diff --git a/.gitignore b/.gitignore
index 67d161cd..daeccda9 100755
--- a/.gitignore
+++ b/.gitignore
@@ -164,3 +164,6 @@ cython_debug/
 tests/data/local/**
 !tests/data/local/**/
 !tests/data/local/**/.gitkeep
+
+#configuration files
+.vscode/launch.json
\ No newline at end of file
diff --git a/docs/training_data_tools/points_to_raster.md b/docs/training_data_tools/points_to_raster.md
new file mode 100644
index 00000000..5fccb9a5
--- /dev/null
+++ b/docs/training_data_tools/points_to_raster.md
@@ -0,0 +1,3 @@
+# Points to raster
+
+::: eis_toolkit.training_data_tools.points_to_raster
diff --git a/docs/training_data_tools/random_sampling.md b/docs/training_data_tools/random_sampling.md
new file mode 100644
index 00000000..83652052
--- /dev/null
+++ b/docs/training_data_tools/random_sampling.md
@@ -0,0 +1,3 @@
+# Random sampling
+
+::: eis_toolkit.training_data_tools.random_sampling
\ No newline at end of file
diff --git a/eis_toolkit/training_data_tools/points_to_raster.py b/eis_toolkit/training_data_tools/points_to_raster.py
new file mode 100644
index 00000000..1e5689ca
--- /dev/null
+++ b/eis_toolkit/training_data_tools/points_to_raster.py
@@ -0,0 +1,157 @@
+from numbers import Number
+
+import geopandas
+import numpy as np
+from beartype import beartype
+from beartype.typing import Literal, Optional, Tuple, Union
+from rasterio import profiles, transform
+from scipy.ndimage import binary_dilation
+
+from eis_toolkit.exceptions import EmptyDataFrameException, NonMatchingCrsException
+from eis_toolkit.utilities.checks.raster import check_raster_profile
+
+
+def _get_kernel_size(radius: int) -> tuple[int, int]:
+    size = 1 + (radius * 2)
+    return size, radius
+
+
+def _create_grid(size: int, radius) -> tuple[np.ndarray, np.ndarray]:
+    y = np.arange(-radius, size - radius)
+    x = np.arange(-radius, size - radius)
+    y, x = np.meshgrid(y, x)
+    return x, y
+
+
+def _basic_kernel(radius: int, value: Number) -> np.ndarray:
+    size, _ = _get_kernel_size(radius)
+
+    x, y = _create_grid(size, radius)
+    mask = x**2 + y**2 <= radius**2
+    kernel = np.zeros((size, size))
+    kernel[mask] = value
+
+    return kernel
+
+
+def _create_local_buffer(
+    array: np.ndarray,
+    radius: int,
+    target_value: Number,
+) -> np.ndarray:
+    kernel = _basic_kernel(radius, target_value)
+    array = np.squeeze(array) if array.ndim >= 3 else array
+
+    return binary_dilation(array == target_value, structure=kernel)
+
+
+def _create_buffer_around_labels(
+    array: np.ndarray,
+    radius: int = 1,
+    target_value: int = 1,
+    buffer: Optional[str] = None,
+    overwrite_nodata: bool = False,
+) -> np.ndarray:
+    out_array = np.copy(array)
+    out_array = _create_local_buffer(
+        array=out_array,
+        radius=radius,
+        target_value=target_value,
+    )
+
+    if buffer == "avg":
+        out_array = np.where(out_array, target_value, 0)
+        out_array = np.where((array != 0) & (out_array != 0), (array + out_array) * 0.5, (array + out_array))
+    elif buffer == "max":
+        out_array = np.where(out_array, target_value, 0)
+        out_array = np.where(array != 0, np.maximum(array, out_array), out_array)
+    elif buffer == "min":
+        out_array = np.where(out_array, target_value, 0)
+        out_array = np.where((array != 0) & (out_array != 0), np.minimum(array, out_array), (array + out_array))
+    else:
+        out_array = np.where(out_array, target_value, array)
+
+    if overwrite_nodata is False:
+        out_array = np.where(np.isnan(array), np.nan, out_array)
+
+    return out_array
+
+
+def _point_to_raster(raster_array, raster_meta, geodataframe, attribute, radius, buffer):
+
+    width = raster_meta.get("width")
+    height = raster_meta.get("height")
+
+    raster_transform = raster_meta.get("transform")
+
+    left = raster_transform[2]
+    top = raster_transform[5]
+    right = left + width * raster_transform[0]
