diff --git a/lib/clone.dart b/lib/clone.dart
new file mode 100644
index 00000000..46e15795
--- /dev/null
+++ b/lib/clone.dart
@@ -0,0 +1,4 @@
+library turf_clone;
+
+export 'package:geotypes/geotypes.dart';
+export 'src/clone.dart';
\ No newline at end of file
diff --git a/lib/dbscan.dart b/lib/dbscan.dart
new file mode 100644
index 00000000..6753d46d
--- /dev/null
+++ b/lib/dbscan.dart
@@ -0,0 +1,4 @@
+library turf_dbscan;
+
+import 'package:geotypes/geotypes.dart';
+import 'src/dbscan.dart';
diff --git a/lib/src/clone.dart b/lib/src/clone.dart
new file mode 100644
index 00000000..90a41617
--- /dev/null
+++ b/lib/src/clone.dart
@@ -0,0 +1,106 @@
+
+// Deep clone any GeoJSON object: FeatureCollection, Feature, Geometry, and Properties.
+
+dynamic clone(dynamic geojson) {
+  if (geojson == null) {
+    throw ArgumentError('geojson is required');
+  }
+
+  switch (geojson['type']) {
+    case 'Feature':
+      return cloneFeature(geojson);
+    case 'FeatureCollection':
+      return cloneFeatureCollection(geojson);
+    case 'Point':
+    case 'LineString':
+    case 'Polygon':
+    case 'MultiPoint':
+    case 'MultiLineString':
+    case 'MultiPolygon':
+    case 'GeometryCollection':
+      return cloneGeometry(geojson);
+    default:
+      throw ArgumentError('unknown GeoJSON type');
+  }
+}
+
+Map<String, dynamic> cloneFeature(Map<String, dynamic> geojson) {
+  final cloned = <String, dynamic>{'type': 'Feature'};
+
+  // Preserve foreign members
+  geojson.forEach((key, value) {
+    if (key != 'type' && key != 'properties' && key != 'geometry') {
+      cloned[key] = value;
+    }
+  });
+
+  cloned['properties'] = cloneProperties(geojson['properties']);
+  cloned['geometry'] = geojson['geometry'] == null
+      ? null
+      : cloneGeometry(geojson['geometry']);
+
+  return cloned;
+}
+
+dynamic cloneProperties(dynamic properties) {
+  if (properties == null) return {};
+
+  final cloned = <String, dynamic>{};
+
+  (properties as Map<String, dynamic>).forEach((key, value) {
+    if (value is Map) {
+      cloned[key] = cloneProperties(value);
+    } else if (value is List) {
+      cloned[key] = List.from(value);
+    } else {
+      cloned[key] = value;
+    }
+  });
+
+  return cloned;
+}
+
+Map<String, dynamic> cloneFeatureCollection(Map<String, dynamic> geojson) {
+  final cloned = <String, dynamic>{'type': 'FeatureCollection'};
+
+  // Preserve foreign members
+  geojson.forEach((key, value) {
+    if (key != 'type' && key != 'features') {
+      cloned[key] = value;
+    }
+  });
+
+  cloned['features'] =
+      (geojson['features'] as List).map((f) => cloneFeature(f)).toList();
+
+  return cloned;
+}
+
+Map<String, dynamic> cloneGeometry(Map<String, dynamic> geometry) {
+  final geom = <String, dynamic>{'type': geometry['type']};
+
+  if (geometry.containsKey('bbox')) {
+    geom['bbox'] = List.from(geometry['bbox']);
+  }
+
+  if (geometry['type'] == 'GeometryCollection') {
+    geom['geometries'] = (geometry['geometries'] as List)
+        .map((g) => cloneGeometry(g))
+        .toList();
+  } else {
+    geom['coordinates'] = deepSlice(geometry['coordinates']);
+  }
+
+  return geom;
+}
+
+dynamic deepSlice(dynamic coords) {
+  if (coords is List) {
+    if (coords.isEmpty || coords[0] is! List) {
+      return List.from(coords);
+    } else {
+      return coords.map((c) => deepSlice(c)).toList();
+    }
+  }
+  return coords;
+}
diff --git a/lib/src/dbscan.dart b/lib/src/dbscan.dart
new file mode 100644
index 00000000..23f6dfe4
--- /dev/null
+++ b/lib/src/dbscan.dart
@@ -0,0 +1,136 @@
+import 'package:turf/clone.dart';
+import 'package:turf/distance.dart';
+import 'package:turf/bbox.dart' as turf_bbox;
+import 'package:rbush/rbush.dart';
+
+// DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm.
