diff --git a/backend/.gitignore b/backend/.gitignore
index e9a2840..e41338d 100644
--- a/backend/.gitignore
+++ b/backend/.gitignore
@@ -1,4 +1,5 @@
 # osm-cache and wikidata cache
+cache_XML/
 cache/
 apicache/
 
diff --git a/backend/report.html b/backend/report.html
index 84a378d..b34ea7a 100644
--- a/backend/report.html
+++ b/backend/report.html
@@ -328,7 +328,7 @@ div.media {
   </head>
   <body>
     <h1 id="title">Backend Testing Report</h1>
-    <p>Report generated on 22-Jan-2025 at 16:51:34 by <a href="https://pypi.python.org/pypi/pytest-html">pytest-html</a>
+    <p>Report generated on 23-Jan-2025 at 08:03:39 by <a href="https://pypi.python.org/pypi/pytest-html">pytest-html</a>
         v4.1.1</p>
     <div id="environment-header">
       <h2>Environment</h2>
@@ -382,7 +382,7 @@ div.media {
         <h2>Summary</h2>
         <div class="additional-summary prefix">
         </div>
-        <p class="run-count">1 test took 00:00:17.</p>
+        <p class="run-count">1 test took 00:00:19.</p>
         <p class="filter">(Un)check the boxes to filter the results.</p>
         <div class="summary__reload">
           <div class="summary__reload__button hidden" onclick="location.reload()">
@@ -432,7 +432,7 @@ div.media {
     </table>
   </body>
   <footer>
-    <div id="data-container" data-jsonblob="{&#34;environment&#34;: {&#34;Python&#34;: &#34;3.12.3&#34;, &#34;Platform&#34;: &#34;Linux-6.8.0-51-generic-x86_64-with-glibc2.39&#34;, &#34;Packages&#34;: {&#34;pytest&#34;: &#34;8.3.4&#34;, &#34;pluggy&#34;: &#34;1.5.0&#34;}, &#34;Plugins&#34;: {&#34;html&#34;: &#34;4.1.1&#34;, &#34;anyio&#34;: &#34;4.8.0&#34;, &#34;metadata&#34;: &#34;3.1.1&#34;}}, &#34;tests&#34;: {&#34;src/tests/test_main.py::test_bellecour&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_bellecour&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_bellecour&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 5 - Grand H\u00f4tel-Dieu (423 | 30) - 8 - Cordeliers (1136 | 40) - 8 - Vieux Lyon (688 | 15) - 13 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;119 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;120 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;00:00:17&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;------------------------------ Captured log call -------------------------------\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     src.main:main.py:65 No end coordinates provided. Using start=end.\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:81 Starting to fetch landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:93 Fetching sightseeing landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[tourism~&amp;quot;^(museum|attraction|gallery|artwork|aquarium)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[tourism~&amp;quot;^(museum|attraction|gallery|artwork|aquarium)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[historic](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[historic](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[amenity~&amp;quot;^(planetarium|place_of_worship|fountain|townhall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[amenity~&amp;quot;^(planetarium|place_of_worship|fountain|townhall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[water~&amp;quot;^(reflecting_pool)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[water~&amp;quot;^(reflecting_pool)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[bridge~&amp;quot;^(aqueduct|viaduct|boardwalk|cantilever|abandoned)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[bridge~&amp;quot;^(aqueduct|viaduct|boardwalk|cantilever|abandoned)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[building~&amp;quot;^(cathedral)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[building~&amp;quot;^(cathedral)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:363 Fetched 2 landmarks of type sightseeing in (&amp;#x27;around:1714.0&amp;#x27;, &amp;#x27;45.7576485&amp;#x27;, &amp;#x27;4.8330241&amp;#x27;)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:97 Fetching sightseeing clusters...\nDEBUG    src.utils.cluster_manager:cluster_manager.py:134 Found 3 different clusters.