+    bottom = top + height * raster_transform[4]
+
+    geodataframe = geodataframe.cx[left:right, bottom:top]
+
+    if attribute is not None:
+        values = geodataframe[attribute]
+    else:
+        values = [1]
+
+    positives_rows, positives_cols = transform.rowcol(
+        raster_transform, geodataframe.geometry.x, geodataframe.geometry.y
+    )
+    raster_array[positives_rows, positives_cols] = values
+
+    unique_values = list(set(values))
+
+    if radius is not None:
+        for target_value in unique_values:
+            raster_array = _create_buffer_around_labels(raster_array, radius, target_value, buffer)
+
+    return raster_array
+
+
+@beartype
+def points_to_raster(
+    geodataframe: geopandas.GeoDataFrame,
+    raster_profile: Union[profiles.Profile, dict],
+    attribute: Optional[str] = None,
+    radius: Optional[int] = None,
+    buffer: Optional[Literal["min", "avg", "max"]] = None,
+) -> Tuple[np.ndarray, Union[profiles.Profile, dict]]:
+    """Convert a point data set into a binary raster.
+
+    Assigs attribute values if provided else 1 to pixels corresponding to the points and 0 elsewhere.
+
+    Args:
+        geodataframe: The geodataframe points set to be converted into raster.
+        attribute: Values to be be assigned to the geodataframe.
+        radius: Radius to be applied around the geodataframe in [m].
+        buffer: Buffers the matrix value when two or more radii with different attribute value overlap.
+                'avg': performs averaging of the two attribute value
+                'min': minimum of the two attribute values
+                'max': maximum of the two attribute values
+
+    Returns:
+        A tuple containing the output raster as a NumPy array and updated metadata.
+
+    Raises:
+        EmptyDataFrameException:  The input GeoDataFrame is empty.
+        NonMatchingCrsException: The raster and geodataframe are not in the same CRS.
+    """
+
+    if geodataframe.empty:
+        raise EmptyDataFrameException("Expected geodataframe to contain geometries.")
+
+    if raster_profile.get("crs") != geodataframe.crs:
+        raise NonMatchingCrsException("Expected coordinate systems to match between raster and GeoDataFrame.")
+
+    check_raster_profile(raster_profile=raster_profile)
+
+    raster_width = raster_profile.get("width")
+    raster_height = raster_profile.get("height")
+
+    raster_array = np.zeros((raster_height, raster_width))
+
+    out_array = _point_to_raster(raster_array, raster_profile, geodataframe, attribute, radius, buffer)
+
+    return out_array, raster_profile
diff --git a/eis_toolkit/training_data_tools/random_sampling.py b/eis_toolkit/training_data_tools/random_sampling.py
new file mode 100644
index 00000000..82114d56
--- /dev/null
+++ b/eis_toolkit/training_data_tools/random_sampling.py
@@ -0,0 +1,90 @@
+from numbers import Number
+
+import geopandas as gpd
+import numpy as np
+import rasterio
+import rasterio.transform
+from beartype import beartype
+from beartype.typing import Tuple, Union
+from rasterio import profiles
+from shapely.geometry import Point
+
+from eis_toolkit.exceptions import EmptyDataException
+
+
+def _random_sampling(
+    indices: np.ndarray,
+    values: np.ndarray,
+    sample_number: Number,
+    random_seed: int,
+) -> np.ndarray:
+
+    indices_negatives = indices[values == 0]
+
+    total_negatives = min(indices_negatives.size, sample_number)
+
+    np.random.seed(random_seed)
+    negative_indices = np.random.choice(indices_negatives.shape[0], total_negatives, replace=False)
+    Negative_sample = indices_negatives[negative_indices]
+
+    return Negative_sample
+
+
+@beartype
+def generate_negatives(
+    raster_array: np.ndarray,
+    raster_meta: Union[profiles.Profile, dict],
+    sample_number: Number,
+    random_seed: int = 48,
+) -> Tuple[gpd.GeoDataFrame, np.ndarray, Union[profiles.Profile, dict]]:
+    """Generate probable negatives from raster array with marked positives.
