np.float32)\n",
+    "    img = torch.from_numpy(img)\n",
+    "    return img\n",
+    "\n",
+    "# open ground truth\n",
+    "def open_mask(fn, chnls=None, cls=torch.Tensor):\n",
+    "    img = np.expand_dims(rasterio.open(fn).read(1),0)\n",
+    "    img = img.astype(np.float32)\n",
+    "    npimg = torch.from_numpy(img)\n",
+    "    clsImg = cls(npimg)\n",
+    "    return clsImg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdfc6910-3a27-481b-b799-5b6291c257f6",
+   "metadata": {},
+   "source": [
+    "## define function wot work with multi-band data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "df48333e-f04b-41e3-80c1-24427b895a56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MultiChannelTensorImage(TensorImage):\n",
+    "    _show_args = ArrayImageBase._show_args\n",
+    "    def show(self, channels=[1], ctx=None, vmin=None, vmax=None, **kwargs):\n",
+    "        if len(channels) == 3: \n",
+    "            return show_composite(self, channels=channels, ctx=ctx, vmin=vmin, vmax=vmax,\n",
+    "                                  **{**self._show_args, **kwargs})\n",
+    "    \n",
+    "            \n",
+    "    def norm(vals, vmin=None, vmax=None):\n",
+    "        vmin = ifnone(vmin, np.quantile(vals, 0.01))\n",
+    "        vmax = ifnone(vmax, np.quantile(vals, 0.99))\n",
+    "        return (vals - vmin)/(vmax-vmin)\n",
+    "\n",
+    "    def show_composite(img, channels, ax=None, figsize=(3,3), title=None, scale=True,\n",
+    "                       ctx=None, vmin=None, vmax=None, **kwargs)->plt.Axes:\n",
+    "        \n",
+    "        ax = ifnone(ax, ctx)\n",
+    "        if ax is None: _, ax = plt.subplots()    \n",
+    "        r, g, b = channels\n",
+    "        tempim = img.data.cpu().numpy()\n",
+    "        im = np.zeros((tempim.shape[1], tempim.shape[2], 3))\n",
+    "        im[...,0] = tempim[r]\n",
+    "        im[...,1] = tempim[g]\n",
+    "        im[...,2] = tempim[b]\n",
+    "        if scale: im = norm(im, vmin, vmax)\n",
+    "        ax.imshow(im, **kwargs)\n",
+    "        ax.axis('off')\n",
+    "        if title is not None: ax.set_title(title)\n",
+    "        return ax\n",
+    "\n",
+    "    @classmethod\n",
+    "    def create(cls, fn, chans=None,  **kwargs) ->None:\n",
+    "        return cls(open_features(fn=fn, chnls=chans))\n",
+    "        \n",
+    "    def __repr__(self): return f'{self.__class__.__name__} size={\"x\".join([str(d) for d in self.shape])}'\n",
+    "    \n",
+    "MultiChannelTensorImage.create = Transform(MultiChannelTensorImage.create)\n",
+    "\n",
+    "def MultiChannelImageBlock(cls=MultiChannelTensorImage, chans=None):\n",
+    "    return TransformBlock(partial(cls.create, chans=chans))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745692df-5418-4d81-80ec-4d24e6012421",
+   "metadata": {},
+   "source": [
+    "## create image blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3e8901c6-1bde-487b-bb21-829601cf2ffb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create image blocks\n",
+    "ImageBlock = MultiChannelImageBlock(chans=None)\n",
+    "MaskBlock = TransformBlock(type_tfms=[partial(open_mask, cls=TensorImage)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd8be7bc-5228-4ddb-8ce5-88c3241f3121",
+   "metadata": {},
+   "source": [
+    "## Split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "00a2c0f3-9661-49a8-bb9d-9ca901f0e304",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def FileSplitter(leaveOutYear):\n",
+    "    def _func(x): return int(x.stem.split('_')[2]) >= int(GEEHelpers.GetGEETimeStampFromDate(leaveOutYear,1,1))\\\n",
+    "                        and int(x.stem.split('_')[2]) <= int(GEEHelpers.GetGEETimeStampFromDate(leaveOutYear,12,31))\n",
+    "    def _inner(o, **kwargs): return FuncSplitter(_func)(o)\n",
+    "    return _inner"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e58986a-c5fa-4e21-a27f-dc1d93aae098",
+   "metadata": {},
+   "source": [
+    "## define function to get dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "imported-transcription",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getFilesForStudy(path, items=lstmDF.File.values):\n",
+    "    return [path/('_'.join(item.split('_')[0:2]))/'Sen1FractionInundatedArea'/item for item in items]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "15fccd7e-4dde-4b14-8ed0-d3cb08983437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uncomment this to check if items are found\n",
+    "# items = getFilesForStudy(dataPath, lstmDF.File.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa022ce8-066f-48d0-916a-0a6993aca4ce",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05085e75-de2a-4cbf-8be7-612c3be32d9e",
+   "metadata": {},
+   "source": [
+    "## Define model params, output dir and tfms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a6ae714d-6657-4ef6-a160-c59c3c661bcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(Path('models')/'CNNLSTM').mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "62aaf0b2-baaf-4dad-8b3a-537ad25050b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_tfms = [Rotate(), Flip(), Dihedral()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e87505d-e2ec-4a89-aa57-04529ee6401e",
+   "metadata": {},
+   "source": [
+    "## Train Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9d405346-bb55-420a-92ba-06cd69adc35c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(leaveOutYear):\n",
+    "    \n",
+    "    # Define data loaders\n",
+    "    db = DataBlock(blocks=(ImageBlock, MaskBlock),\n",
+    "               get_items = getFilesForStudy,\n",
+    "               splitter=FileSplitter(leaveOutYear),\n",
+    "               batch_tfms = batch_tfms,\n",
+    "              )\n",
+    "\n",
+    "    dl = db.dataloaders(dataPath, num_workers=20, bs=128)#os.cpu_count()-20, num_workers=20, num_workers=int((os.cpu_count()-20) / 3)\n",
+    "\n",
+    "    # Set model metrics\n",
+    "    acc_metric = [mse, rmse, R2Score()]\n",
+    "    loss_fn = MSELossFlat()\n",
+    "\n",
+    "    # create model\n",
+    "    model = CNNLSTM(nbTimeSteps = timeSteps)\n",
+    "\n",
+    "    # create learner\n",
+    "    learn = Learner(dl, model, loss_func = loss_fn, metrics=acc_metric, opt_func=ranger, cbs=CSVLogger(append=True, fname='history_' + str(leaveOutYear) + '.csv'))\n",
+    "\n",
+    "    # in case we want to load a previous iteration of learning (also modify the for loop below)\n",
+    "    # learn_iter = 0\n",
+    "    # learn.load(\"'CNNLSTM/' + str(leaveOutYear) + str(0), with_opt=False)\n",
+    "\n",
+    "    # in case we want to find the learning rate valley\n",
+    "    # lr = learn.lr_find().valley\n",
+    "    # print(lr)\n",
+    "\n",
+    "    lrs = [.001, .0001, .00001]\n",
+    "    # epochs = [20, 5, 5]\n",
+    "    epochs = [3, 1, 1]\n",
+    "\n",
+    "    # train\n",
+    "    for k in range(3):\n",
+    "        lrslice = slice(lrs[k])\n",
+    "        learn.fit_flat_cos(epochs[k], lr=lrslice)\n",
+    "        learn.save('CNNLSTM/' + str(leaveOutYear) + \"_\" + str(k))\n",
+    "        print('done saving ' + str(k))\n",
+    "\n",
+    "    print('Done with Leave-out year ' + str(leaveOutYear))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "db084ffc-a918-4eb4-b0e0-b2fef714904e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Loop through cross validated years\n",
+    "for leaveOutYear in range(2017, 2022):\n",
+    "    print(' Starting Leave-out year ' + str(leaveOutYear))\n",
+    "    train(leaveOutYear)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbce5cf4-23fb-49f7-82eb-34c9f4940e96",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1313297f-667f-442e-a506-2086289177b1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "886eb264-6767-4843-884f-e88f6b341019",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a95b3558-b2fa-4407-bd3c-12941e5bf122",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57b038c6-9bff-48d0-8693-a80245338cec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Source/1_Inference/InferenceAndPostProcessing.ipynb b/Source/1_Inference/InferenceAndPostProcessing.ipynb
new file mode 100644
index 0000000..43a7056
--- /dev/null
+++ b/Source/1_Inference/InferenceAndPostProcessing.ipynb