\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:103 Sightseeing clusters done\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:107 Fetching nature landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[leisure=park](around:1714.0, 45.7576485, 4.8330241);relation[leisure=park](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[geological](around:1714.0, 45.7576485, 4.8330241);relation[geological](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[natural~&amp;quot;^(geyser|hot_spring|arch|volcano|stone)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241);relation[natural~&amp;quot;^(geyser|hot_spring|arch|volcano|stone)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241);relation[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241);node[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[water~&amp;quot;^(pond|lake|river|basin|stream|lagoon|rapids)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241);relation[water~&amp;quot;^(pond|lake|river|basin|stream|lagoon|rapids)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[waterway~&amp;quot;^(waterfall|river|canal|dam|dock|boatyard)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241);relation[waterway~&amp;quot;^(waterfall|river|canal|dam|dock|boatyard)$&amp;quot;](around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:363 Fetched 6 landmarks of type nature in (&amp;#x27;around:1714.0&amp;#x27;, &amp;#x27;45.7576485&amp;#x27;, &amp;#x27;4.8330241&amp;#x27;)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:115 Fetching shopping landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:353 Query: (way[shop~&amp;quot;^(department_store|mall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241);relation[shop~&amp;quot;^(department_store|mall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1714.0, 45.7576485, 4.8330241););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:363 Fetched 6 landmarks of type shopping in (&amp;#x27;around:1714.0&amp;#x27;, &amp;#x27;45.7576485&amp;#x27;, &amp;#x27;4.8330241&amp;#x27;)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:118 Fetching shopping clusters...\nDEBUG    src.utils.cluster_manager:cluster_manager.py:134 Found 8 different clusters.\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:129 Shopping clusters done\nINFO     src.main:main.py:97 Fetched 18 landmarks in  \t: 16.315 seconds\nDEBUG    src.utils.optimizer:optimizer.py:577 First results are out. Looking out for circles and correcting.\nDEBUG    src.utils.optimizer:optimizer.py:619 Re-optimized 2 times, objective value : 2247\nDEBUG    src.utils.refiner:refiner.py:345 Using 3 minor landmarks around the predicted path\nDEBUG    src.utils.optimizer:optimizer.py:577 First results are out. Looking out for circles and correcting.\nDEBUG    src.utils.optimizer:optimizer.py:619 Re-optimized 1 times, objective value : 2247\nDEBUG    src.main:main.py:120 First stage optimization\t: 0.183 seconds\nDEBUG    src.main:main.py:121 Second stage optimization\t: 0.062 seconds\nINFO     src.main:main.py:122 Total computation time\t: 0.244 seconds\nINFO     httpx:_client.py:1025 HTTP Request: POST http://testserver/trip/new &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/be6d1e25-7e12-49f7-b2f1-69f7daa8df52 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/0d207a69-3c80-4614-9b26-59395670fc67 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/4d07189c-ca64-40a0-8f98-8650040a139a &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/1ef7e345-3d86-41c7-bef1-ba6a1a4faaee &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/512091d3-80fa-44a0-9964-dc4aca2f2d85 &amp;quot;HTTP/1.1 200 OK&amp;quot;\n\n&#34;}]}, &#34;renderCollapsed&#34;: [&#34;passed&#34;], &#34;initialSort&#34;: &#34;result&#34;, &#34;title&#34;: &#34;Backend Testing Report&#34;}"></div>