+
+    Args:
+        raster_array: Raster array with marked positives.
+        raster_meta: Raster metadata.
+        sample_number: maximum number of negatives to be generated.
+        random_seed: Seed for generating random negatives.
+
+    Returns:
+        A tuple containing the shapely points, output raster as a NumPy array and updated metadata.
+
+    Raises:
+        EmptyDataException:  The raster array is empty.
+    """
+
+    if raster_array.size == 0:
+        raise EmptyDataException
+
+    out_array = np.copy(raster_array)
+
+    total_rows = out_array.shape[0]
+    total_cols = out_array.shape[1]
+
+    indices = np.arange(total_rows * total_cols)
+
+    indices = indices.reshape(-1, 1)
+
+    values = out_array.reshape(-1, 1)
+
+    sampled_negatives = _random_sampling(
+        indices=indices, values=values, sample_number=sample_number, random_seed=random_seed
+    )
+
+    sampled_negatives = sampled_negatives.reshape(1, -1)
+
+    row = sampled_negatives // total_cols
+    row = row[0]
+
+    col = np.mod(sampled_negatives, total_cols)
+    col = col[0]
+
+    out_array[row, col] = -1
+
+    x, y = rasterio.transform.xy(raster_meta["transform"], row, col)
+
+    points = [Point(x[i], y[i]) for i in range(len(x))]
+
+    sample_negative = gpd.GeoDataFrame(geometry=points)
+    sample_negative.set_crs(raster_meta["crs"], allow_override=True, inplace=True)
+
+    return sample_negative, out_array, raster_meta
diff --git a/notebooks/testing_points_to_raster.ipynb b/notebooks/testing_points_to_raster.ipynb
new file mode 100644
index 00000000..ffeddb61
--- /dev/null
+++ b/notebooks/testing_points_to_raster.ipynb
@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "import rasterio\n",
+    "from rasterio import plot\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import textwrap\n",
+    "\n",
+    "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n",
+    "from eis_toolkit.training_data_tools.points_to_raster import points_to_raster\n",
+    "\n",
+    "PATH_LABELS_GPKG = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n",
+    "RASTER_PATH = \"../tests/data/remote/small_raster.tif\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating data points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      id  value                        geometry\n",
+      "0    1.0   10.0  POINT (384758.306 6671365.922)\n",
+      "1    2.0   20.0  POINT (384769.919 6671362.194)\n",
+      "2    3.0   30.0  POINT (384769.059 6671350.580)\n",
+      "3    4.0   40.0  POINT (384777.519 6671344.128)\n",
+      "4    5.0   35.0  POINT (384761.030 6671342.264)\n",
+      "5    6.0   25.0  POINT (384758.306 6671355.025)\n",
+      "6    7.0   50.0  POINT (384783.397 6671365.205)\n",
+      "7    8.0  100.0  POINT (384795.584 6671358.179)\n",
+      "8    9.0  120.0  POINT (384795.728 6671344.988)\n",
+      "9   11.0   79.0  POINT (384786.408 6671335.955)\n",
+      "10  12.0   60.0  POINT (384780.243 6671327.783)\n",
+      "11  13.0   50.0  POINT (384771.497 6671320.040)\n",
+      "12   NaN   40.0  POINT (384791.797 6671314.259)\n",
+      "13   NaN   30.0  POINT (384799.487 6671303.044)\n",
+      "14   NaN   20.0  POINT (384808.459 6671290.707)\n",
+      "15   NaN   30.0  POINT (384787.791 6671295.033)\n",
+      "16   NaN   30.0  POINT (384815.509 6671310.254)\n",
+      "17   NaN   20.0  POINT (384826.244 6671297.436)\n",
+      "18   NaN   15.0  POINT (384825.763 6671287.503)\n",
+      "19   NaN   10.0  POINT (384815.349 6671280.453)\n",
+      "20   NaN   13.0  POINT (384797.404 6671281.895)\n",
+      "21   NaN   32.0  POINT (384779.941 6671288.785)\n",
+      "22   NaN   35.0  POINT (384765.521 6671282.216)\n",
+      "23   NaN   45.0  POINT (384768.084 6671297.917)\n",
+      "24   NaN   50.0  POINT (384762.637 6671305.608)\n",
+      "25   NaN   55.0  POINT (384759.913 6671316.182)\n",
+      "26   NaN   60.0  POINT (384749.820 6671310.414)\n",
+      "27   NaN   65.0  POINT (384750.941 6671299.840)\n",