@@ -0,0 +1,770 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f0c97d6f-be29-4d4e-9d4c-95df913abeaf",
+   "metadata": {},
+   "source": [
+    "# Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e44bc5b6-ee0e-46ec-9f08-fbef736ee6b7",
+   "metadata": {},
+   "source": [
+    "## Load all required packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "375407ae-eb55-4fcd-b65e-1cf2fb890694",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.7.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "import fastai\n",
+    "print(fastai.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "elementary-vault",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "24e89830-8d12-449f-aab1-b0dab2d3b890",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import rasterio\n",
+    "import rasterio.plot\n",
+    "import rioxarray as riox\n",
+    "from rioxarray.merge import merge_arrays\n",
+    "import json\n",
+    "from py_linq import Enumerable\n",
+    "import wget\n",
+    "import shutil\n",
+    "import multiprocessing as mp\n",
+    "from functools import partial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "obvious-extra",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "module_path = os.path.abspath(os.path.join('..'))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "regional-orientation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import importlib\n",
+    "import Helpers.MODIS8DaysHelper as mh\n",
+    "import Helpers.GEEHelpers as GEEHelpers\n",
+    "import Helpers.StaticFeaturesHelper as StaticFeaturesHelper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "ff87cc4b-434e-4796-8e42-b94dff3956bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'Helpers.StaticFeaturesHelper' from '/home/jgiezendanner/UA/cvpr23-earthvision-CNN-LSTM-Inundation/Source/Helpers/StaticFeaturesHelper.py'>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(mh)\n",
+    "importlib.reload(GEEHelpers)\n",
+    "importlib.reload(StaticFeaturesHelper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "d7a263fa-20bb-4f9c-bbd0-1b67de2cb1d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ModelClasses.Model import CNNLSTM as CNNLSTM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c589b4bd-cd2b-41f2-84ea-d7ab6c1251f5",
+   "metadata": {},
+   "source": [
+    "## Define Data Path and load data references"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "477851ef-0c86-49f3-a570-beef36e5ce05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataPath = Path('../../Data/InferenceData/Data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "064b639c-fb0f-467f-97c6-4f65140b27b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstmDF = pd.read_json(dataPath/'lstmFilesInference.json')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d097baed-1ad4-497e-985b-b35be0892cf5",
+   "metadata": {},
+   "source": [
+    "## Define number of time steps for LSTM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "0379c033-5621-429b-ab37-45a04637a6a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeSteps = 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a89da150-bd7e-4f8f-80ef-a3d9d45ab7fd",
+   "metadata": {},
+   "source": [
+    "## Define functions to access file path and open files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "1cef4603-403d-4191-bc13-cf85f1710cc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getStaticFeaturesFromLabel(filePath):\n",
+    "    elevation = np.expand_dims(StaticFeaturesHelper.getScaledElevation(filePath.parent.parent/'Elevation'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    slopeFile = np.expand_dims(StaticFeaturesHelper.getScaledHAND(filePath.parent.parent/'Slope'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    hand = np.expand_dims(StaticFeaturesHelper.getSlope(filePath.parent.parent/'HAND'/('_'.join(filePath.stem.split('_')[0:2]) + '.tif')), 0)\n",
+    "    return np.concatenate((elevation, slopeFile, hand))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "039baecc-1f47-4a1d-a3be-76462eae76e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getModisFileFromLabel(filePath):\n",
+    "    fileDir = filePath.parent\n",
+    "    return [fileDir/item for item in lstmDF[lstmDF.File == filePath.name].FeatureFiles.values[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "61ed3a37-15c1-4748-a0fc-cc4d5c9ab684",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def readImage(file, bandsToUse):\n",
+    "    return mh.getScaledModisFileBands(file, bandsToUse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "63e4fc17-0513-4fd9-bf55-21788387210d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def open_features(fn, chnls=None):\n",
+    "    bandsToUse = ['sur_refl_b03', 'sur_refl_b02', 'sur_refl_b01', 'sur_refl_b04', 'sur_refl_b05', 'sur_refl_b06', 'sur_refl_b07']\n",
+    "    files = getModisFileFromLabel(fn)[0:timeSteps]\n",
+    "    \n",
+    "    staticFeatures = getStaticFeaturesFromLabel(fn)\n",
+    "    \n",
+    "    img = np.empty((0,32,32))\n",
+    "    for file in files:\n",
+    "        try:\n",
+    "            newimg = readImage(file, bandsToUse)\n",
+    "        except:\n",
+    "            newimg = readImage(file, bandsToUse)\n",
+    "        \n",
+    "        newimg = np.concatenate((newimg, staticFeatures))\n",
+    "        img = np.concatenate((newimg, img))\n",
+    "    \n",
+    "    img = img.astype(np.float32)\n",
+    "    img = torch.from_numpy(img)\n",
+    "    return img\n",
+    "\n",
+    "def open_mask(fn, chnls=None, cls=torch.Tensor):\n",
+    "    img = np.expand_dims(rasterio.open(fn).read(1),0)\n",
+    "    img = img.astype(np.float32)\n",
+    "    npimg = torch.from_numpy(img)\n",
+    "    clsImg = cls(npimg)\n",
+    "    return clsImg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "e068d761-bbef-466a-b881-2e0905162e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MultiChannelTensorImage(TensorImage):\n",
+    "    _show_args = ArrayImageBase._show_args\n",
+    "    def show(self, channels=[1], ctx=None, vmin=None, vmax=None, **kwargs):\n",
+    "        if len(channels) == 3: \n",
+    "            return show_composite(self, channels=channels, ctx=ctx, vmin=vmin, vmax=vmax,\n",
+    "                                  **{**self._show_args, **kwargs})\n",
+    "    \n",
+    "            \n",
+    "    def norm(vals, vmin=None, vmax=None):\n",
+    "        vmin = ifnone(vmin, np.quantile(vals, 0.01))\n",
+    "        vmax = ifnone(vmax, np.quantile(vals, 0.99))\n",
+    "        return (vals - vmin)/(vmax-vmin)\n",
+    "\n",
+    "    def show_composite(img, channels, ax=None, figsize=(3,3), title=None, scale=True,\n",
+    "                       ctx=None, vmin=None, vmax=None, **kwargs)->plt.Axes:\n",
+    "        \n",
+    "        ax = ifnone(ax, ctx)\n",
+    "        if ax is None: _, ax = plt.subplots()    \n",
+    "        r, g, b = channels\n",
+    "        tempim = img.data.cpu().numpy()\n",
+    "        im = np.zeros((tempim.shape[1], tempim.shape[2], 3))\n",
+    "        im[...,0] = tempim[r]\n",
+    "        im[...,1] = tempim[g]\n",
+    "        im[...,2] = tempim[b]\n",
+    "        if scale: im = norm(im, vmin, vmax)\n",
+    "        ax.imshow(im, **kwargs)\n",
+    "        ax.axis('off')\n",
+    "        if title is not None: ax.set_title(title)\n",
+    "        return ax\n",
+    "\n",
+    "    @classmethod\n",
+    "    def create(cls, fn, chans=None,  **kwargs) ->None:\n",
+    "        return cls(open_features(fn=fn, chnls=chans))\n",
+    "        \n",
+    "    def __repr__(self): return f'{self.__class__.__name__} size={\"x\".join([str(d) for d in self.shape])}'\n",
+    "    \n",
+    "MultiChannelTensorImage.create = Transform(MultiChannelTensorImage.create)\n",
+    "\n",
+    "def MultiChannelImageBlock(cls=MultiChannelTensorImage, chans=None):\n",
+    "    return TransformBlock(partial(cls.create, chans=chans))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ca67af-b312-4a60-8c24-4b9e6aecc213",
+   "metadata": {},
+   "source": [
+    "## create image blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "7cf18395-f939-4cce-9f5d-dc96c0618652",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ImageBlock = MultiChannelImageBlock(chans=None)\n",