+    <div id="data-container" data-jsonblob="{&#34;environment&#34;: {&#34;Python&#34;: &#34;3.12.3&#34;, &#34;Platform&#34;: &#34;Linux-6.8.0-51-generic-x86_64-with-glibc2.39&#34;, &#34;Packages&#34;: {&#34;pytest&#34;: &#34;8.3.4&#34;, &#34;pluggy&#34;: &#34;1.5.0&#34;}, &#34;Plugins&#34;: {&#34;html&#34;: &#34;4.1.1&#34;, &#34;anyio&#34;: &#34;4.8.0&#34;, &#34;metadata&#34;: &#34;3.1.1&#34;}}, &#34;tests&#34;: {&#34;src/tests/test_main.py::test_bellecour&#34;: [{&#34;extras&#34;: [], &#34;result&#34;: &#34;Passed&#34;, &#34;testId&#34;: &#34;src/tests/test_main.py::test_bellecour&#34;, &#34;resultsTableRow&#34;: [&#34;&lt;td class=\&#34;col-result\&#34;&gt;Passed&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-testId\&#34;&gt;src/tests/test_main.py::test_bellecour&lt;/td&gt;&#34;, &#34;&lt;td&gt;start (0 | 0) - 7 - Grande Synagogue de Lyon (191 | 5) - 5 - Impasse Turquet (228 | 5) - 5 - Palais archi\u00e9piscopal (191 | 5) - 1 - Cath\u00e9drale Saint-Jean (305 | 5) - 7 - Basilique de Fourvi\u00e8re (251 | 5) - 8 - Temple du Change (314 | 5) - 5 - La Bibliotheque de la Cit\u00e9 \&#34;des \u00e9crivains en Rh\u00f4ne-Alpes\&#34; (141 | 5) - 4 - Mus\u00e9e des Beaux-Arts de Lyon (170 | 5) - 8 - Basilique Saint-Bonaventure (364 | 5) - 12 - finish (0 | 0) - 0&lt;/td&gt;&#34;, &#34;&lt;td&gt;107 min&lt;/td&gt;&#34;, &#34;&lt;td&gt;120 min&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-duration\&#34;&gt;00:00:19&lt;/td&gt;&#34;, &#34;&lt;td class=\&#34;col-links\&#34;&gt;&lt;/td&gt;&#34;], &#34;log&#34;: &#34;------------------------------ Captured log call -------------------------------\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     src.main:main.py:65 No end coordinates provided. Using start=end.\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:81 Starting to fetch landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:93 Fetching sightseeing landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[tourism~&amp;quot;^(museum|attraction|gallery|artwork|aquarium)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[tourism~&amp;quot;^(museum|attraction|gallery|artwork|aquarium)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[historic](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[historic](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[amenity~&amp;quot;^(planetarium|place_of_worship|fountain|townhall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[amenity~&amp;quot;^(planetarium|place_of_worship|fountain|townhall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[water~&amp;quot;^(reflecting_pool)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[water~&amp;quot;^(reflecting_pool)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[bridge~&amp;quot;^(aqueduct|viaduct|boardwalk|cantilever|abandoned)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[bridge~&amp;quot;^(aqueduct|viaduct|boardwalk|cantilever|abandoned)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[building~&amp;quot;^(cathedral)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[building~&amp;quot;^(cathedral)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:215 Fetched 139 landmarks of type sightseeing in (1714.0, 45.7576485, 4.8330241)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:96 Fetching sightseeing clusters...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:106 Fetching nature landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[leisure=park](around:1700, 45.76, 4.83);relation[leisure=park](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[geological](around:1700, 45.76, 4.83);relation[geological](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[natural~&amp;quot;^(geyser|hot_spring|arch|volcano|stone)$&amp;quot;](around:1700, 45.76, 4.83);relation[natural~&amp;quot;^(geyser|hot_spring|arch|volcano|stone)$&amp;quot;](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1700, 45.76, 4.83);relation[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1700, 45.76, 4.83);node[tourism~&amp;quot;^(alpine_hut|viewpoint|zoo|resort|picnic_site)$&amp;quot;](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[water~&amp;quot;^(pond|lake|river|basin|stream|lagoon|rapids)$&amp;quot;](around:1700, 45.76, 4.83);relation[water~&amp;quot;^(pond|lake|river|basin|stream|lagoon|rapids)$&amp;quot;](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[waterway~&amp;quot;^(waterfall|river|canal|dam|dock|boatyard)$&amp;quot;](around:1700, 45.76, 4.83);relation[waterway~&amp;quot;^(waterfall|river|canal|dam|dock|boatyard)$&amp;quot;](around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:215 Fetched 63 landmarks of type nature in (1714.0, 45.7576485, 4.8330241)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:113 Fetching shopping landmarks...