+// Given a set of points in some space, it groups together points that are closely packed together
+// (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions.
+
+// A wrapper class to make GeoJSON features compatible with RBush spatial indexing.
+class SpatialFeature extends RBushElement<Feature<Point>> {
+    final Feature<Point> feature;
+
+    SpatialFeature(this.feature)
+        : assert(feature.bbox != null && feature.bbox!.length >= 4, 'Feature must have a bbox'),
+          super(
+            minX: feature.bbox![0]!.toDouble(),
+            minY: feature.bbox![1]!.toDouble(),
+            maxX: feature.bbox![2]!.toDouble(),
+            maxY: feature.bbox![3]!.toDouble(),
+            data: feature,
+          );
+}
+
+FeatureCollection<Point> dbscan(
+    FeatureCollection<Point> points,
+    int maxClusterLength,
+    int minPoints,
+    double maxRadius, {
+    bool mutableInput = true,
+  }) {
+    if (minPoints <= 0) {
+      throw ArgumentError('minPoints must be greater than 0');
+    }
+    if (maxRadius < 0) {
+      throw ArgumentError('maxRadius must be greater than or equal to 0');
+    }
+
+    final numberOfPoints = points.features.length;
+    final clustered = mutableInput ? points : clone(points);
+    final visited = List<bool>.filled(numberOfPoints, false);
+    final noise = List<bool>.filled(numberOfPoints, false);
+    int clusterId = 0;
+
+    // Ensure all features have a bounding box
+    for (final feature in clustered.features) {
+      if (feature.geometry != null && feature.bbox == null) {
+        feature.bbox = turf_bbox.bbox(feature);
+      }
+    }
+
+    // Build an R-tree index for efficient neighbor searching
+    final tree = RBush<Feature<Point>>();
+    for (int i = 0; i < numberOfPoints; i++) {
+      final feature = clustered.features[i];
+      if (feature.geometry != null && feature.bbox != null) {
+        tree.insert(SpatialFeature(feature));
+      }
+    }
+
+    // Function to find neighbors within a given radius
+    List<int> getNeighbors(int pointIndex) {
+        final neighbors = <int>[];
+        final targetPoint = clustered.features[pointIndex];
+        if (targetPoint.geometry == null || targetPoint.bbox == null) {
+            return neighbors;
+        }
+
+        final envelope = RBushBox(
+            minX: targetPoint.bbox![0] - maxRadius,
+            minY: targetPoint.bbox![1] - maxRadius,
+            maxX: targetPoint.bbox![2] + maxRadius,
+            maxY: targetPoint.bbox![3] + maxRadius,
+        );
+
+        final potentialNeighbors = tree.search(envelope);
+      for (final wrapped in potentialNeighbors) {
+          final spatialFeature = wrapped as SpatialFeature;
+          final neighborFeature = spatialFeature.feature;
+          final neighborIndex = clustered.features.indexOf(neighborFeature);
+          if (pointIndex != neighborIndex) {
+            final dist = distance(targetPoint.geometry!, neighborFeature.geometry!);
+            if (dist <= maxRadius) {
+              neighbors.add(neighborIndex);
+            }
+          }
+        }
+        return neighbors;
+      }
+
+  // Expand the cluster recursively
+  void expandCluster(int pointIndex, List<int> neighbors) {
+      visited[pointIndex] = true;
+      clustered.features[pointIndex].properties['cluster'] = clusterId;
+
+      int i = 0;
+      while (i < neighbors.length) {
+          final neighborIndex = neighbors[i];
+          if (!visited[neighborIndex]) {
+              visited[neighborIndex] = true;
+              clustered.features[neighborIndex].properties['cluster'] = clusterId;
