Upgraded refiner

backend/src/landmarks_manager.py

@@ -15,7 +15,33 @@ SHOPPING = LandmarkType(landmark_type='shopping')
 
 # Include the json here
 # Create a list of all things to visit given some preferences and a city. Ready for the optimizer
-def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
+def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
+
+    l_sights, l_nature, l_shop = get_amenities()
+    L = []
+
+    # List for sightseeing
+    if preferences.sightseeing.score != 0 :
+        L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
+        correct_score(L1, preferences.sightseeing)
+        L += L1
+    
+    # List for nature
+    if preferences.nature.score != 0 :
+        L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
+        correct_score(L2, preferences.nature)
+        L += L2
+    
+    # List for shopping
+    if preferences.shopping.score != 0 :
+        L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
+        correct_score(L3, preferences.shopping)
+        L += L3
+
+    return remove_duplicates(L), take_most_important(L)
+
+
+"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
 
     l_sights, l_nature, l_shop = get_amenities()
     L = []
@@ -39,8 +65,7 @@ def generate_landmarks(preferences: Preferences, city_country: str = None, coord
         L += L3
 
     return remove_duplicates(L), take_most_important(L)
-
+"""
-
 # Helper function to gather the amenities list
 def get_amenities() -> List[List[str]] :
     
@@ -199,7 +224,71 @@ def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[flo
     return min_lat, min_lon, max_lat, max_lon
 
 
-def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
+def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+        parameters = json.loads(f.read())
+        tag_coeff = parameters['tag coeff']
+        park_coeff = parameters['park coeff']
+        church_coeff = parameters['church coeff']
+        radius = parameters['radius close to']
+        bbox_side = parameters['city bbox side']
+    
+    # Create bbox around start location
+    bbox = create_bbox(coordinates, bbox_side)
+
+    # Initialize some variables
+    N = 0
+    L = []
+    overpass = Overpass()
+
+    for amenity in list_amenity :
+        query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
+        result = overpass.query(query)
+        N += result.countElements()
+
+        for elem in result.elements():
+
+            name = elem.tag('name')                             # Add name
+            location = (elem.centerLat(), elem.centerLon())     # Add coordinates (lat, lon)
+
+            # skip if unprecise location
+            if name is None or location[0] is None:
+                continue
+
+            # skip if unused
+            if 'disused:leisure' in elem.tags().keys():
+                continue
+            
+            # skip if part of another building
+            if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
+                continue
+            
+            else :
+                osm_type = elem.type()              # Add type : 'way' or 'relation'
+                osm_id = elem.id()                  # Add OSM id 
+                elem_type = landmarktype            # Add the landmark type as 'sightseeing
+                n_tags = len(elem.tags().keys())    # Add number of tags
+                
+                # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
+                if amenity == "'amenity'='place_of_worship'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)  
+                elif amenity == "'leisure'='park'" :
+                    score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)  
+                else :
+                    score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+
+                if score is not None :
+                    # Generate the landmark and append it to the list
+                    landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
+                    L.append(landmark)
+
+    return L
+
+
+
+"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
     
     if city_country is None and coordinates is None :
         raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
@@ -277,3 +366,4 @@ def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country:
                     L.append(landmark)
 
     return L
+"""

backend/src/main.py

@@ -1,6 +1,6 @@
 from optimizer import solve_optimization
+from refiner import refine_optimization
 from landmarks_manager import generate_landmarks
-from structs.landmarks import LandmarkTest
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
@@ -12,35 +12,45 @@ app = FastAPI()
 