+    "MaskBlock = TransformBlock(type_tfms=[partial(open_mask, cls=TensorImage)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee544584-a9e0-4d1e-9c14-68e46701a20e",
+   "metadata": {},
+   "source": [
+    "## Get files for inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "imported-transcription",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def getFilesForStudy(path, items=lstmDF.File.values):\n",
+    "    return [path/('_'.join(item.split('_')[0:2]))/'MOD09A1.061'/item for item in items]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "15fccd7e-4dde-4b14-8ed0-d3cb08983437",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "items = getFilesForStudy(dataPath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d574edf1-b2a1-45e5-8bfa-e65663409870",
+   "metadata": {},
+   "source": [
+    "## Create data blocks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "moving-literacy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = DataBlock(blocks=(ImageBlock, MaskBlock),\n",
+    "               get_items = getFilesForStudy\n",
+    "              )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "850d4bde-096f-48f6-bea4-10cc37e85917",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "662cb52c-0292-474f-b993-b5c18fa469e7",
+   "metadata": {},
+   "source": [
+    "## Run Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "913a84fa-1765-4f68-b474-09398e5ca4ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = CNNLSTM(nbTimeSteps = timeSteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "piano-superintendent",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dl = db.dataloaders(dataPath, num_workers=10, bs=180)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "9e7eefa8-b4ba-4a07-bd8a-1f06bc77601e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acc_metric = [mse, rmse, R2Score()]\n",
+    "loss_fn = MSELossFlat()\n",
+    "learn = Learner(dl, model, loss_func = loss_fn, metrics=acc_metric, opt_func=ranger)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8a868ac-1644-4725-8874-6b3216291c71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9b0c30c-8474-48ac-9ca6-445e339187bd",
+   "metadata": {},
+   "source": [
+    "### Get model weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "14567488-4f72-4ec5-afad-a0e8f9be32bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modelFolder = Path('./models')\n",
+    "modelFolder.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "a48e7cf0-94fc-4d23-a356-4428c75b529c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "downloadLink = \"https://github.com/GieziJo/cvpr23-earthvision-CNN-LSTM-Inundation/releases/download/v1.0.0/ModelWeights.zip\"\n",
+    "zipFilePath = modelFolder/'ModelWeights.zip'\n",
+    "wget.download(downloadLink, out = (zipFilePath).as_posix())\n",
+    "shutil.unpack_archive(zipFilePath, modelFolder)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "b83171f4-7d45-4ec2-af1d-1bf6b9411d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modelFolder = Path('./ModelWeights')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "376aaf8f-95df-4162-8403-79aea6fcee5b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e722a454-663d-4c5e-960b-bc70cf1198e1",
+   "metadata": {},
+   "source": [
+    "### Prepare output folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "d5173568-8d42-41f9-9c98-53b2b835f969",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outputFolder = Path('./InferredData')\n",
+    "outputFolder.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "outputFolder_pickleData = outputFolder/'PickleData'\n",
+    "outputFolder_pickleData.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "outputFolder_individualTifs = outputFolder/'IndividualTifs'\n",
+    "outputFolder_individualTifs.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "outputFolder_fullTifs = outputFolder/'FullTifs'\n",
+    "outputFolder_fullTifs.mkdir(exist_ok=True, parents=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8994b6bf-9e3e-4867-8ad4-59c7f718cd8b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "824d6e45-c42e-4dfa-87ca-3c5cf64a1627",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0203e80b-6c9f-446d-a80f-de1e13477aac",
+   "metadata": {},
+   "source": [
+    "### Run inference and save results as raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "53ee6dce-723b-47d4-8949-dc3b7ce6b774",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define raster creation function\n",
+    "def processResultAsRaster(k, items, targetPath, preds):\n",
+    "    item = items[k]\n",
+    "    # new file target path\n",
+    "    fileTargetPath = targetPath/('_'.join(item.stem.split('_')[0:2]))\n",
+    "    fileTargetPath.mkdir(exist_ok=True, parents=True)\n",
+    "    \n",
+    "    # new file\n",
+    "    targetFile = fileTargetPath/item.name\n",
+    "    if targetFile.exists():\n",
+    "        return\n",
+    "    \n",
+    "    with rasterio.open(item) as r:\n",
+    "        profile = r.profile.copy()\n",
+    "        profile.update(count = 1)\n",
+    "        with rasterio.open(targetFile, 'w', **profile) as dst:\n",
+    "            dst.write(preds[k,::],1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "786417d0-4446-4289-9cb8-17cb71e564f4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a5c5c34-49c6-4a79-bfde-f39a6e3f2a11",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "c9217be6-a615-43e2-8c7e-233f5e30f3ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_dl = learn.dls.test_dl(items)\n",
+    "\n",
+    "# run inference for each leave-out year\n",
+    "for year in range(2017,2022):\n",
+    "    featurePicklePath = outputFolder_pickleData/(str(year) + '_Features')\n",
+    "    predictionPicklePath = outputFolder_pickleData/(str(year) + '_Infered')\n",
+    "                                                 \n",
+    "    outputFolder_individualTifs_year = outputFolder_individualTifs/(str(year))\n",
+    "    \n",
+    "    # only run inference if pickle files don't exist\n",
+    "    if not (featurePicklePath.exists() and predictionPicklePath.exists()):\n",
+    "    \n",
+    "        # load model for leave-out year\n",
+    "        modelNamePath = modelFolder/str(year)\n",
+    "        learn.load(modelNamePath)\n",
+    "\n",
+    "        # infer for all items\n",
+    "        preds, _ = learn.get_preds(dl=test_dl)\n",
+    "        preds = preds.squeeze()\n",
+    "\n",
+    "        # save pickel files in case something goes wrong\n",
+    "        with open(featurePicklePath,\"wb\") as f:\n",
+    "            pickle.dump(items, f)\n",
+    "        with open(predictionPicklePath,\"wb\") as f:\n",
+    "            pickle.dump(preds, f)\n",
+    "        \n",
+    "    else:\n",
+    "        with open(predictionPicklePath, \"rb\") as f:\n",
+    "            preds = np.array(pickle.load(f))\n",
+    "        \n",
+    "    # process results as tifs in parallel\n",
+    "    func_part = partial(processResultAsRaster, items=items, targetPath=outputFolder_individualTifs_year, preds=preds)\n",
+    "    _ = mp.Pool(10).map(func_part, range(len(items)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9630fbfe-711b-4e6c-9508-1f24e7a20f52",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b7697f5-5789-4275-b477-d52ca991b530",
+   "metadata": {},
+   "source": [
+    "## Results Post-processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0536dd5-ec51-4a26-842f-d2a38e0d47a3",
+   "metadata": {},
+   "source": [
+    "### create bangladesh full raster for each cross validated year and each time step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "a21b1759-04e2-410d-b656-924ff6dcf209",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def createRasterForTime(time, files, targetPath):\n",
+    "    \n",
+    "    fileName = targetPath/(str(time) + '.tif')\n",
+    "    if fileName.exists():\n",
+    "        return\n",
+    "    \n",
+    "    filesForTime = Enumerable(files).where(lambda item: int(item.stem.split('_')[2]) == time)\n",
+    "    rasters = list(map(lambda item: riox.open_rasterio(item), filesForTime))\n",
+    "    \n",
+    "    gt = rasters[0].rio.transform()\n",
+    "    res = (gt[0], -gt[4])\n",
+    "    crs = str(rasters[0].rio.crs)\n",
+    "    \n",
+    "    merged_raster = merge_arrays(dataarrays = rasters, res = res, crs=crs, nodata = -9999)\n",
+    "    \n",
