better structure

backend/src/landmarks/cluster_manager.py

@@ -0,0 +1,300 @@
+"""Find clusters of interest to add more general areas of visit to the tour."""
+import logging
+from typing import Literal, Tuple
+
+import numpy as np
+from sklearn.cluster import DBSCAN
+from pydantic import BaseModel
+
+from ..overpass.overpass import Overpass, get_base_info
+from ..structs.landmark import Landmark
+from ..utils.get_time_distance import get_distance
+from ..utils.bbox import create_bbox
+
+
+
+# silence the overpass logger
+logging.getLogger('Overpass').setLevel(level=logging.CRITICAL)
+
+
+class Cluster(BaseModel):
+    """"
+    A class representing an interesting area for shopping or sightseeing.
+    
+    It can represent either a general area or a specifc route with start and end point.
+    The importance represents the number of shops found in this cluster.
+    
+    Attributes:
+        type :       either a 'street' or 'area' (representing a denser field of shops).
+        importance : size of the cluster (number of points).
+        centroid :   center of the cluster.
+        start :      if the type is a street it goes from here...
+        end :        ...to here
+    """
+    type: Literal['street', 'area']
+    importance: int
+    centroid: Tuple[float, float]
+    # start: Optional[list] = None      # for later use if we want to have streets as well
+    # end: Optional[list] = None
+
+
+class ClusterManager:
+    """
+    A manager responsible for clustering points of interest, such as shops or historic sites, 
+    to identify areas worth visiting. It uses the DBSCAN algorithm to detect clusters 
+    based on a set of points retrieved from OpenStreetMap (OSM).
+
+    Attributes:
+        logger (logging.Logger): Logger for capturing relevant events and errors.
+        valid (bool): Indicates whether clusters were successfully identified.
+        all_points (list): All points retrieved from OSM, representing locations of interest.
+        cluster_points (list): Points identified as part of a cluster.
+        cluster_labels (list): Labels corresponding to the clusters each point belongs to.
+        cluster_type (Literal['sightseeing', 'shopping']): Type of clustering, either for sightseeing 
+            landmarks or shopping areas.
+    """
+    logger = logging.getLogger(__name__)
+
+    # NOTE: all points are in (lat, lon) format
+    valid: bool             # Ensure the manager is valid (ie there are some clusters to be found)
+    all_points: list
+    cluster_points: list
+    cluster_labels: list
+    cluster_type: Literal['sightseeing', 'shopping']
+
+    def __init__(self, bbox: tuple, cluster_type: Literal['sightseeing', 'shopping']) -> None:
+        """
+        Upon intialization, generate the point cloud used for cluster detection.
+        The points represent bag/clothes shops and general boutiques.
+        If the first step is successful, it applies the DBSCAN clustering algorithm with different
+        parameters depending on the size of the city (number of points). 
+        It filters out noise points and keeps only the largest clusters.
+
+        A successful initialization updates:
+            - `self.cluster_points`: The points belonging to clusters.
+            - `self.cluster_labels`: The labels for the points in clusters.
+        
+        The method also calls `filter_clusters()` to retain only the largest clusters.
+
+        Args: 
+            bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
+        """
+        # Setup the caching in the Overpass class.
+        self.overpass = Overpass()
+
+        self.cluster_type = cluster_type
+        if cluster_type == 'shopping' :
+            osm_types = ['node']
+            sel = '"shop"~"^(bag|boutique|clothes)$"'
+            out = 'ids center'
+        elif cluster_type == 'sightseeing' :
+            osm_types = ['way']
+            sel = '"historic"~"^(monument|building|yes)$"'
+            out = 'ids center'
+        else :
+            raise NotImplementedError("Please choose only an available option for cluster detection")
+
+        # Initialize the points for cluster detection
+        try:
+            result = self.overpass.send_query(
+            bbox = bbox,
+            osm_types = osm_types,
+            selector = sel,
+            out = out
+        )
+        except Exception as e:
+            self.logger.error(f"Error fetching clusters: {e}")
+
+        if result is None :
+            self.logger.debug(f"Found no {cluster_type} clusters, overpass query returned no datapoints.")