 # Assuming frontend is calling like this : 
 #"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-@app.post("/optimizer_coords/{latitude}/{longitude}/{city_country}")
+@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
-def main1(preferences: Preferences = Body(...), latitude: float = None, longitude: float = None, city_country: str = None) -> List[Landmark]:
+def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
     
     if preferences is None :
         raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
-    elif latitude is None and longitude is None and city_country is None :
+    if bool(start_lat) ^ bool(start_lon) :
-        raise ValueError("Please provide GPS coordinates or a 'city_country' string.")
+        raise ValueError("Please provide both latitude and longitude for the starting point")
-    elif latitude is not None and longitude is not None and city_country is not None :
+    if bool(finish_lat) ^ bool(finish_lon) :
-        raise ValueError("Please provide EITHER GPS coordinates or a 'city_country' string.")
+        raise ValueError("Please provide both latitude and longitude for the finish point")
 
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
-    # From frontend get longitude, latitude and prefence list
+    if bool(finish_lat) and bool(finish_lon) :
-    if city_country is None :
+        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-        coordinates = tuple((latitude, longitude))
+    else :
+        finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
     
-    [], landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
-
     start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
 
+    # Generate the landmarks from the start location
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
+
+    # insert start and finish to the landmarks list
     landmarks_short.insert(0, start)
     landmarks_short.append(finish)
 
+    # TODO use these parameters in another way
     max_walking_time = 4    # hours
+    detour = 30             # minutes
 
-    visiting_list = solve_optimization(landmarks_short, max_walking_time*60, True)
+    # First stage optimization
+    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
     
-    return visiting_list
+    # Second stage optimization
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    
+    return refined_tour
 
 
 

backend/src/optimizer.py

@@ -141,19 +141,19 @@ def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float
     else: 
         dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
 
-    # Consider the detour factor for average city
+    # Consider the detour factor for average cityto deterline walking distance (in km)
-    wdist = dist*detour
+    walk_dist = dist*detour
 
     # Time to walk this distance (in minutes)
-    wtime = wdist/speed*60
+    walk_time = walk_dist/speed*60
 
-    if wtime > 15 :
+    if walk_time > 15 :
-        wtime = 5*round(wtime/5)
+        walk_time = 5*round(walk_time/5)
     else :
-        wtime = round(wtime)
+        walk_time = round(walk_time)
   
 
-    return round(wdist, 1), wtime
+    return round(walk_dist, 1), walk_time
 
 
 # Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
@@ -291,23 +291,23 @@ def respect_order(N: int, A_eq, b_eq):
 
 
 # Computes the path length given path matrix (dist_table) and a result
-def add_time_to_reach(order: List[Landmark], landmarks: List[Landmark])->List[Landmark] :
+def add_time_to_reach(order: List[int], landmarks: List[Landmark])->List[Landmark] :
-    
-    j = 0
-    L =  []
     
     # Read the parameters from the file
     with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
         parameters = json.loads(f.read())
-        detour = parameters['detour factor']
+        detour_factor = parameters['detour factor']
         speed = parameters['average walking speed']
     
+    j = 0
+    L =  []
     prev = landmarks[0]
+
     while(len(L) != len(order)) :
         
         elem = landmarks[order[j]]
         if elem != prev :
-            elem.time_to_reach = get_distance(elem.location, prev.location, detour, speed)[1]
+            elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
             elem.must_do = True
         L.append(elem)
         prev = elem   
@@ -315,6 +315,26 @@ def add_time_to_reach(order: List[Landmark], landmarks: List[Landmark])->List[La
     
     return L
 
+def add_time_to_reach_simple(ordered_visit: List[Landmark])-> List[Landmark] :
+    
+    # Read the parameters from the file
+    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+        parameters = json.loads(f.read())
+        detour_factor = parameters['detour factor']
+        speed = parameters['average walking speed']
+    
+    L =  []
+    prev = ordered_visit[0]
+    L.append(prev)
+
+    for elem in ordered_visit[1:] :
+        elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
+        elem.must_do = True
+        L.append(elem)
+        prev = elem
+
+    return L
+
 