+    "    merged_raster.rio.to_raster(fileName)\n",
+    "    \n",
+    "    rasters = list(map(lambda item: item.close(), rasters))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "e9fa5e90-5d18-4409-9bd9-9ea65e52be4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "times = np.sort(np.unique(list(map(lambda item: int(item.stem.split('_')[2]), items))))\n",
+    "\n",
+    "for year in range(2017,2022):\n",
+    "    outputFolder_fullTifs_year = outputFolder_fullTifs/(str(year))\n",
+    "    outputFolder_fullTifs_year.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "    outputFolder_individualTifs_year = outputFolder_individualTifs/(str(year))\n",
+    "    files = Enumerable(outputFolder_individualTifs_year.rglob(\"*\")).where(lambda p: p.suffix == '.tif').to_list()\n",
+    "    \n",
+    "    mp.Pool(10).map(partial(createRasterForTime, files = files, targetPath=outputFolder_fullTifs_year), times)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77165a48-b909-4ebe-8eb9-8e6bbebb0faf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d946a5d8-f432-4391-97cf-bd870fea2e55",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dc1a524-64d1-4b1c-bbd7-009a483cb89a",
+   "metadata": {},
+   "source": [
+    "### create bangladesh ensemble full raster for each time step from cross-validated models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "ee8fee01-9de1-4750-a279-ad2ab549d638",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outputFolder_fullTifs_ensemble = outputFolder_fullTifs/'Ensemble'\n",
+    "outputFolder_fullTifs_ensemble.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "for time in times:\n",
+    "    fileName = outputFolder_fullTifs_ensemble/(str(time) + '.tif')\n",
+    "    \n",
+    "    if fileName.exists():\n",
+    "        continue\n",
+    "    \n",
+    "    vals = []\n",
+    "    \n",
+    "    for year in range(2017,2022):\n",
+    "        file = outputFolder_fullTifs/(str(year))/(str(time) + '.tif')\n",
+    "        with riox.open_rasterio(file) as r:\n",
+    "            vals.append(r.values)\n",
+    "    \n",
+    "            if year == 2021:\n",
+    "                out = np.median(np.array(vals), axis=0)\n",
+    "                out[out < 0] = -9999\n",
+    "                \n",
+    "                r.values = out\n",
+    "                r.rio.to_raster(fileName)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a15b5c22-dc36-4fa1-8b3d-5b774ee07d6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Source/1_ModelTraining/placeholder b/Source/1_ModelTraining/placeholder
deleted file mode 100644
index e69de29..0000000
diff --git a/Source/2_Inference/MODISInfo.csv b/Source/2_Inference/MODISInfo.csv
deleted file mode 100644
index efa1eab..0000000
--- a/Source/2_Inference/MODISInfo.csv
+++ /dev/null
@@ -1,1012 +0,0 @@
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-1376697600000,2013-08-17,1376352000000,1377043200000
-1377388800000,2013-08-25,1377043200000,1377734400000
-1378080000000,2013-09-02,1377734400000,1378425600000
-1378771200000,2013-09-10,1378425600000,1379116800000
-1379462400000,2013-09-18,1379116800000,1379808000000
-1380153600000,2013-09-26,1379808000000,1380499200000
-1380844800000,2013-10-04,1380499200000,1381190400000
-1381536000000,2013-10-12,1381190400000,1381881600000
-1382227200000,2013-10-20,1381881600000,1382572800000
-1382918400000,2013-10-28,1382572800000,1383264000000
-1383609600000,2013-11-05,1383264000000,1383955200000
-1384300800000,2013-11-13,1383955200000,1384646400000
-1384992000000,2013-11-21,1384646400000,1385337600000
-1385683200000,2013-11-29,1385337600000,1386028800000
-1386374400000,2013-12-07,1386028800000,1386720000000
-1387065600000,2013-12-15,1386720000000,1387411200000
-1387756800000,2013-12-23,1387411200000,1388102400000
-1388448000000,2013-12-31,1388102400000,1388793600000
-1388880000000,2014-01-05,1388534400000,1389225600000
-1389571200000,2014-01-13,1389225600000,1389916800000
-1390262400000,2014-01-21,1389916800000,1390608000000
-1390953600000,2014-01-29,1390608000000,1391299200000
-1391644800000,2014-02-06,1391299200000,1391990400000
-1392336000000,2014-02-14,1391990400000,1392681600000
-1393027200000,2014-02-22,1392681600000,1393372800000
-1393718400000,2014-03-02,1393372800000,1394064000000
-1394409600000,2014-03-10,1394064000000,1394755200000
-1395100800000,2014-03-18,1394755200000,1395446400000
-1395792000000,2014-03-26,1395446400000,1396137600000
-1396483200000,2014-04-03,1396137600000,1396828800000
-1397174400000,2014-04-11,1396828800000,1397520000000
-1397865600000,2014-04-19,1397520000000,1398211200000
-1398556800000,2014-04-27,1398211200000,1398902400000
-1399248000000,2014-05-05,1398902400000,1399593600000
-1399939200000,2014-05-13,1399593600000,1400284800000
-1400630400000,2014-05-21,1400284800000,1400976000000
-1401321600000,2014-05-29,1400976000000,1401667200000
-1402012800000,2014-06-06,1401667200000,1402358400000
-1402704000000,2014-06-14,1402358400000,1403049600000
-1403395200000,2014-06-22,1403049600000,1403740800000
-1404086400000,2014-06-30,1403740800000,1404432000000
-1404777600000,2014-07-08,1404432000000,1405123200000
-1405468800000,2014-07-16,1405123200000,1405814400000
-1406160000000,2014-07-24,1405814400000,1406505600000
-1406851200000,2014-08-01,1406505600000,1407196800000
-1407542400000,2014-08-09,1407196800000,1407888000000
-1408233600000,2014-08-17,1407888000000,1408579200000
-1408924800000,2014-08-25,1408579200000,1409270400000
-1409616000000,2014-09-02,1409270400000,1409961600000
-1410307200000,2014-09-10,1409961600000,1410652800000
-1410998400000,2014-09-18,1410652800000,1411344000000
-1411689600000,2014-09-26,1411344000000,1412035200000
-1412380800000,2014-10-04,1412035200000,1412726400000
-1413072000000,2014-10-12,1412726400000,1413417600000
-1413763200000,2014-10-20,1413417600000,1414108800000
-1414454400000,2014-10-28,1414108800000,1414800000000
-1415145600000,2014-11-05,1414800000000,1415491200000
-1415836800000,2014-11-13,1415491200000,1416182400000
-1416528000000,2014-11-21,1416182400000,1416873600000
-1417219200000,2014-11-29,1416873600000,1417564800000
-1417910400000,2014-12-07,1417564800000,1418256000000
-1418601600000,2014-12-15,1418256000000,1418947200000
-1419292800000,2014-12-23,1418947200000,1419638400000
-1419984000000,2014-12-31,1419638400000,1420329600000
-1420416000000,2015-01-05,1420070400000,1420761600000
-1421107200000,2015-01-13,1420761600000,1421452800000
-1421798400000,2015-01-21,1421452800000,1422144000000
-1422489600000,2015-01-29,1422144000000,1422835200000
-1423180800000,2015-02-06,1422835200000,1423526400000
-1423872000000,2015-02-14,1423526400000,1424217600000
-1424563200000,2015-02-22,1424217600000,1424908800000
-1425254400000,2015-03-02,1424908800000,1425600000000
-1425945600000,2015-03-10,1425600000000,1426291200000
-1426636800000,2015-03-18,1426291200000,1426982400000
-1427328000000,2015-03-26,1426982400000,1427673600000
-1428019200000,2015-04-03,1427673600000,1428364800000
-1428710400000,2015-04-11,1428364800000,1429056000000
-1429401600000,2015-04-19,1429056000000,1429747200000
-1430092800000,2015-04-27,1429747200000,1430438400000
-1430784000000,2015-05-05,1430438400000,1431129600000
-1431475200000,2015-05-13,1431129600000,1431820800000
-1432166400000,2015-05-21,1431820800000,1432512000000
-1432857600000,2015-05-29,1432512000000,1433203200000
-1433548800000,2015-06-06,1433203200000,1433894400000
-1434240000000,2015-06-14,1433894400000,1434585600000
-1434931200000,2015-06-22,1434585600000,1435276800000
-1435622400000,2015-06-30,1435276800000,1435968000000
-1436313600000,2015-07-08,1435968000000,1436659200000
-1437004800000,2015-07-16,1436659200000,1437350400000
-1437696000000,2015-07-24,1437350400000,1438041600000
-1438387200000,2015-08-01,1438041600000,1438732800000
-1439078400000,2015-08-09,1438732800000,1439424000000
-1439769600000,2015-08-17,1439424000000,1440115200000
-1440460800000,2015-08-25,1440115200000,1440806400000
-1441152000000,2015-09-02,1440806400000,1441497600000
-1441843200000,2015-09-10,1441497600000,1442188800000
-1442534400000,2015-09-18,1442188800000,1442880000000
-1443225600000,2015-09-26,1442880000000,1443571200000
-1443916800000,2015-10-04,1443571200000,1444262400000
-1444608000000,2015-10-12,1444262400000,1444953600000
-1445299200000,2015-10-20,1444953600000,1445644800000