+            self.valid = False
+
+        else :
+            points = []
+            for elem in result:
+                osm_type = elem.get('type')
+                
+                # Get coordinates and append them to the points list
+                _, coords = get_base_info(elem, osm_type)
+                if coords is not None :
+                    points.append(coords)
+
+            if points :
+                self.all_points = np.array(points)
+
+                # Apply DBSCAN to find clusters. Choose different settings for different cities.
+                if self.cluster_type == 'shopping' and len(self.all_points) > 200 :
+                    dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree')  # for large cities
+                elif self.cluster_type == 'sightseeing' :
+                    dbscan = DBSCAN(eps=0.0025, min_samples=15, algorithm='kd_tree')  # for historic neighborhoods
+                else :
+                    dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree')  # for small cities
+
+                labels = dbscan.fit_predict(self.all_points)
+
+                # Check that there are is least 1 cluster
+                if len(set(labels)) > 1 :
+                    self.logger.info(f"Found {len(set(labels))} different {cluster_type} clusters.")
+                    # Separate clustered points and noise points
+                    self.cluster_points = self.all_points[labels != -1]
+                    self.cluster_labels = labels[labels != -1]
+                    self.filter_clusters()      # ValueError here sometimes. I dont know why. # Filter the clusters to keep only the largest ones.
+                    self.valid = True
+
+                else :
+                    self.logger.info(f"Found 0 {cluster_type} clusters.")
+                    self.valid = False
+
+            else :
+                self.logger.debug(f"Detected 0 {cluster_type} clusters.")
+                self.valid = False
+
+
+    def generate_clusters(self) -> list[Landmark]:
+        """
+        Generate a list of landmarks based on identified clusters.
+
+        This method iterates over the different clusters, calculates the centroid 
+        (as the mean of the points within each cluster), and assigns an importance 
+        based on the size of the cluster.
+
+        The generated shopping locations are stored in `self.clusters` 
+        as a list of `Cluster` objects, each with:
+            - `type`: Set to 'area'.
+            - `centroid`: The calculated centroid of the cluster.
+            - `importance`: The number of points in the cluster.
+        """
+
+        if not self.valid :
+            return []       # Return empty list if no clusters were found
+
+        locations = []
+
+        # loop through the different clusters
+        for label in set(self.cluster_labels):
+
+            # Extract points belonging to the current cluster
+            current_cluster = self.cluster_points[self.cluster_labels == label]
+
+            # Calculate the centroid as the mean of the points
+            centroid = np.mean(current_cluster, axis=0)
+            centroid = tuple((round(centroid[0], 7), round(centroid[1], 7)))
+
+            if self.cluster_type == 'shopping' :
+                score = len(current_cluster)*3
+            else :
+                score = len(current_cluster)*15
+            locations.append(Cluster(
+                type='area',
+                centroid=centroid,
+                importance = score
+            ))
+
+        # Transform the locations in landmarks and return the list
+        cluster_landmarks = []
+        for cluster in locations :
+            cluster_landmarks.append(self.create_landmark(cluster))
+
+        return cluster_landmarks
+
+
+    def create_landmark(self, cluster: Cluster) -> Landmark:
+        """
+        Create a Landmark object based on the given shopping location.
+
+        This method queries the Overpass API for nearby neighborhoods and shopping malls 
+        within a 1000m radius around the shopping location centroid. It selects the closest 
+        result and creates a landmark with the associated details such as name, type, and OSM ID.
+
+        Parameters:
+            shopping_location (Cluster): A Cluster object containing 
+            the centroid and importance of the area.
+
+        Returns:
+            Landmark: A Landmark object containing details such as the name, type, 
+            location, attractiveness, and OSM details.