 # Main optimization pipeline
 def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
@@ -356,7 +376,7 @@ def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_deta
                 # Add the times to reach and stop optimizing
                 L = add_time_to_reach(order, landmarks)
                 break
-            print(i)
+            #print(i)
             i += 1
         
         if i == timeout :

backend/src/parameters/optimizer.params

@@ -1,4 +1,4 @@
 {
-  "detour factor" : 10,
+  "detour factor" : 1.4,
-  "average walking speed" : 27.5
+  "average walking speed" : 4.8
 }

backend/src/refiner.py

@@ -1,9 +1,10 @@
-from shapely import buffer, LineString, Point, Polygon
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
 from typing import List
 from math import pi
 
 from structs.landmarks import Landmark
 from landmarks_manager import take_most_important
+from optimizer import solve_optimization, add_time_to_reach_simple, print_res
 
 
 def create_corridor(landmarks: List[Landmark], width: float) :
@@ -34,10 +35,78 @@ def is_in_area(area: Polygon, coordinates) -> bool :
 def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
 
   second_order_landmarks = []
+  visited_names = []
   area = create_corridor(visited_landmarks, width)
 
+  for visited in visited_landmarks :
+    visited_names.append(visited.name)
+  
   for landmark in all_landmarks :
-    if is_in_area(area, landmark.location) and landmark not in visited_landmarks:
+    if is_in_area(area, landmark.location) and landmark.name not in visited_names:
       second_order_landmarks.append(landmark)
 
   return take_most_important(second_order_landmarks)
+
+
+
+"""def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+  if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+  full_set.append(base_tour[-1])                # add finish back
+
+  new_route = solve_optimization(full_set, max_time, print_infos)
+
+  return new_route"""
+
+
+def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+  minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+  if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+  # full set of visitable landmarks
+  full_set = base_tour[:-1] + minor_landmarks   # create full set of possible landmarks (without finish)
+  full_set.append(base_tour[-1])                # add finish back
+
+  # get a new route
+  new_route = solve_optimization(full_set, max_time, False)
+
+  coords = []         # Coordinates of the new route
+  coords_dict = {}    # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
+
+  # Iterate through the new route without finish
+  for elem in new_route[:-1] :
+    coords.append(Point(elem.location))
+    coords_dict[elem.location] = elem         # if start = goal, only finish remains
+
+  # Create a concave polygon using the coordinates
+  better_route_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of route leaving out start and finish
+  xs, ys = better_route_poly.exterior.xy
+
+  better_route = []   # List of ordered visit
+  name_index = {}     # Maps the name of a landmark to its index in the concave polygon
+
+  # Loop through the polygon and generate the better (ordered) route
+  for i,x in enumerate(xs[:-1]) :
+    better_route.append(coords_dict[tuple((x,ys[i]))])
+    name_index[coords_dict[tuple((x,ys[i]))].name] = i
+
+  
+  # Scroll the list to have start in front again
+  start_index = name_index['start']
+  better_route = better_route[start_index:] + better_route[:start_index]
+
+  # Append the finish back and correct the time to reach
+  better_route.append(new_route[-1])
+  better_route = add_time_to_reach_simple(better_route)
+
+  if print_infos :
+    print("\nRefined tour (result of second stage optimization): ")
+    print_res(better_route, len(better_route))
+
+  return better_route

backend/src/tester.py

@@ -5,7 +5,7 @@ from landmarks_manager import generate_landmarks
 from fastapi.encoders import jsonable_encoder
 
 from optimizer import solve_optimization
-from refiner import get_minor_landmarks
+from refiner import refine_optimization
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
 from structs.preferences import Preferences, Preference
@@ -80,38 +80,32 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
                                   type=LandmarkType(landmark_type='shopping'),
                                   score = 5))
 
-    city_country = None
 
-    landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
+    # Create start and finish 
+    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    
+    # Generate the landmarks from the start location
+    landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
     #write_data(landmarks, "landmarks.txt")
 
+    # Insert start and finish to the landmarks list
+    landmarks_short.insert(0, start)
+    landmarks_short.append(finish)
 
-    start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    # TODO use these parameters in another way
-    finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-    
-    test = landmarks_short
-    
-    test.insert(0, start)
-    test.append(finish)
-
     max_walking_time = 4    # hours
     detour = 30             # minutes
 