-1445990400000,2015-10-28,1445644800000,1446336000000
-1446681600000,2015-11-05,1446336000000,1447027200000
-1447372800000,2015-11-13,1447027200000,1447718400000
-1448064000000,2015-11-21,1447718400000,1448409600000
-1448755200000,2015-11-29,1448409600000,1449100800000
-1449446400000,2015-12-07,1449100800000,1449792000000
-1450137600000,2015-12-15,1449792000000,1450483200000
-1450828800000,2015-12-23,1450483200000,1451174400000
-1451520000000,2015-12-31,1451174400000,1451865600000
-1451952000000,2016-01-05,1451606400000,1452297600000
-1452643200000,2016-01-13,1452297600000,1452988800000
-1453334400000,2016-01-21,1452988800000,1453680000000
-1454025600000,2016-01-29,1453680000000,1454371200000
-1454716800000,2016-02-06,1454371200000,1455062400000
-1455408000000,2016-02-14,1455062400000,1455753600000
-1456099200000,2016-02-22,1455753600000,1456444800000
-1456790400000,2016-03-01,1456444800000,1457136000000
-1457481600000,2016-03-09,1457136000000,1457827200000
-1458172800000,2016-03-17,1457827200000,1458518400000
-1458864000000,2016-03-25,1458518400000,1459209600000
-1459555200000,2016-04-02,1459209600000,1459900800000
-1460246400000,2016-04-10,1459900800000,1460592000000
-1460937600000,2016-04-18,1460592000000,1461283200000
-1461628800000,2016-04-26,1461283200000,1461974400000
-1462320000000,2016-05-04,1461974400000,1462665600000
-1463011200000,2016-05-12,1462665600000,1463356800000
-1463702400000,2016-05-20,1463356800000,1464048000000
-1464393600000,2016-05-28,1464048000000,1464739200000
-1465084800000,2016-06-05,1464739200000,1465430400000
-1465776000000,2016-06-13,1465430400000,1466121600000
-1466467200000,2016-06-21,1466121600000,1466812800000
-1467158400000,2016-06-29,1466812800000,1467504000000
-1467849600000,2016-07-07,1467504000000,1468195200000
-1468540800000,2016-07-15,1468195200000,1468886400000
-1469232000000,2016-07-23,1468886400000,1469577600000
-1469923200000,2016-07-31,1469577600000,1470268800000
-1470614400000,2016-08-08,1470268800000,1470960000000
-1471305600000,2016-08-16,1470960000000,1471651200000
-1471996800000,2016-08-24,1471651200000,1472342400000
-1472688000000,2016-09-01,1472342400000,1473033600000
-1473379200000,2016-09-09,1473033600000,1473724800000
-1474070400000,2016-09-17,1473724800000,1474416000000
-1474761600000,2016-09-25,1474416000000,1475107200000
-1475452800000,2016-10-03,1475107200000,1475798400000
-1476144000000,2016-10-11,1475798400000,1476489600000
-1476835200000,2016-10-19,1476489600000,1477180800000
-1477526400000,2016-10-27,1477180800000,1477872000000
-1478217600000,2016-11-04,1477872000000,1478563200000
-1478908800000,2016-11-12,1478563200000,1479254400000
-1479600000000,2016-11-20,1479254400000,1479945600000
-1480291200000,2016-11-28,1479945600000,1480636800000
-1480982400000,2016-12-06,1480636800000,1481328000000
-1481673600000,2016-12-14,1481328000000,1482019200000
-1482364800000,2016-12-22,1482019200000,1482710400000
-1483056000000,2016-12-30,1482710400000,1483401600000
-1483574400000,2017-01-05,1483228800000,1483920000000
-1484265600000,2017-01-13,1483920000000,1484611200000
-1484956800000,2017-01-21,1484611200000,1485302400000
-1485648000000,2017-01-29,1485302400000,1485993600000
-1486339200000,2017-02-06,1485993600000,1486684800000
-1487030400000,2017-02-14,1486684800000,1487376000000
-1487721600000,2017-02-22,1487376000000,1488067200000
-1488412800000,2017-03-02,1488067200000,1488758400000
-1489104000000,2017-03-10,1488758400000,1489449600000
-1489795200000,2017-03-18,1489449600000,1490140800000
-1490486400000,2017-03-26,1490140800000,1490832000000
-1491177600000,2017-04-03,1490832000000,1491523200000
-1491868800000,2017-04-11,1491523200000,1492214400000
-1492560000000,2017-04-19,1492214400000,1492905600000
-1493251200000,2017-04-27,1492905600000,1493596800000
-1493942400000,2017-05-05,1493596800000,1494288000000
-1494633600000,2017-05-13,1494288000000,1494979200000
-1495324800000,2017-05-21,1494979200000,1495670400000
-1496016000000,2017-05-29,1495670400000,1496361600000
-1496707200000,2017-06-06,1496361600000,1497052800000
-1497398400000,2017-06-14,1497052800000,1497744000000
-1498089600000,2017-06-22,1497744000000,1498435200000
-1498780800000,2017-06-30,1498435200000,1499126400000
-1499472000000,2017-07-08,1499126400000,1499817600000
-1500163200000,2017-07-16,1499817600000,1500508800000
-1500854400000,2017-07-24,1500508800000,1501200000000
-1501545600000,2017-08-01,1501200000000,1501891200000
-1502236800000,2017-08-09,1501891200000,1502582400000
-1502928000000,2017-08-17,1502582400000,1503273600000
-1503619200000,2017-08-25,1503273600000,1503964800000
-1504310400000,2017-09-02,1503964800000,1504656000000
-1505001600000,2017-09-10,1504656000000,1505347200000
-1505692800000,2017-09-18,1505347200000,1506038400000
-1506384000000,2017-09-26,1506038400000,1506729600000
-1507075200000,2017-10-04,1506729600000,1507420800000
-1507766400000,2017-10-12,1507420800000,1508112000000
-1508457600000,2017-10-20,1508112000000,1508803200000
-1509148800000,2017-10-28,1508803200000,1509494400000
-1509840000000,2017-11-05,1509494400000,1510185600000
-1510531200000,2017-11-13,1510185600000,1510876800000
-1511222400000,2017-11-21,1510876800000,1511568000000
-1511913600000,2017-11-29,1511568000000,1512259200000
-1512604800000,2017-12-07,1512259200000,1512950400000
-1513296000000,2017-12-15,1512950400000,1513641600000
-1513987200000,2017-12-23,1513641600000,1514332800000
-1514678400000,2017-12-31,1514332800000,1515024000000
-1515110400000,2018-01-05,1514764800000,1515456000000
-1515801600000,2018-01-13,1515456000000,1516147200000
-1516492800000,2018-01-21,1516147200000,1516838400000
-1517184000000,2018-01-29,1516838400000,1517529600000
-1517875200000,2018-02-06,1517529600000,1518220800000
-1518566400000,2018-02-14,1518220800000,1518912000000
-1519257600000,2018-02-22,1518912000000,1519603200000
-1519948800000,2018-03-02,1519603200000,1520294400000
-1520640000000,2018-03-10,1520294400000,1520985600000
-1521331200000,2018-03-18,1520985600000,1521676800000
-1522022400000,2018-03-26,1521676800000,1522368000000
-1522713600000,2018-04-03,1522368000000,1523059200000
-1523404800000,2018-04-11,1523059200000,1523750400000
-1524096000000,2018-04-19,1523750400000,1524441600000
-1524787200000,2018-04-27,1524441600000,1525132800000
-1525478400000,2018-05-05,1525132800000,1525824000000
-1526169600000,2018-05-13,1525824000000,1526515200000
-1526860800000,2018-05-21,1526515200000,1527206400000
-1527552000000,2018-05-29,1527206400000,1527897600000
-1528243200000,2018-06-06,1527897600000,1528588800000
-1528934400000,2018-06-14,1528588800000,1529280000000
-1529625600000,2018-06-22,1529280000000,1529971200000
-1530316800000,2018-06-30,1529971200000,1530662400000
-1531008000000,2018-07-08,1530662400000,1531353600000
-1531699200000,2018-07-16,1531353600000,1532044800000
-1532390400000,2018-07-24,1532044800000,1532736000000
-1533081600000,2018-08-01,1532736000000,1533427200000
-1533772800000,2018-08-09,1533427200000,1534118400000
-1534464000000,2018-08-17,1534118400000,1534809600000
-1535155200000,2018-08-25,1534809600000,1535500800000
-1535846400000,2018-09-02,1535500800000,1536192000000
-1536537600000,2018-09-10,1536192000000,1536883200000
-1537228800000,2018-09-18,1536883200000,1537574400000
-1537920000000,2018-09-26,1537574400000,1538265600000
-1538611200000,2018-10-04,1538265600000,1538956800000
-1539302400000,2018-10-12,1538956800000,1539648000000
-1539993600000,2018-10-20,1539648000000,1540339200000
-1540684800000,2018-10-28,1540339200000,1541030400000
-1541376000000,2018-11-05,1541030400000,1541721600000
-1542067200000,2018-11-13,1541721600000,1542412800000
-1542758400000,2018-11-21,1542412800000,1543104000000
-1543449600000,2018-11-29,1543104000000,1543795200000
-1544140800000,2018-12-07,1543795200000,1544486400000
-1544832000000,2018-12-15,1544486400000,1545177600000
-1545523200000,2018-12-23,1545177600000,1545868800000
-1546214400000,2018-12-31,1545868800000,1546560000000
-1546646400000,2019-01-05,1546300800000,1546992000000
-1547337600000,2019-01-13,1546992000000,1547683200000
-1548028800000,2019-01-21,1547683200000,1548374400000
-1548720000000,2019-01-29,1548374400000,1549065600000
-1549411200000,2019-02-06,1549065600000,1549756800000
-1550102400000,2019-02-14,1549756800000,1550448000000
-1550793600000,2019-02-22,1550448000000,1551139200000
-1551484800000,2019-03-02,1551139200000,1551830400000