+      "28   NaN   40.0  POINT (384753.665 6671287.343)\n",
+      "29   NaN  100.0  POINT (384815.028 6671333.325)\n",
+      "30   NaN   90.0  POINT (384801.410 6671331.723)\n",
+      "31   NaN   70.0  POINT (384792.438 6671325.795)\n",
+      "32   NaN  120.0  POINT (384812.144 6671349.828)\n",
+      "33   NaN  100.0  POINT (384807.338 6671365.689)\n",
+      "34   NaN   80.0  POINT (384796.443 6671373.380)\n",
+      "35   NaN   30.0  POINT (384778.499 6671375.623)\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf = gpd.read_file(PATH_LABELS_GPKG)\n",
+    "print(gdf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating binary raster from template raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_binary_raster_from_template_raster(template_raster_path):\n",
+    "\n",
+    "    attribute = 'value'\n",
+    "    radius = 3\n",
+    "    \n",
+    "    with rasterio.open(template_raster_path) as temp_raster:\n",
+    "\n",
+    "        raster_profile = temp_raster.profile\n",
+    "        \n",
+    "        outarray,outmeta = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile)\n",
+    "        \n",
+    "        outarray2,outmeta2 = points_to_raster(geodataframe=gdf,\n",
+    "                                            attribute=attribute,\n",
+    "                                            raster_profile=raster_profile)\n",
+    "        \n",
+    "        outarray3,outmeta3 = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile,\n",
+    "                                            radius=radius)\n",
+    "        \n",
+    "        outarray4,outmeta4 = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile,\n",
+    "                                            attribute=attribute,\n",
+    "                                            radius=radius)\n",
+    "        \n",
+    "        outarray5,outmeta5 = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile,\n",
+    "                                            attribute=attribute,\n",
+    "                                            radius=radius,\n",
+    "                                            buffer='avg')\n",
+    "        \n",
+    "        outarray6,outmeta6 = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile,\n",
+    "                                            attribute=attribute,\n",
+    "                                            radius=radius,\n",
+    "                                            buffer='min')\n",
+    "        \n",
+    "        outarray7,outmeta7 = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile,\n",
+    "                                            attribute=attribute,\n",
+    "                                            radius=radius,\n",
+    "                                            buffer='max')\n",
+    "\n",
+    "        extent = [temp_raster.bounds.left, temp_raster.bounds.right,\n",
+    "                  temp_raster.bounds.bottom, temp_raster.bounds.top]\n",
+    "\n",
+    "        fig,axes = plt.subplots(3,3,figsize=(20,15))\n",
+    "        norm = plt.Normalize(vmax=np.nanmax(outarray), vmin=np.nanmin(outarray))\n",
+    "        norm2 = plt.Normalize(vmax=np.nanmax(outarray2), vmin=np.nanmin(outarray2))\n",
+    "\n",
+    "        plot.show(temp_raster, ax=axes[0][0])\n",
+    "        axes[0][0].set_title('Template Raster with geodataframe.', fontsize=15)\n",
+    "        points = gdf.cx[\n",
+    "                temp_raster.bounds.left : temp_raster.bounds.right,  # noqa: E203\n",
+    "                temp_raster.bounds.bottom : temp_raster.bounds.top,  # noqa: E203\n",
+    "            ]\n",
+    "        points.plot(ax=axes[0][0], facecolor='w', edgecolor='w')\n",
+    "\n",
+    "        im1 = axes[0][1].imshow(outarray, cmap='gray', norm=norm, extent=extent)\n",
+    "        axes[0][1].set_title(textwrap.fill('True geodataframe converted to raster.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im1, ax=axes[0][1], orientation='vertical')\n",
+    "\n",