-    visited_list = solve_optimization(test, max_walking_time*60, True)
+    # First stage optimization
-    #visited_list = [Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=0), Landmark(name='Palais du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8614768, 2.3351677), osm_type='relation', osm_id=3262297, attractiveness=32, must_do=False, n_tags=32, time_to_reach=85), Landmark(name='Musée du Louvre', type=LandmarkType(landmark_type='sightseeing'), location=(48.8611474, 2.3358637), osm_type='relation', osm_id=7515426, attractiveness=34, must_do=False, n_tags=33, time_to_reach=1), Landmark(name='Bourse de Commerce — Pinault Collection', type=LandmarkType(landmark_type='sightseeing'), location=(48.8628167, 2.3428183), osm_type='way', osm_id=19856722, attractiveness=32, must_do=False, n_tags=32, time_to_reach=12), Landmark(name='Centre Georges Pompidou', type=LandmarkType(landmark_type='sightseeing'), location=(48.8605235, 2.3524395), osm_type='way', osm_id=55503397, attractiveness=43, must_do=False, n_tags=43, time_to_reach=15), Landmark(name='Tour Saint-Jacques', type=LandmarkType(landmark_type='sightseeing'), location=(48.8579983, 2.3489178), osm_type='way', osm_id=20326709, attractiveness=33, must_do=False, n_tags=31, time_to_reach=8), Landmark(name='Hôtel de Ville', type=LandmarkType(landmark_type='sightseeing'), location=(48.8564265, 2.352527), osm_type='relation', osm_id=284089, attractiveness=34, must_do=False, n_tags=32, time_to_reach=7), Landmark(name='Cathédrale Notre-Dame de Paris', type=LandmarkType(landmark_type='sightseeing'), location=(48.8529372, 2.3498701), osm_type='way', osm_id=201611261, attractiveness=55, must_do=False, n_tags=54, time_to_reach=9), Landmark(name='Sainte-Chapelle', type=LandmarkType(landmark_type='sightseeing'), location=(48.8553966, 2.3450136), osm_type='relation', osm_id=3344870, attractiveness=57, must_do=False, n_tags=54, time_to_reach=10), Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags=0, time_to_reach=90)] 
+    base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
+    
+    # Second stage optimization
+    refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
+    
+    return refined_tour
 
 
-    minor_landmarks = get_minor_landmarks(landmarks, visited_list, 200)
+#test4(tuple((48.8344400, 2.3220540)))       # Café Chez César 
-    #write_data(minor_landmarks, 'minor_landmarks.txt')
+test4(tuple((48.8375946, 2.2949904)))       # Point random
-    print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
-
-    full_set = visited_list[:-1] + minor_landmarks[:30]
-    full_set.append(finish)
-
-    new_route = solve_optimization(full_set, max_walking_time*60+detour, True)
-    
-    return new_route
-
-
-test4(tuple((48.8795156, 2.3660204)))
 #test3('Vienna, Austria')