-1552176000000,2019-03-10,1551830400000,1552521600000
-1552867200000,2019-03-18,1552521600000,1553212800000
-1553558400000,2019-03-26,1553212800000,1553904000000
-1554249600000,2019-04-03,1553904000000,1554595200000
-1554940800000,2019-04-11,1554595200000,1555286400000
-1555632000000,2019-04-19,1555286400000,1555977600000
-1556323200000,2019-04-27,1555977600000,1556668800000
-1557014400000,2019-05-05,1556668800000,1557360000000
-1557705600000,2019-05-13,1557360000000,1558051200000
-1558396800000,2019-05-21,1558051200000,1558742400000
-1559088000000,2019-05-29,1558742400000,1559433600000
-1559779200000,2019-06-06,1559433600000,1560124800000
-1560470400000,2019-06-14,1560124800000,1560816000000
-1561161600000,2019-06-22,1560816000000,1561507200000
-1561852800000,2019-06-30,1561507200000,1562198400000
-1562544000000,2019-07-08,1562198400000,1562889600000
-1563235200000,2019-07-16,1562889600000,1563580800000
-1563926400000,2019-07-24,1563580800000,1564272000000
-1564617600000,2019-08-01,1564272000000,1564963200000
-1565308800000,2019-08-09,1564963200000,1565654400000
-1566000000000,2019-08-17,1565654400000,1566345600000
-1566691200000,2019-08-25,1566345600000,1567036800000
-1567382400000,2019-09-02,1567036800000,1567728000000
-1568073600000,2019-09-10,1567728000000,1568419200000
-1568764800000,2019-09-18,1568419200000,1569110400000
-1569456000000,2019-09-26,1569110400000,1569801600000
-1570147200000,2019-10-04,1569801600000,1570492800000
-1570838400000,2019-10-12,1570492800000,1571184000000
-1571529600000,2019-10-20,1571184000000,1571875200000
-1572220800000,2019-10-28,1571875200000,1572566400000
-1572912000000,2019-11-05,1572566400000,1573257600000
-1573603200000,2019-11-13,1573257600000,1573948800000
-1574294400000,2019-11-21,1573948800000,1574640000000
-1574985600000,2019-11-29,1574640000000,1575331200000
-1575676800000,2019-12-07,1575331200000,1576022400000
-1576368000000,2019-12-15,1576022400000,1576713600000
-1577059200000,2019-12-23,1576713600000,1577404800000
-1577750400000,2019-12-31,1577404800000,1578096000000
-1578182400000,2020-01-05,1577836800000,1578528000000
-1578873600000,2020-01-13,1578528000000,1579219200000
-1579564800000,2020-01-21,1579219200000,1579910400000
-1580256000000,2020-01-29,1579910400000,1580601600000
-1580947200000,2020-02-06,1580601600000,1581292800000
-1581638400000,2020-02-14,1581292800000,1581984000000
-1582329600000,2020-02-22,1581984000000,1582675200000
-1583020800000,2020-03-01,1582675200000,1583366400000
-1583712000000,2020-03-09,1583366400000,1584057600000
-1584403200000,2020-03-17,1584057600000,1584748800000
-1585094400000,2020-03-25,1584748800000,1585440000000
-1585785600000,2020-04-02,1585440000000,1586131200000
-1586476800000,2020-04-10,1586131200000,1586822400000
-1587168000000,2020-04-18,1586822400000,1587513600000
-1587859200000,2020-04-26,1587513600000,1588204800000
-1588550400000,2020-05-04,1588204800000,1588896000000
-1589241600000,2020-05-12,1588896000000,1589587200000
-1589932800000,2020-05-20,1589587200000,1590278400000
-1590624000000,2020-05-28,1590278400000,1590969600000
-1591315200000,2020-06-05,1590969600000,1591660800000
-1592006400000,2020-06-13,1591660800000,1592352000000
-1592697600000,2020-06-21,1592352000000,1593043200000
-1593388800000,2020-06-29,1593043200000,1593734400000
-1594080000000,2020-07-07,1593734400000,1594425600000
-1594771200000,2020-07-15,1594425600000,1595116800000
-1595462400000,2020-07-23,1595116800000,1595808000000
-1596153600000,2020-07-31,1595808000000,1596499200000
-1596844800000,2020-08-08,1596499200000,1597190400000
-1597536000000,2020-08-16,1597190400000,1597881600000
-1598227200000,2020-08-24,1597881600000,1598572800000
-1598918400000,2020-09-01,1598572800000,1599264000000
-1599609600000,2020-09-09,1599264000000,1599955200000
-1600300800000,2020-09-17,1599955200000,1600646400000
-1600992000000,2020-09-25,1600646400000,1601337600000
-1601683200000,2020-10-03,1601337600000,1602028800000
-1602374400000,2020-10-11,1602028800000,1602720000000
-1603065600000,2020-10-19,1602720000000,1603411200000
-1603756800000,2020-10-27,1603411200000,1604102400000
-1604448000000,2020-11-04,1604102400000,1604793600000
-1605139200000,2020-11-12,1604793600000,1605484800000
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diff --git a/Source/2_Inference/STAC.ipynb b/Source/2_Inference/STAC.ipynb
deleted file mode 100644
index 97c6c66..0000000
--- a/Source/2_Inference/STAC.ipynb
+++ /dev/null
@@ -1,523 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bd032238-7e2c-4e7d-860e-5ed8c818d346",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "43785806-5c93-4851-b652-f24447f572c5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import rasterio\n",
-    "from shapely.geometry import Polygon, mapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8315815a-12d1-4aa6-b2f7-41d798ea3f8d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import pystac\n",
-    "from pystac.extensions.eo import Band, EOExtension\n",
-    "\n",
-    "from pystac.extensions.scientific import ScientificExtension\n",
-    "from pystac.extensions.scientific import Publication\n",
-    "\n",
-    "from pystac.provider import Provider\n",
-    "from pystac.provider import ProviderRole\n",
-    "\n",
-    "import pystac.provider \n",
-    "\n",
-    "from pystac import Provider"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6b41424d-d67f-495a-ad12-4148ed6633fa",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "from py_linq import Enumerable\n",
-    "import json"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3b6ab76d-8e99-403a-8365-d781ed505403",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7e13491d-058b-4fa8-8bbd-ef43ea1baf5a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "Path.lsTif = lambda x: Enumerable(x.iterdir()).where(lambda p: p.suffix == '.tiff').to_list()\n",
-    "Path.lsTifName = lambda x: Enumerable(x.iterdir()).where(lambda p: p.suffix == '.tif').select(lambda p: p.name).to_list()\n",
-    "Path.lsTifStem = lambda x: Enumerable(x.iterdir()).where(lambda p: p.suffix == '.tif').select(lambda p: p.stem).to_list()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2cf4730a-4fa6-4eec-9359-bab3b6ee3f4a",
-   "metadata": {},
-   "source": [
-    "### Define Root folder and Data folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "a43b5836-d7ab-44d7-b765-6804e6168c89",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "rootPath = Path('//mule.sbs.arizona.edu/')/'btellman'/'Projects'/'NASA'/'NIP'/'Data'/'Raster'/'CVPR23'/'CVPR23FractionalInundationHistoryData'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "96aaf18d-772b-4687-9931-81fc86559c67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "fractionInundatedFolder = 'CVPR23FractionalInundationHistory'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "38b2a5d8-49ab-4231-8faf-6eec3a61abc9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "dataPath = rootPath/fractionInundatedFolder\n",
-    "stacPath = Path('RelativeSTAC')\n",
-    "stacPath.mkdir(exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0226f1d-438d-4530-869f-1d3afbe6e807",
-   "metadata": {},
-   "source": [
-    "### Create STAC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "698bef26-7896-4298-95b4-944aacf8db34",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "Define function to extract bounding box and footprint from open tif and do it for one file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "bcfb0b9c-398f-4aea-9835-48f89e1210e5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def get_bbox_and_footprint(ds):\n",
-    "    bounds = ds.bounds\n",
-    "    bbox = [bounds.left, bounds.bottom, bounds.right, bounds.top]\n",
-    "    footprint = Polygon([\n",
-    "        [bounds.left, bounds.bottom],\n",
-    "        [bounds.left, bounds.top],\n",
-    "        [bounds.right, bounds.top],\n",
-    "        [bounds.right, bounds.bottom]\n",
-    "    ])\n",
-    "\n",
-    "    return (bbox, mapping(footprint))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "efaa86bc-ef2c-4968-897a-23e01fa9890b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "filePath = dataPath.lsTif()[0]\n",
-    "with rasterio.open(filePath) as ds:\n",
-    "    bbox_footprints = get_bbox_and_footprint(ds)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "825c8dd6-ccf2-4be2-8075-6a12c2507b70",