+    "        im2 = axes[0][2].imshow(outarray2, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[0][2].set_title(textwrap.fill('True geodataframe converted to raster with attribute.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im2, ax=axes[0][2], orientation='vertical')\n",
+    "\n",
+    "        im3 = axes[1][0].imshow(outarray3, cmap='gray', norm=norm, extent=extent)\n",
+    "        axes[1][0].set_title(textwrap.fill('True geodataframe converted to raster with radius.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im3, ax=axes[1][0], orientation='vertical')\n",
+    "\n",
+    "        im4 = axes[1][1].imshow(outarray4, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[1][1].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im4, ax=axes[1][1], orientation='vertical')\n",
+    "\n",
+    "        im5 = axes[1][2].imshow(outarray5, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[1][2].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and averaging buffer.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im5, ax=axes[1][2], orientation='vertical')\n",
+    "        \n",
+    "        im6 = axes[2][0].imshow(outarray6, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[2][0].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and min buffer.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im6, ax=axes[2][0], orientation='vertical')\n",
+    "\n",
+    "        im7 = axes[2][1].imshow(outarray7, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[2][1].set_title(textwrap.fill('True geodataframe converted to raster with attribute and radius and max buffer.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im7, ax=axes[2][1], orientation='vertical')\n",
+    "\n",
+    "        axes[2][2].axis('off')\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_binary_raster_from_template_raster(SMALL_RASTER_PATH)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/testing_random_sampling.ipynb b/notebooks/testing_random_sampling.ipynb
new file mode 100644
index 00000000..64d7f5be
--- /dev/null
+++ b/notebooks/testing_random_sampling.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.insert(0, \"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import geopandas as gpd\n",
+    "import rasterio\n",
+    "from rasterio import plot\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import textwrap\n",
+    "\n",
+    "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n",
+    "from eis_toolkit.training_data_tools.points_to_raster import points_to_raster\n",
+    "from eis_toolkit.training_data_tools.random_sampling import generate_negatives\n",
+    "\n",
+    "PATH_LABELS_GPKG = \"../tests/data/remote/interpolating/interpolation_test_data_small.gpkg\"\n",
+    "RASTER_PATH = \"../tests/data/remote/small_raster.tif\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      id  value                        geometry\n",
+      "0    1.0   10.0  POINT (384758.306 6671365.922)\n",
+      "1    2.0   20.0  POINT (384769.919 6671362.194)\n",
+      "2    3.0   30.0  POINT (384769.059 6671350.580)\n",
+      "3    4.0   40.0  POINT (384777.519 6671344.128)\n",
+      "4    5.0   35.0  POINT (384761.030 6671342.264)\n",
+      "5    6.0   25.0  POINT (384758.306 6671355.025)\n",
+      "6    7.0   50.0  POINT (384783.397 6671365.205)\n",
+      "7    8.0  100.0  POINT (384795.584 6671358.179)\n",
+      "8    9.0  120.0  POINT (384795.728 6671344.988)\n",
+      "9   11.0   79.0  POINT (384786.408 6671335.955)\n",
+      "10  12.0   60.0  POINT (384780.243 6671327.783)\n",
+      "11  13.0   50.0  POINT (384771.497 6671320.040)\n",
+      "12   NaN   40.0  POINT (384791.797 6671314.259)\n",
+      "13   NaN   30.0  POINT (384799.487 6671303.044)\n",
+      "14   NaN   20.0  POINT (384808.459 6671290.707)\n",
+      "15   NaN   30.0  POINT (384787.791 6671295.033)\n",
+      "16   NaN   30.0  POINT (384815.509 6671310.254)\n",
+      "17   NaN   20.0  POINT (384826.244 6671297.436)\n",