+        """
+
+        # Define the bounding box for a given radius around the coordinates
+        bbox = create_bbox(cluster.centroid, 300)
+        
+        # Query neighborhoods and shopping malls
+        selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"']
+
+        if self.cluster_type == 'shopping' :
+            selectors.append('"shop"="mall"')
+            new_name = 'Shopping Area'
+            t = 30
+        else :
+            new_name = 'Neighborhood'
+            t = 20
+
+        min_dist = float('inf')
+        osm_id = 0
+        osm_type = 'node'
+        osm_types = ['node', 'way', 'relation']
+
+        for sel in selectors :
+            try:
+                result = self.overpass.send_query(bbox = bbox,
+                                                  osm_types = osm_types,
+                                                  selector = sel,
+                                                  out = 'ids center tags'
+                                                  )
+            except Exception as e:
+                self.logger.error(f"Error fetching clusters: {e}")
+                continue
+
+            if result is None :
+                self.logger.error(f"Error fetching clusters: {e}")
+                continue
+
+            for elem in result:
+                # Get basic info
+                id, coords, name = get_base_info(elem, elem.get('type'), with_name=True)
+                if name is None or coords is None :
+                    continue
+
+                d = get_distance(cluster.centroid, coords)
+                if  d < min_dist :
+                    min_dist = d
+                    new_name = name             # add name
+                    osm_type = elem.get('type') # add type: 'way' or 'relation'
+                    osm_id = id                 # add OSM id
+
+        return Landmark(
+            name=new_name,
+            type=self.cluster_type,
+            location=cluster.centroid,              # later: use the fact the we can also recognize streets.
+            attractiveness=cluster.importance,
+            n_tags=0,
+            osm_id=osm_id,
+            osm_type=osm_type,
+            duration=t
+        )
+
+
+    def filter_clusters(self):
+        """
+        Filter clusters to retain only the 5 largest clusters by point count.
+
+        This method calculates the size of each cluster and filters out all but the 
+        5 largest clusters. It then updates the cluster points and labels to reflect 
+        only those from the top 5 clusters.
+        """
+        label_counts = np.bincount(self.cluster_labels)
+
+        # Step 3: Get the indices (labels) of the 5 largest clusters
+        top_5_labels = np.argsort(label_counts)[-5:]  # Get the largest 5 clusters
+
+        # Step 4: Filter points to keep only the points in the top 5 clusters
+        filtered_cluster_points = []
+        filtered_cluster_labels = []
+
+        for label in top_5_labels:
+            filtered_cluster_points.append(self.cluster_points[self.cluster_labels == label])
+            filtered_cluster_labels.append(np.full((label_counts[label],), label))  # Replicate the label
+
+        # update the cluster points and labels with the filtered data
+        self.cluster_points = np.vstack(filtered_cluster_points)        # ValueError here
+        self.cluster_labels = np.concatenate(filtered_cluster_labels)

backend/src/landmarks/landmarks_manager.py

@@ -0,0 +1,409 @@
+"""Module used to import data from OSM and arrange them in categories."""
+import logging
+import yaml
+
+from ..structs.preferences import Preferences
+from ..structs.landmark import Landmark
+from ..utils.take_most_important import take_most_important
+from .cluster_manager import ClusterManager
+from ..overpass.overpass import Overpass, get_base_info
+from ..utils.bbox import create_bbox
+
+from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH
+
+
+class LandmarkManager:
+    """
+    Use this to manage landmarks.
+    Uses the overpass api to fetch landmarks and classify them.
+    """
+    logger = logging.getLogger(__name__)
+
+    radius_close_to: int    # radius in meters
+    church_coeff: float     # coeff to adjsut score of churches
+    nature_coeff: float       # coeff to adjust score of parks
+    overall_coeff: float        # coeff to adjust weight of tags
+    n_important: int        # number of important landmarks to consider
+
+
+    def __init__(self) -> None:
+
+        with AMENITY_SELECTORS_PATH.open('r') as f:
+            self.amenity_selectors = yaml.safe_load(f)
+
+        with LANDMARK_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            self.max_bbox_side = parameters['max_bbox_side']
+            self.church_coeff = parameters['church_coeff']
+            self.nature_coeff = parameters['nature_coeff']
+            self.overall_coeff = parameters['overall_coeff']
+            self.tag_exponent = parameters['tag_exponent']
+            self.image_bonus = parameters['image_bonus']
+            self.wikipedia_bonus = parameters['wikipedia_bonus']
+            self.viewpoint_bonus = parameters['viewpoint_bonus']
+            self.pay_bonus = parameters['pay_bonus']
+            self.n_important = parameters['N_important']
+
+        with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+            parameters = yaml.safe_load(f)
+            self.walking_speed = parameters['average_walking_speed']
+            self.detour_factor = parameters['detour_factor']
+
+        # Setup the caching in the Overpass class.