\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:205 Query: (way[shop~&amp;quot;^(department_store|mall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83);relation[shop~&amp;quot;^(department_store|mall)$&amp;quot;](if: count_tags()&amp;gt;5)(around:1700, 45.76, 4.83););out center;\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:215 Fetched 3 landmarks of type shopping in (1714.0, 45.7576485, 4.8330241)\nDEBUG    src.utils.landmarks_manager:landmarks_manager.py:115 Fetching shopping clusters...\nINFO     src.main:main.py:97 Fetched 176 landmarks in  \t: 7.117 seconds\nDEBUG    src.utils.optimizer:optimizer.py:577 First results are out. Looking out for circles and correcting.\nDEBUG    src.utils.optimizer:optimizer.py:619 Re-optimized 10 times, objective value : 2641\nDEBUG    src.utils.refiner:refiner.py:345 Using 15 minor landmarks around the predicted path\nDEBUG    src.utils.optimizer:optimizer.py:577 First results are out. Looking out for circles and correcting.\nDEBUG    src.utils.optimizer:optimizer.py:619 Re-optimized 11 times, objective value : 2155\nDEBUG    src.main:main.py:120 First stage optimization\t: 6.879 seconds\nDEBUG    src.main:main.py:121 Second stage optimization\t: 5.385 seconds\nINFO     src.main:main.py:122 Total computation time\t: 12.263 seconds\nINFO     httpx:_client.py:1025 HTTP Request: POST http://testserver/trip/new &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/7b9c1658-f1dc-437d-adc5-6baa12488f79 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/f1a49879-a4d6-4ac0-a943-32781c0365fd &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/4c4576c4-cd8c-4421-9ca6-e41b20ed30d2 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/d1020ab2-99f2-413c-9d02-1bf818e6d3a0 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/543c25f1-f7cc-4527-ae21-b7217112e0f1 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/9beeca61-4096-4e6f-93f9-951a58e683d9 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/42ebbd1b-8d82-4c09-9217-d4e2070d7aed &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/eb791467-e11b-494e-8f6e-19189a329aa2 &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/bbe1579e-315d-457c-a7e4-b6142d4fffaf &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/17acceb7-8434-42b4-baf2-c37003a60c6e &amp;quot;HTTP/1.1 200 OK&amp;quot;\nDEBUG    asyncio:selector_events.py:64 Using selector: EpollSelector\nINFO     httpx:_client.py:1025 HTTP Request: GET http://testserver/landmark/34bbe85f-705f-4f69-8717-a54a6cd7bdca &amp;quot;HTTP/1.1 200 OK&amp;quot;\n\n&#34;}]}, &#34;renderCollapsed&#34;: [&#34;passed&#34;], &#34;initialSort&#34;: &#34;result&#34;, &#34;title&#34;: &#34;Backend Testing Report&#34;}"></div>
     <script>
       (function(){function r(e,n,t){function o(i,f){if(!n[i]){if(!e[i]){var c="function"==typeof require&&require;if(!f&&c)return c(i,!0);if(u)return u(i,!0);var a=new Error("Cannot find module '"+i+"'");throw a.code="MODULE_NOT_FOUND",a}var p=n[i]={exports:{}};e[i][0].call(p.exports,function(r){var n=e[i][1][r];return o(n||r)},p,p.exports,r,e,n,t)}return n[i].exports}for(var u="function"==typeof require&&require,i=0;i<t.length;i++)o(t[i]);return o}return r})()({1:[function(require,module,exports){
 const { getCollapsedCategory, setCollapsedIds } = require('./storage.js')
diff --git a/backend/src/overpass/caching_strategy.py b/backend/src/overpass/caching_strategy.py
new file mode 100644
index 0000000..e53b228
--- /dev/null
+++ b/backend/src/overpass/caching_strategy.py
@@ -0,0 +1,133 @@
+import os
+import xml.etree.ElementTree as ET
+import hashlib
+import ujson
+
+from ..constants import OSM_CACHE_DIR
+
+
+def get_cache_key(query: str) -> str:
+    """
+    Generate a unique cache key for the query using a hash function.