-   "metadata": {},
-   "source": [
-    "Load the modis info file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "id": "74b60015-94b6-49e7-924e-53f3761715b2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "df_modisInfoTimes = pd.read_csv('MODISInfo.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdad5ab2-5041-4837-b680-3fc218ce0fea",
-   "metadata": {},
-   "source": [
-    "Define band name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "id": "e09a1c55-ce21-49a5-9090-fd7ce8aaa8a9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "sen1_bands =  [\n",
-    "    Band.create(name='b1', description='Fractional Inundated Area')\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f61bf8b3-3ad0-4eb7-a950-c569a36e8fbc",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "define stac info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "eb4c66b2-babc-46b5-872e-31522f408bba",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "platform = 'Fusion Model Output'\n",
-    "license = 'CC-BY-SA-4.0'\n",
-    "items_mission_description = 'This imagery contains the fractional inundated area as inferred from the Fusion model Giezendanner et al. 2023, for the country of Bangladesh, from 2001 to 2022'\n",
-    "pub_doi = '10.48550/arXiv.2305.00640'\n",
-    "\n",
-    "citation = 'Giezendanner, J.; Mukherjee, R.; Purri, M.; Thomas, M.; Mauerman, M.; Islam, A. K. M. S.; Tellman, B. Inferring the Past: A Combined CNN-LSTM Deep Learning Framework to Fuse Satellites for Historical Inundation Mapping. arXiv April 30, 2023. http://arxiv.org/abs/2305.00640'\n",
-    "\n",
-    "# collection definitions\n",
-    "collection_id=\"FractionalInundationHistory_ts\"\n",
-    "collection_title='Fractional Inundation History Time Series'\n",
-    "collection_description='Bangladesh Historical Fractional Inundated Area Dataset'\n",
-    "\n",
-    "# top-level catalog definitions\n",
-    "catalog_id = 'Bangladesh Historical Fractional Inundated Area'\n",
-    "catalog_description = 'STAC catalog for Bangladesh Historical Fractional Inundated Area Dataset'\n",
-    "\n",
-    "#Provider information\n",
-    "provider = Provider(name='Jonathan Giezendanner',\n",
-    "                       description=\"Postdoctoral Researcher University of Arizona, Social [Pixel] Lab\",\n",
-    "                       roles=[ProviderRole.PRODUCER, ProviderRole.PROCESSOR],\n",
-    "                       url='https://www.jgiezendanner.com/')\n",
-    "\n",
-    "provider_dict = provider.to_dict()\n",
-    "\n",
-    "contact_email = 'jgiezendanner@arizona.edu'\n",
-    "            \n",
-    "targetUrl = Path('https://data.cyverse.org/dav-anon/iplant/commons/cyverse_curated/Giezendanner_BangladeshInundationHistory_Mai2023/CVPR23FractionalInundationHistory/')\n",
-    "# catalogPath = Path('/dav-anon/iplant/commons/curated/Giezendanner_BangladeshInundationHistory_Mai2023/CVPR23FractionalInundationHistory/CVPR23FractionalInundationHistorySTAC/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0d983e29-755c-4f1c-8f7f-6c1f63469124",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32a62333-71f3-47e5-888b-f8f062f7644e",
-   "metadata": {},
-   "source": [
-    "Create function to convert time stamp to date time format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "id": "94815834-03f0-4b77-8869-b271184188c1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def getDateFromTimeStamp(timeStamp):\n",
-    "    return datetime.datetime.fromtimestamp(timeStamp / 1000.0, tz=datetime.timezone.utc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eea83e4d-6e97-42c8-b62e-d777ea537fbc",
-   "metadata": {},
-   "source": [
-    "Get item from raster URI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "id": "c3f9ba83-cbb2-4c10-9bdc-7947c861743a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "def getWaterItem(raster_uri):\n",
-    "    # use stem of file as id\n",
-    "    idx = raster_uri.stem\n",
-    "    \n",
-    "    # get modis info from pandas dataframe\n",
-    "    modisInfo = df_modisInfoTimes[df_modisInfoTimes.Time == int(idx)]\n",
-    "    \n",
-    "    # get bbox and footprint\n",
-    "    bbox, footprint = bbox_footprints\n",
-    "    \n",
-    "    # get dates from modisInfo and convert to datetime object\n",
-    "    date = getDateFromTimeStamp(int(idx))\n",
-    "    startDate = getDateFromTimeStamp(int(modisInfo.StartDate.values[0]))\n",
-    "    endDate = getDateFromTimeStamp(int(modisInfo.EndDate.values[0]))\n",
-    "    \n",
-    "    #create item    \n",
-    "    item = pystac.Item(\n",
-    "        id=idx,\n",
-    "        geometry=footprint,\n",
-    "        bbox=bbox,\n",
-    "        stac_extensions=['https://stac-extensions.github.io/projection/v1.0.0/schema.json', 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json'],\n",
-    "        datetime=date,\n",
-    "        start_datetime=startDate,\n",
-    "        end_datetime=endDate,\n",
-    "        properties=dict(\n",
-    "            tile ='FractionalInundatedArea_' + idx,\n",
-    "            gsd = 500,\n",
-    "            platform= platform,\n",
-    "            license= license,\n",
-    "            mission= items_mission_description,\n",
-    "            providers= [provider_dict]\n",
-    "        )\n",
-    "    )\n",
-    "    \n",
-    "    sci_ext = ScientificExtension.ext(item)\n",
-    "    sci_ext.apply(doi=pub_doi, citation=citation)\n",
-    "    \n",
-    "    # apply eo extension\n",
-    "    eo = EOExtension.ext(item, add_if_missing=True)\n",
-    "    eo.apply(bands=sen1_bands)\n",
-    "    \n",
-    "    # add asset to item with raster path\n",
-    "    item.add_asset(\n",
-    "        key='FractionalInundatedArea',\n",
-    "        asset=pystac.Asset(\n",
-    "            title='Fractional Inundated Area',\n",
-    "            href= (targetUrl/raster_uri.name).as_posix(),\n",
-    "            media_type=pystac.MediaType.GEOTIFF\n",
-    "        )\n",
-    "    )\n",
-    "    return item"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb7f9f80-44da-4c94-a50f-c94ce551c027",
-   "metadata": {},
-   "source": [
-    "Create catalog"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "id": "94b6559d-b8bd-4be5-9ce3-6e52771d9a9d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# create catalog\n",
-    "catalog = pystac.Catalog(id=catalog_id, description=catalog_description)\n",
-    "\n",
-    "# create modis collection\n",
-    "modisTimeSeriesCollection = pystac.Collection(id=collection_id, title=collection_title, description=collection_description, extent=bbox_footprints)\n",
-    "\n",
-    "# set the collection's catalog\n",
-    "modisTimeSeriesCollection.catalog = catalog\n",
-    "\n",
-    "# crawl data folder to get all files\n",
-    "modisFiles = dataPath.lsTif()\n",
-    "\n",
-    "# loop over all files\n",
-    "for file in modisFiles:\n",
-    "    # get item for file\n",
-    "    item = getWaterItem(file)\n",
-    "    # set the item's collection\n",
-    "    item.collection = modisTimeSeriesCollection\n",
-    "    # add item to collection\n",
-    "    modisTimeSeriesCollection.add_item(item)\n",
-    "\n",
-    "# update the collection's extent from the items\n",
-    "modisTimeSeriesCollection.update_extent_from_items()\n",
-    "\n",
-    "# add collection to catalogue\n",
-    "catalog.add_child(modisTimeSeriesCollection)\n",
-    "\n",
-    "\n",
-    "# normalise all paths relative to stac folder\n",
-    "# catalog.normalize_hrefs(catalogPath.as_posix())\n",
-    "# catalog.make_all_asset_hrefs_absolute()\n",
-    "# catalog.save(catalog_type=pystac.CatalogType.ABSOLUTE_PUBLISHED, dest_href=stacPath.as_posix())\n",
-    "\n",
-    "catalog.normalize_hrefs(stacPath.as_posix())\n",
-    "catalog.make_all_asset_hrefs_relative()\n",
-    "catalog.save(catalog_type=pystac.CatalogType.SELF_CONTAINED)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "556892c9-8fa2-4b9a-975c-49cd72352146",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "id": "b2aba5bc-6d7f-4b20-8243-09f1468337c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#########Combine all of the STAC item metadata into a single geojson and output to directory\n",
-    "####################\n",
-    "\n",
-    "# Create an empty GeoJSON FeatureCollection\n",
-    "geojson = {\n",