+        self.overpass = Overpass()
+
+        self.logger.info('LandmakManager successfully initialized.')
+
+
+    def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) -> tuple[list[Landmark], list[Landmark]]:
+        """
+        Generate and prioritize a list of landmarks based on user preferences.
+
+        This method fetches landmarks from various categories (sightseeing, nature, shopping) based on the user's preferences
+        and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
+        landmarks based on a predefined criterion.
+
+        Args:
+        center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
+        preferences (Preferences): The user's preference settings that influence the landmark selection.
+
+        Returns:
+        tuple[list[Landmark], list[Landmark]]:
+        - A list of all existing landmarks.
+        - A list of the most important landmarks based on the user's preferences.
+        """
+        self.logger.debug('Starting to fetch landmarks...')
+        max_walk_dist = int((preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor)
+        radius = min(max_walk_dist, int(self.max_bbox_side/2))
+
+        # use set to avoid duplicates, this requires some __methods__ to be set in Landmark
+        all_landmarks = set()
+
+        # Create a bbox using the around technique, tuple of strings
+        bbox = create_bbox(center_coordinates, radius)
+
+        # list for sightseeing
+        if preferences.sightseeing.score != 0:
+            self.logger.debug('Fetching sightseeing landmarks...')
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, preferences.sightseeing.score)
+            all_landmarks.update(current_landmarks)
+            self.logger.info(f'Found {len(current_landmarks)} sightseeing landmarks')
+
+            # special pipeline for historic neighborhoods
+            neighborhood_manager = ClusterManager(bbox, 'sightseeing')
+            historic_clusters = neighborhood_manager.generate_clusters()
+            all_landmarks.update(historic_clusters)
+
+        # list for nature
+        if preferences.nature.score != 0:
+            self.logger.debug('Fetching nature landmarks...')
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, preferences.nature.score)
+            all_landmarks.update(current_landmarks)
+            self.logger.info(f'Found {len(current_landmarks)} nature landmarks')
+
+
+        # list for shopping
+        if preferences.shopping.score != 0:
+            self.logger.debug('Fetching shopping landmarks...')
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, preferences.shopping.score)
+            self.logger.info(f'Found {len(current_landmarks)} shopping landmarks')
+
+            # set time for all shopping activites :
+            for landmark in current_landmarks :
+                landmark.duration = 30
+            all_landmarks.update(current_landmarks)
+
+            # special pipeline for shopping malls
+            shopping_manager = ClusterManager(bbox, 'shopping')
+            shopping_clusters = shopping_manager.generate_clusters()
+            all_landmarks.update(shopping_clusters)
+
+
+        landmarks_constrained = take_most_important(all_landmarks, self.n_important)
+        # self.logger.info(f'All landmarks generated : {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
+
+        return all_landmarks, landmarks_constrained
+
+    def set_landmark_score(self, landmark: Landmark, landmarktype: str, preference_level: int) :
+        """
+        Calculate and set the attractiveness score for a given landmark.
+
+        This method evaluates the landmark's attractiveness based on its properties 
+        (number of tags, presence of Wikipedia URL, image, website, and whether it's 
+        a place of worship) and adjusts the score using the user's preference level.
+
+        Args:
+        landmark (Landmark): The landmark object to score.
+        landmarktype (str): The type of the landmark (currently unused).
+        preference_level (int): The user's preference level for this landmark type.