+              final newNeighbors = getNeighbors(neighborIndex);
+              if (newNeighbors.length >= minPoints) {
+                  neighbors.addAll(newNeighbors.where((n) => !neighbors.contains(n)));
+              }
+          }
+          i++;
+        }
+      }
+
+  // Iterate through each point
+  for (int i = 0; i < numberOfPoints; i++) {
+      if (!visited[i]) {
+          final neighbors = getNeighbors(i);
+          if (neighbors.length < minPoints) {
+              noise[i] = true;
+          } else {
+              expandCluster(i, neighbors);
+              clusterId++;
+              if (clusterId > maxClusterLength) {
+                  throw ArgumentError(
+                    'Cluster exceeded maxClusterLength ($maxClusterLength)');
+              }
+          }
+      }
+  }
+
+    // Mark noise points with null cluster
+    for (int i = 0; i < numberOfPoints; i++) {
+        if (noise[i]) {
+            clustered.features[i].properties['cluster'] = null;
+        }
+    }
+
+    return clustered;
+}
\ No newline at end of file
diff --git a/lib/turf.dart b/lib/turf.dart
index 1ee09fcc..64616916 100644
--- a/lib/turf.dart
+++ b/lib/turf.dart
@@ -10,7 +10,9 @@ export 'boolean.dart';
 export 'center.dart';
 export 'centroid.dart';
 export 'clean_coords.dart';
+export 'clone.dart';
 export 'clusters.dart';
+export 'dbscan.dart';
 export 'destination.dart';
 export 'distance.dart';
 export 'explode.dart';
diff --git a/test/components/clone_test.dart b/test/components/clone_test.dart
new file mode 100644
index 00000000..d46776fe
--- /dev/null
+++ b/test/components/clone_test.dart
@@ -0,0 +1,112 @@
+import 'package:test/test.dart';
+import 'package:turf/clone.dart'; // Adjust path to where your `clone` function lives
+
+void main() {
+  group('GeoJSON clone tests', () {
+    test('Clones a simple Point feature', () {
+      final input = {
+        "type": "Feature",
+        "geometry": {
+          "type": "Point",
+          "coordinates": [102.0, 0.5]
+        },
+        "properties": {"prop0": "value0"}
+      };
+
+      final result = clone(input);
+
+      expect(result, equals(input));
+      expect(identical(result, input), isFalse); // Ensure it's a deep clone
+    });
+
+    test('Clones a LineString feature with properties', () {
+      final input = {
+        "type": "Feature",
+        "geometry": {
+          "type": "LineString",
+          "coordinates": [
+            [102.0, 0.0],
+            [103.0, 1.0],
+            [104.0, 0.0],
+            [105.0, 1.0]
+          ]
+        },
+        "properties": {"stroke": "blue", "opacity": 0.6}
+      };
+
+      final result = clone(input);
+
+      expect(result, equals(input));
+      expect(result['properties'], isNot(same(input['properties'])));
+    });
+
+    test('Clones a FeatureCollection', () {
+      final input = {
+        "type": "FeatureCollection",
+        "features": [
+          {
+            "type": "Feature",
+            "geometry": {
+              "type": "Point",
+              "coordinates": [102.0, 0.5]
+            },
+            "properties": {"prop0": "value0"}
+          },
+          {
+            "type": "Feature",
+            "geometry": {
+              "type": "LineString",
+              "coordinates": [
+                [102.0, 0.0],
+                [103.0, 1.0]
+              ]
+            },
+            "properties": {"prop1": "value1"}
+          }
+        ]
+      };
+
+      final result = clone(input);
+
+      expect(result, equals(input));
+      expect(result['features'][0], isNot(same(input['features'][0])));
+    });
+
+    test('Clones a GeometryCollection', () {