-    "    \"type\": \"FeatureCollection\",\n",
-    "    \"features\": []\n",
-    "}\n",
-    "\n",
-    "# Iterate through all the items in the collection and convert them to GeoJSON Features\n",
-    "for item in modisTimeSeriesCollection.get_all_items():\n",
-    "    # Get the STAC Item as a dictionary\n",
-    "    item_dict = item.to_dict()\n",
-    "\n",
-    "    # Convert the STAC Item to a GeoJSON Feature\n",
-    "    feature = {\n",
-    "        \"type\": \"Feature\",\n",
-    "        \"collection\": item_dict[\"collection\"],\n",
-    "        \"stac_version\": item_dict[\"stac_version\"],\n",
-    "        \"stac_extensions\": item_dict[\"stac_extensions\"],\n",
-    "        \"id\": item_dict[\"id\"],\n",
-    "        \"geometry\": item_dict[\"geometry\"],\n",
-    "        \"bbox\": item_dict[\"bbox\"],\n",
-    "        \"properties\": item_dict[\"properties\"],\n",
-    "        \"assets\": item_dict[\"assets\"]\n",
-    "    }\n",
-    "\n",
-    "    # Add the GeoJSON Feature to the FeatureCollection\n",
-    "    geojson[\"features\"].append(feature)\n",
-    "\n",
-    "\n",
-    "\n",
-    "output_file_path = stacPath/\"index.geojson\"\n",
-    "\n",
-    "# Write the GeoJSON FeatureCollection to a file\n",
-    "with open(output_file_path, \"w\") as f:\n",
-    "    json.dump(geojson, f, indent=4)   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14eb0b5f-ef68-4b2b-8ee2-8ebb00abefce",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/Source/Helpers/GEEHelpers.py b/Source/Helpers/GEEHelpers.py
new file mode 100644
index 0000000..7de26a4
--- /dev/null
+++ b/Source/Helpers/GEEHelpers.py
@@ -0,0 +1,7 @@
+import datetime
+
+def GetDateFromGEETimeStamp(timeStamp):
+    return datetime.datetime.fromtimestamp(timeStamp / 1000.0, tz=datetime.timezone.utc)
+
+def GetGEETimeStampFromDate(year,month,day):
+    return int(datetime.datetime.timestamp(datetime.datetime(year,month,day)) * 1000)
\ No newline at end of file
diff --git a/Source/Helpers/MODIS8DaysHelper.py b/Source/Helpers/MODIS8DaysHelper.py
new file mode 100644
index 0000000..9127c3b
--- /dev/null
+++ b/Source/Helpers/MODIS8DaysHelper.py
@@ -0,0 +1,63 @@
+import numpy as np
+import rasterio
+
+bandNames = ["sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04", "sur_refl_b05", "sur_refl_b06", "sur_refl_b07", "QA", "SolarZenith", "ViewZenith", "RelativeAzimuth", "StateQA", "DayOfYear"]
+ranges = [[-100, 16000],[-100, 16000],[-100, 16000],[-100, 16000],[-100, 16000],[-100, 16000],[-100, 16000],[],[],[],[],[],[]]
+
+def getBandNumbers(names):
+    return [bandNames.index(entry) for entry in names]
+
+def getBandNumber(name):
+    return bandNames.index(name)
+
+def convertToRasterioNumbers(numbers):
+    return [number + 1 for number in numbers]
+
+def getBandRange(name):
+    return ranges[getBandNumber(name)]
+def getBandRanges(names):
+    return [ranges[i] for i in getBandNumbers(names)]
+
+def getByteImage(image, dataRange):
+    return np.interp(image, dataRange, (0, 255)).astype(np.uint8)
+
+def getScaledImage(image, dataRange):
+    return np.interp(image, dataRange, (0.0, 1.0))
+
+def getModisFileBandsAsUInt8Matrix(modisFile, bandNames):
+    banNumbers = getBandNumbers(bandNames)
+    with rasterio.open(modisFile) as modisImage:
+        bandValues = modisImage.read(convertToRasterioNumbers(banNumbers))
+        bandRanges = getBandRanges(bandNames)
+        bOut = np.zeros(bandValues.shape).astype(np.uint8)
+        for i in range(len(bandRanges)):
+            bOut[i,:] = getByteImage(bandValues[i,:], bandRanges[i])
+        return bOut
+
+def getScaledModisFileBands(modisFile, bandNames):
+    banNumbers = getBandNumbers(bandNames)
+    with rasterio.open(modisFile) as modisImage:
+        bandValues = modisImage.read(convertToRasterioNumbers(banNumbers))
+        bandRanges = getBandRanges(bandNames)
+        bOut = np.zeros(bandValues.shape)
+        for i in range(len(bandRanges)):
+            bOut[i,:] = getScaledImage(bandValues[i,:], bandRanges[i])
+    return bOut
+
+def getScaledModisFileBandsWithOpenRaster(modisRasterImage, bandNames):
+    banNumbers = getBandNumbers(bandNames)
+    bandValues = modisRasterImage.read(convertToRasterioNumbers(banNumbers))
+    bandRanges = getBandRanges(bandNames)
+    bOut = np.zeros(bandValues.shape)
+    for i in range(len(bandRanges)):
+        bOut[i,:] = getScaledImage(bandValues[i,:], bandRanges[i])
+    return bOut
+
+def getModisFileBands(modisFile, bandNames):
+    banNumbers = getBandNumbers(bandNames)
+    with rasterio.open(modisFile) as modisImage:
+        return modisImage.read(convertToRasterioNumbers(banNumbers))
+
+def getModisFileBandsWithOpenRaster(modisRasterImage, bandNames):
+    banNumbers = getBandNumbers(bandNames)
+    return modisRasterImage.read(convertToRasterioNumbers(banNumbers))
diff --git a/Source/Helpers/StaticFeaturesHelper.py b/Source/Helpers/StaticFeaturesHelper.py
new file mode 100644
index 0000000..84f72f7
--- /dev/null
+++ b/Source/Helpers/StaticFeaturesHelper.py
@@ -0,0 +1,20 @@
+import numpy as np
+import rasterio
+
+def getScaledImage(image, dataRange):
+    return np.interp(image, dataRange, (0.0, 1.0))
+
+def getScaledHAND(file):
+    with rasterio.open(file) as image:
+        bOut = getScaledImage(image.read(1), [0.0, 7.0])
+    return bOut
+
+def getScaledElevation(file):
+    with rasterio.open(file) as image:
+        bOut = getScaledImage(image.read(1), [0.0, 100.0])
+    return bOut
+
+def getSlope(file):
+    with rasterio.open(file) as image:
+        bOut = image.read(1)
+    return bOut
diff --git a/Source/ModelClasses/Model.py b/Source/ModelClasses/Model.py
new file mode 100644
index 0000000..d247607
--- /dev/null
+++ b/Source/ModelClasses/Model.py
@@ -0,0 +1,53 @@
+from fastai.vision.all import *
+
+class CNNLSTM(nn.Module):
+    def __init__(self, nbFeatures=10, initSize=32, nbLayers=1, nbTimeSteps=10, input_size=32*32, hidden_size=32*32):
+        super().__init__()
+        
+        self.nbFeatures = nbFeatures
+        self.input_size = input_size
+        self.nbTimeSteps = nbTimeSteps
+        
+        # define helper functions for convolutions, either single convolution, or combined with res block
+        def conv2_single(ni,nf): return nn.Conv2d(ni, nf, kernel_size=3, padding=1, padding_mode='reflect', stride=1)
+        def conv2(ni,nf): return nn.Conv2d(ni, nf, groups=nbTimeSteps, kernel_size=3, padding=1, padding_mode='reflect', stride=1)
+        def conv2_and_res(ni, nf): return nn.Sequential(conv2(ni,nf), ResBlock(2, ni, nf, groups=nbTimeSteps, stride=1))
+        
+        # Create CNN A
+        # note that groups are defined by number of time steps, i.e. each time step is applied the same CNN separatly
+        self.cnn = nn.Sequential(
+            conv2(nbFeatures*nbTimeSteps,nbTimeSteps*initSize)
+        )
+        
+        for k in range(nbLayers):
+            self.cnn = self.cnn.append(conv2_and_res(nbTimeSteps*initSize * 4**k, nbTimeSteps*(initSize * 2) * 4**k))
+        self.cnn = self.cnn.append(conv2(nbTimeSteps*(initSize * 2) * 4**(nbLayers-1)* 2,nbTimeSteps*1))
+        
+        # Create LSTM
+        self.LSTM = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=1, batch_first=True, bidirectional=False)
+        
+        # Create transpose convolution
+        self.convTrans = nn.ConvTranspose2d(1024,1,kernel_size=32)
+        
+        # Set the output to be a single convolution and a sigmoid
+        self.outLayer = nn.Sequential(conv2_single(2,1), SigmoidRange(0,1))
+
+    def forward(self, x):
+        # get size of problem
+        batchSize = x.shape[0]
+        imgSize = x.shape[2:4]
+        
+        # pass all time steps through the CNN
+        x = self.cnn(x)
+        # extract time step 0
+        x_now = x[:,0,::].view((batchSize,1,imgSize[0],imgSize[1]))
+        # pass time step -1 to -9 through lstm
+        x, (_,_) = self.LSTM(x[:,1:,::].view((batchSize,self.nbTimeSteps-1,-1)))
+        # extract result and pass through transpose convolution
+        x = x[:,-1,:].view((batchSize,-1,1,1))
+        x = self.convTrans(x)
+        # concatenate lstm output and time step t
+        x = torch.cat((x_now,x),1)
+        # pass output through output layer
+        x = self.outLayer(x)
+        return x
\ No newline at end of file