+        """
+        score = landmark.n_tags**self.tag_exponent
+        if landmark.wiki_url :
+            score *= self.wikipedia_bonus
+        if landmark.image_url :
+            score *= self.image_bonus
+        if landmark.website_url :
+            score *= self.wikipedia_bonus
+        if landmark.is_place_of_worship :
+            score *= self.church_coeff
+        if landmark.is_viewpoint :
+            score *= self.viewpoint_bonus
+        if landmarktype == 'nature' :
+            score *= self.nature_coeff
+
+        landmark.attractiveness = int(score * preference_level * 2)
+
+
+    def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, preference_level: int) -> list[Landmark]:
+        """
+        Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
+
+        Args:
+            bbox (tuple[float, float, float, float]): The bounding box coordinates (around:radius, center_lat, center_lon).
+            amenity_selector (dict): The Overpass API query selector for the desired landmark type. 
+            landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
+
+        Returns:
+            list[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
+
+        Notes:
+            - Landmarks are fetched using Overpass API queries.
+            - Selectors are translated from the dictionary to the Overpass query format. (e.g., 'amenity'='place_of_worship')
+            - Landmarks are filtered based on various conditions including tags and type.
+        """
+        return_list = []
+
+        if landmarktype == 'nature' : query_conditions = None
+        else : query_conditions = ['count_tags()>5']
+
+        # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
+        # we need to split the selectors into separate queries and merge the results
+        for sel in dict_to_selector_list(amenity_selector):
+            # self.logger.debug(f"Current selector: {sel}")
+
+            osm_types = ['way', 'relation']
+
+            if 'viewpoint' in sel :
+                query_conditions = None
+                osm_types.append('node')
+
+            # Send the overpass query
+            try:
+                result = self.overpass.send_query(
+                    bbox = bbox,
+                    osm_types = osm_types,
+                    selector = sel,
+                    conditions = query_conditions,        # except for nature....
+                    out = 'ids center tags'
+                    )
+            except Exception as e:
+                self.logger.error(f"Error fetching landmarks: {str(e)}")
+                continue
+
+            return_list += self._to_landmarks(result, landmarktype, preference_level)
+
+        # self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
+
+        return return_list
+
+
+    def _to_landmarks(self, elements: list, landmarktype, preference_level) -> list[Landmark]:
+        """
+        Parse the Overpass API result and extract landmarks.
+
+        This method processes the JSON elements returned by the Overpass API and 
+        extracts landmarks of types 'node', 'way', and 'relation'. It retrieves 
+        relevant information such as name, coordinates, and tags, and converts them 
+        into Landmark objects.
+
+        Args:
+        elements (list): The elements of json response from Overpass API.
+        elem_type (str): The type of landmark (e.g., node, way, relation).
+
+        Returns:
+        list[Landmark]: A list of Landmark objects extracted from the JSON data.
+        """
+        if elements is None :
+            return []
+
+        landmarks = []
+        for elem in elements:
+            osm_type = elem.get('type')
+
+            id, coords, name = get_base_info(elem, osm_type, with_name=True)
+
+            if name is None or coords is None :
+                continue
+
+            tags = elem.get('tags')
+
+            # Convert this to Landmark object
+            landmark = Landmark(name=name,
+                                type=landmarktype,
+                                location=coords,
+                                osm_id=id,
+                                osm_type=osm_type,
+                                attractiveness=0,
+                                n_tags=len(tags))
+
+            # Browse through tags to add information to landmark.
+            for key, value in tags.items():
+
+                # Skip this landmark if not suitable.
+                if key == 'building:part' and value == 'yes' :
+                    break
+                if 'disused:' in key :
+                    break
+                if 'boundary:' in key :
+                    break
+                if 'shop' in key and landmarktype != 'shopping' :
+                    break
+                # if value == 'apartments' :
+                #     break
+
+                # Fill in the other attributes.
+                if key == 'image' :
+                    landmark.image_url = value
+                if key == 'website' :
+                    landmark.website_url = value
+                if value == 'place_of_worship' :
+                    landmark.is_place_of_worship = True
+                if key == 'wikipedia' :
+                    landmark.wiki_url = value
+                if key == 'name:en' :
+                    landmark.name_en = value
+                if 'building:' in key or 'pay' in key :
+                    landmark.n_tags -= 1
+
+
+                # Set the duration.