+      final input = {
+        "type": "GeometryCollection",
+        "geometries": [
+          {
+            "type": "Point",
+            "coordinates": [100.0, 0.0]
+          },
+          {
+            "type": "LineString",
+            "coordinates": [
+              [101.0, 0.0],
+              [102.0, 1.0]
+            ]
+          }
+        ]
+      };
+
+      final result = clone(input);
+
+      expect(result, equals(input));
+      expect(result['geometries'][1], isNot(same(input['geometries'][1])));
+    });
+
+    test('Throws error for null input', () {
+      expect(() => clone(null), throwsArgumentError);
+    });
+
+    test('Throws error for unknown GeoJSON type', () {
+      final input = {
+        "type": "UnknownThing",
+        "data": []
+      };
+
+      expect(() => clone(input), throwsArgumentError);
+    });
+  });
+}
diff --git a/test/components/dbscan_test.dart b/test/components/dbscan_test.dart
new file mode 100644
index 00000000..991a2451
--- /dev/null
+++ b/test/components/dbscan_test.dart
@@ -0,0 +1,67 @@
+import 'package:test/test.dart';
+import 'package:turf/turf.dart'; 
+import 'package:turf/dbscan.dart'; 
+
+void main() {
+  group('DBSCAN clustering', () {
+    test('clusters points correctly', () {
+      // Create some sample points that form two clusters
+      final points = FeatureCollection<Point>(features: [
+        Feature<Point>(geometry: Point(coordinates: Position(0, 0))),
+        Feature<Point>(geometry: Point(coordinates: Position(0.1, 0.1))),
+        Feature<Point>(geometry: Point(coordinates: Position(0.2, 0.2))),
+        Feature<Point>(geometry: Point(coordinates: Position(5, 5))),
+        Feature<Point>(geometry: Point(coordinates: Position(5.1, 5.1))),
+      ]);
+
+      // Run dbscan with parameters:
+      // maxClusterLength = 10 clusters max
+      // minPoints = 2 points needed to form a cluster
+      // maxRadius = 20000 meters (~20 km)
+      final clustered = dbscan(points, 10, 2, 20000);
+
+      // Check clusters assigned:
+      final clusters = clustered.features.map((f) => f.properties['cluster']).toList();
+
+      // Points 0,1,2 should have same cluster id (0)
+      expect(clusters[0], equals(0));
+      expect(clusters[1], equals(0));
+      expect(clusters[2], equals(0));
+
+      // Points 3,4 should have same cluster id (1)
+      expect(clusters[3], equals(1));
+      expect(clusters[4], equals(1));
+    });
+
+    test('noise points are marked with null cluster', () {
+      // Points spaced far apart, no clusters
+      final points = FeatureCollection<Point>(features: [
+        Feature<Point>(geometry: Point(coordinates: Position(0, 0))),
+        Feature<Point>(geometry: Point(coordinates: Position(10, 10))),
+      ]);
+
+      final clustered = dbscan(points, 10, 2, 1000); // maxRadius only 1 km
+
+      // Both points should be noise (cluster == null)
+      for (final feature in clustered.features) {
+        expect(feature.properties['cluster'], isNull);
+      }
+    });
+
+    test('throws error if minPoints <= 0', () {
+      final points = FeatureCollection<Point>(features: [
+        Feature<Point>(geometry: Point(coordinates: Position(0, 0))),
+      ]);
+
+      expect(() => dbscan(points, 10, 0, 1000), throwsArgumentError);
+    });
+
+    test('throws error if maxRadius < 0', () {
+      final points = FeatureCollection<Point>(features: [
+        Feature<Point>(geometry: Point(coordinates: Position(0, 0))),
+      ]);
+
+      expect(() => dbscan(points, 10, 1, -10), throwsArgumentError);
+    });
+  });
+}
\ No newline at end of file