minor_landmarks.txt

@@ -16,86 +16,198 @@
     "time_to_reach":0
   },
   "1":{
-    "name":"Musée d'art et d'histoire du Judaïsme",
+    "name":"Musée de Cluny",
     "type":{
       "landmark_type":"sightseeing"
     },
     "location":[
-      48.8612656,
+      48.8506604,
-      2.355418
+      2.3437398
     ],
     "osm_type":"way",
-    "osm_id":56687783,
+    "osm_id":56640163,
-    "attractiveness":9,
-    "must_do":false,
-    "n_tags":8,
-    "time_to_reach":0
-  },
-  "2":{
-    "name":"Musée Ernest-Hébert (en travaux)",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8473326,
-      2.3227159
-    ],
-    "osm_type":"way",
-    "osm_id":64314872,
-    "attractiveness":10,
-    "must_do":false,
-    "n_tags":9,
-    "time_to_reach":0
-  },
-  "3":{
-    "name":"Crypte Archéologique du Parvis Notre-Dame",
-    "type":{
-      "landmark_type":"sightseeing"
-    },
-    "location":[
-      48.8535851,
-      2.3480846
-    ],
-    "osm_type":"way",
-    "osm_id":159896046,
     "attractiveness":18,
     "must_do":false,
     "n_tags":18,
     "time_to_reach":0
   },
-  "4":{
+  "2":{
-    "name":"Jardin Catherine Labouré",
+    "name":"Musée du Luxembourg",
     "type":{
       "landmark_type":"sightseeing"
     },
     "location":[
-      48.8510562,
+      48.8485965,
-      2.320532
+      2.3340156
     ],
     "osm_type":"way",
-    "osm_id":148481812,
+    "osm_id":170226810,
-    "attractiveness":8,
+    "attractiveness":15,
     "must_do":false,
-    "n_tags":8,
+    "n_tags":15,
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+  },
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+    "name":"Musée national Eugène Delacroix",
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+    "name":"Hôtel de Sully",
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+      "landmark_type":"sightseeing"
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   "5":{
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   "6":{
+    "name":"Musée Bourdelle",
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+    "name":"Musée Carnavalet",
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+    "name":"Jardin du Luxembourg",
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+    "name":"Cathédrale Notre-Dame de Paris",
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+  "12":{
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+  "13":{
     "name":"Palais de Justice de Paris",
     "type":{
       "landmark_type":"sightseeing"
@@ -111,7 +223,23 @@
     "n_tags":24,
     "time_to_reach":0
   },
-  "7":{
+  "14":{
+    "name":"Mémorial des Martyrs de la Déportation",
+    "type":{
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+  "15":{
     "name":"Fontaine des Innocents",
     "type":{
       "landmark_type":"sightseeing"
@@ -127,7 +255,39 @@
     "n_tags":15,
     "time_to_reach":0
   },
-  "8":{
+  "16":{
+    "name":"Hôtel de Sens",
+    "type":{
+      "landmark_type":"sightseeing"
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+  "17":{
+    "name":"Hôtel d'Ourscamp",
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+      "landmark_type":"sightseeing"
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     "name":"Hôtel de Choiseul-Praslin",
     "type":{
       "landmark_type":"sightseeing"
@@ -143,23 +303,7 @@
     "n_tags":12,
     "time_to_reach":0
   },
-  "9":{
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-    "name":"Hôtel de Cassini",
-    "type":{
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     "name":"Chapelle Notre-Dame-des-Anges",
     "type":{
       "landmark_type":"sightseeing"
@@ -175,7 +319,7 @@
     "n_tags":16,
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-  "11":{
+  "20":{
     "name":"Fontaine du Palmier",
     "type":{
       "landmark_type":"sightseeing"
@@ -191,103 +335,39 @@
     "n_tags":14,
     "time_to_reach":0
   },
-  "12":{
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-    "name":"Colonne Médicis",
+    "name":"Église Saint-Séverin",
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-  "13":{
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-    "name":"Noviciat des Dominicains",
+    "name":"Église Orthodoxe Roumaine des Saints Archanges",
     "type":{
       "landmark_type":"sightseeing"
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     "location":[
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-  "17":{
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@@ -303,87 +383,23 @@
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   "24":{
+    "name":"Église Saint-Germain des Prés",
+    "type":{
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+  "25":{
     "name":"Square de la Tour Saint-Jacques",
     "type":{
       "landmark_type":"nature"
@@ -399,7 +415,7 @@
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-  "25":{
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     "name":"Square Jean XXIII",
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@@ -415,52 +431,36 @@
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   "27":{
-    "name":"Square de l'Abbé Esquerré",
+    "name":"Square Félix Desruelles",
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   "28":{
-    "name":"Jardin Nelson Mandela",
+    "name":"Jardin des Rosiers – Joseph-Migneret",
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