+                if value in ['museum', 'aquarium', 'planetarium'] :
+                    landmark.duration = 60
+                elif value == 'viewpoint' :
+                    landmark.is_viewpoint = True
+                    landmark.duration = 10
+                elif value == 'cathedral' :
+                    landmark.is_place_of_worship = False
+                    landmark.duration = 10
+
+            landmark.description, landmark.keywords = self.description_and_keywords(tags)
+            self.set_landmark_score(landmark, landmarktype, preference_level)
+            landmarks.append(landmark)
+
+            continue
+
+
+        return landmarks
+
+
+    def description_and_keywords(self, tags: dict):
+        """
+        """
+        # Extract relevant fields
+        name = tags.get('name')
+        importance = tags.get('importance', None)
+        n_visitors = tags.get('tourism:visitors', None)
+        height = tags.get('height')
+        place_type = self.get_place_type(tags)
+        date = self.get_date(tags)
+
+        if place_type is None :
+            return None, None
+
+        # Start the description.
+        if importance is None :
+            if len(tags.keys()) < 5 :
+                return None, None
+            elif len(tags.keys()) < 10 :
+                description = f"{name} is a well known {place_type}."
+            elif len(tags.keys()) < 17 :
+                importance = 'national'
+                description = f"{name} is a {place_type} of national importance."
+            else :
+                importance = 'international'
+                description = f"{name} is an internationally famous {place_type}."
+        else :
+            description = f"{name} is a {place_type} of {importance} importance."
+
+        if height is not None and date is not None :
+            description += f" This {place_type} was constructed in {date} and is ca. {height} meters high."
+        elif height is not None :
+            description += f" This {place_type} stands ca. {height} meters tall."
+        elif date is not None:
+            description += f" It was constructed in {date}."
+
+        # Format the visitor number
+        if n_visitors is not None :
+            n_visitors = int(n_visitors)
+            if n_visitors < 1000000 :
+                description += f" It welcomes {int(n_visitors/1000)} thousand visitors every year."
+            else :
+                description += f" It welcomes {round(n_visitors/1000000, 1)} million visitors every year."
+
+        # Set the keywords.
+        keywords = {"importance": importance,
+                    "height": height,
+                    "place_type": place_type,
+                    "date": date}
+
+        return description, keywords
+
+
+    def get_place_type(self, data):
+        amenity = data.get('amenity', None)
+        building = data.get('building', None)
+        historic = data.get('historic', None)
+        leisure = data.get('leisure')
+
+        if historic and historic != "yes":
+            return historic
+        if building and building not in ["yes", "civic", "government", "apartments", "residential", "commericial", "industrial", "retail", "religious", "public", "service"]:
+            return building
+        if amenity:
+            return amenity
+        if leisure:
+            return leisure
+
+
+        return None
+
+
+    def get_date(self, data):
+        construction_date = data.get('construction_date', None)
+        opening_date = data.get('opening_date', None)
+        start_date = data.get('start_date', None)
+        year_of_construction = data.get('year_of_construction', None)
+
+        # Prioritize based on availability
+        if construction_date:
+            return construction_date
+        if start_date:
+            return start_date
+        if year_of_construction:
+            return year_of_construction
+        if opening_date:
+            return opening_date
+
+        return None
+
+
+def dict_to_selector_list(d: dict) -> list:
+    """
+    Convert a dictionary of key-value pairs to a list of Overpass query strings.
+
+    Args:
+        d (dict): A dictionary of key-value pairs representing the selector.
+
+    Returns:
+        list: A list of strings representing the Overpass query selectors.
+    """
+    return_list = []
+    for key, value in d.items():
+        if isinstance(value, list):
+            val = '|'.join(value)
+            return_list.append(f'{key}~"^({val})$"')
+        elif isinstance(value, str) and len(value) == 0:
+            return_list.append(f'{key}')
+        else:
+            return_list.append(f'{key}={value}')
+    return return_list