+      "18   NaN   15.0  POINT (384825.763 6671287.503)\n",
+      "19   NaN   10.0  POINT (384815.349 6671280.453)\n",
+      "20   NaN   13.0  POINT (384797.404 6671281.895)\n",
+      "21   NaN   32.0  POINT (384779.941 6671288.785)\n",
+      "22   NaN   35.0  POINT (384765.521 6671282.216)\n",
+      "23   NaN   45.0  POINT (384768.084 6671297.917)\n",
+      "24   NaN   50.0  POINT (384762.637 6671305.608)\n",
+      "25   NaN   55.0  POINT (384759.913 6671316.182)\n",
+      "26   NaN   60.0  POINT (384749.820 6671310.414)\n",
+      "27   NaN   65.0  POINT (384750.941 6671299.840)\n",
+      "28   NaN   40.0  POINT (384753.665 6671287.343)\n",
+      "29   NaN  100.0  POINT (384815.028 6671333.325)\n",
+      "30   NaN   90.0  POINT (384801.410 6671331.723)\n",
+      "31   NaN   70.0  POINT (384792.438 6671325.795)\n",
+      "32   NaN  120.0  POINT (384812.144 6671349.828)\n",
+      "33   NaN  100.0  POINT (384807.338 6671365.689)\n",
+      "34   NaN   80.0  POINT (384796.443 6671373.380)\n",
+      "35   NaN   30.0  POINT (384778.499 6671375.623)\n"
+     ]
+    }
+   ],
+   "source": [
+    "gdf = gpd.read_file(PATH_LABELS_GPKG)\n",
+    "print(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_samples(template_raster_path):\n",
+    "    \n",
+    "    with rasterio.open(template_raster_path) as temp_raster:\n",
+    "        raster_profile = temp_raster.profile\n",
+    "        \n",
+    "        outarray,outmeta = points_to_raster(geodataframe=gdf,\n",
+    "                                            raster_profile=raster_profile)\n",
+    "        \n",
+    "        sampled_negatives, outarray2, _ = generate_negatives(raster_array=outarray,\n",
+    "                                                 raster_meta=outmeta,\n",
+    "                                                 sample_number=10,\n",
+    "                                                 random_seed=30)\n",
+    "\n",
+    "        extent = [temp_raster.bounds.left, temp_raster.bounds.right,\n",
+    "                  temp_raster.bounds.bottom, temp_raster.bounds.top]\n",
+    "\n",
+    "        fig,axes = plt.subplots(2,2,figsize=(20,15))\n",
+    "        norm = plt.Normalize(vmax=np.nanmax(outarray), vmin=np.nanmin(outarray))\n",
+    "        norm2 = plt.Normalize(vmax=np.nanmax(outarray2), vmin=np.nanmin(outarray2))\n",
+    "\n",
+    "        plot.show(temp_raster, ax=axes[0][0])\n",
+    "        axes[0][0].set_title('Template Raster with positives', fontsize=15)\n",
+    "        points = gdf.cx[\n",
+    "                temp_raster.bounds.left : temp_raster.bounds.right,  \n",
+    "                temp_raster.bounds.bottom : temp_raster.bounds.top,  \n",
+    "            ]\n",
+    "        points.plot(ax=axes[0][0], facecolor='w', edgecolor='w')\n",
+    "\n",
+    "        im1 = axes[0][1].imshow(outarray, cmap='gray', norm=norm, extent=extent)\n",
+    "        axes[0][1].set_title(textwrap.fill('True positives converted to raster.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im1, ax=axes[0][1], orientation='vertical')\n",
+    "\n",
+    "        plot.show(temp_raster, ax=axes[1][0])\n",
+    "        axes[1][0].set_title('Template Raster with positives and random negatives', fontsize=15)\n",
+    "        sampled_negatives.plot(ax=axes[1][0], facecolor='r', edgecolor='r')\n",
+    "        points.plot(ax=axes[1][0], facecolor='w', edgecolor='w')\n",
+    "\n",
+    "        im1 = axes[1][1].imshow(outarray2, cmap='gray', norm=norm2, extent=extent)\n",
+    "        axes[1][1].set_title(textwrap.fill('True positives and sampled negatives converted to raster.', width=30), fontsize=15)\n",
+    "        fig.colorbar(im1, ax=axes[1][1], orientation='vertical')\n",
+    "\n",
+    "        plt.tight_layout()\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2000x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_samples(SMALL_RASTER_PATH)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "eis_toolkit_development_platform_eis_not_installed_py3.9",
+   "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": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg
index dbc9cf3c..7b741f58 100644
Binary files a/tests/data/remote/interpolating/interpolation_test_data_small.gpkg and b/tests/data/remote/interpolating/interpolation_test_data_small.gpkg differ
diff --git a/tests/training_data_tools/points_to_raster_test.py b/tests/training_data_tools/points_to_raster_test.py
new file mode 100644
index 00000000..11a1b04d
--- /dev/null
+++ b/tests/training_data_tools/points_to_raster_test.py
@@ -0,0 +1,43 @@
+from pathlib import Path
+
+import geopandas as gpd
+import pytest
+import rasterio
+
+from eis_toolkit.exceptions import NonMatchingCrsException
+from eis_toolkit.training_data_tools.points_to_raster import points_to_raster
+from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH
+
+test_dir = Path(__file__).parent.parent
+PATH_LABELS_GPKG = test_dir.joinpath("data/remote/interpolating/interpolation_test_data_small.gpkg")
+
+geodataframe = gpd.read_file(PATH_LABELS_GPKG)
+
+
+@pytest.mark.parametrize("geodataframe", [geodataframe])  # Case where CRS matches
+def test_points_to_raster(geodataframe):
+    """Test that points_to_raster function works as expected."""
+    with rasterio.open(SMALL_RASTER_PATH) as temp_raster:
+
+        raster_profile = temp_raster.profile
+
+        outarray, outmeta = points_to_raster(
+            geodataframe=geodataframe, attribute="value", raster_profile=raster_profile
+        )
+
+        assert outarray.shape == (
+            temp_raster.height,
+            temp_raster.width,
+        ), f"Expected output array shape {(temp_raster.height,temp_raster.width)} but got {outarray.shape}"
+
+
+@pytest.mark.parametrize("geodataframe", [geodataframe.to_crs(epsg=4326)])  # Case where CRS do not matches
+def test_non_matching_crs_error(geodataframe):
+    """Test that different crs raises NonMatchingCrsException."""
+
+    with pytest.raises(NonMatchingCrsException):
+        with rasterio.open(SMALL_RASTER_PATH) as temp_raster:
+            raster_profile = temp_raster.profile
+            outarray, outmeta = points_to_raster(
+                geodataframe=geodataframe, attribute="value", raster_profile=raster_profile
+            )
diff --git a/tests/training_data_tools/random_sampling_test.py b/tests/training_data_tools/random_sampling_test.py
new file mode 100644
index 00000000..c1a2da56
--- /dev/null
+++ b/tests/training_data_tools/random_sampling_test.py
@@ -0,0 +1,38 @@
+from pathlib import Path
+
+import geopandas as gpd
+import pytest
+import rasterio
+
+from eis_toolkit.training_data_tools.points_to_raster import points_to_raster
+from eis_toolkit.training_data_tools.random_sampling import generate_negatives
+from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH
+
+test_dir = Path(__file__).parent.parent
+PATH_LABELS_GPKG = test_dir.joinpath("data/remote/interpolating/interpolation_test_data_small.gpkg")
+
+gdf = gpd.read_file(PATH_LABELS_GPKG)
+
+
+@pytest.mark.parametrize("geodataframe", [gdf])
+def test_points_to_raster(geodataframe):
+    """Test that generate_negatives function works as expected."""
+    with rasterio.open(SMALL_RASTER_PATH) as temp_raster:
+        raster_profile = temp_raster.profile
+
+        outarray, outmeta = points_to_raster(geodataframe=gdf, attribute="value", raster_profile=raster_profile)
+
+        sampled_negatives, outarray2, outmeta2 = generate_negatives(
+            raster_array=outarray, raster_meta=outmeta, sample_number=10, random_seed=30
+        )
+
+        row, col = rasterio.transform.rowcol(
+            outmeta2["transform"], sampled_negatives.geometry.x, sampled_negatives.geometry.y
+        )
+
+        assert outarray2.shape == (
+            temp_raster.height,
+            temp_raster.width,
+        ), f"Expected output array shape {(temp_raster.height, temp_raster.width)} but got {outarray2.shape}"
+
+        assert (outarray2[row, col] == -1).all()