+    This ensures that queries with different parameters are cached separately.
+    """
+    return hashlib.md5(query.encode('utf-8')).hexdigest()
+
+
+class CachingStrategyBase:
+    def get(self, key):
+        raise NotImplementedError('Subclass should implement get')
+
+    def set(self, key, data):
+        raise NotImplementedError('Subclass should implement set')
+
+    def close(self):
+        pass
+
+
+# For later use if xml does not suit well
+class JSONCache(CachingStrategyBase):
+    def __init__(self, cache_dir=OSM_CACHE_DIR):
+        # Add the class name as a suffix to the directory
+        self._cache_dir = f'{cache_dir}_JSON'
+        if not os.path.exists(self._cache_dir):
+            os.makedirs(self._cache_dir)
+
+    def _filename(self, key):
+        return os.path.join(self._cache_dir, f'{key}.json')
+
+    def get(self, key):
+        filename = self._filename(key)
+        if os.path.exists(filename):
+            with open(filename, 'r') as file:
+                return ujson.load(file)
+        return None
+
+    def set(self, key, value):
+        with open(self._filename(key), 'w') as file:
+            ujson.dump(value, file)
+
+
+class XMLCache(CachingStrategyBase):
+    def __init__(self, cache_dir=OSM_CACHE_DIR):
+        # Add the class name as a suffix to the directory
+        self._cache_dir = f'{cache_dir}_XML'
+        if not os.path.exists(self._cache_dir):
+            os.makedirs(self._cache_dir)
+
+    def _filename(self, key):
+        return os.path.join(self._cache_dir, f'{key}.xml')
+
+    def get(self, key):
+        """Retrieve XML data from the cache and parse it as an ElementTree."""
+        filename = self._filename(key)
+        if os.path.exists(filename):
+            try:
+                # Parse and return the cached XML data
+                tree = ET.parse(filename)
+                return tree.getroot()  # Return the root element of the parsed XML
+            except ET.ParseError:
+                print(f"Error parsing cached XML file: {filename}")
+                return None
+        return None
+
+    def set(self, key, value):
+        """Save the XML data as an ElementTree to the cache."""
+        filename = self._filename(key)
+        tree = ET.ElementTree(value)  # value is expected to be an ElementTree root element
+        try:
+            # Write the XML data to a file
+            with open(filename, 'wb') as file:
+                tree.write(file, encoding='utf-8', xml_declaration=True)
+        except IOError as e:
+            print(f"Error writing to cache file: {filename} - {e}")
+
+
+class CachingStrategy:
+    __strategy = XMLCache()  # Default caching strategy
+
+    # Dictionary to map string identifiers to caching strategy classes
+    __strategies = {
+        'XML': XMLCache,
+        'JSON': JSONCache,
+        # Add more strategies here if needed
+    }
+
+    @classmethod
+    def use(cls, strategy_name='XML', **kwargs):
+        """
+        Set the caching strategy based on the strategy_name provided.
+
+        Args:
+            strategy_name (str): The name of the caching strategy (e.g., 'XML').
+            **kwargs: Additional keyword arguments to pass when initializing the strategy.
+        """
+        # If a previous strategy exists, close it
+        if cls.__strategy:
+            cls.__strategy.close()
+        
+        # Retrieve the strategy class based on the strategy name
+        strategy_class = cls.__strategies.get(strategy_name)
+        
+        if not strategy_class:
+            raise ValueError(f"Unknown caching strategy: {strategy_name}")
+        
+        # Instantiate the new strategy with the provided arguments
+        cls.__strategy = strategy_class(**kwargs)
+        return cls.__strategy
+
+    @classmethod
+    def get(cls, key):
+        """Get data from the current strategy's cache."""
+        if not cls.__strategy:
+            raise RuntimeError("Caching strategy has not been set.")
+        return cls.__strategy.get(key)
+
+    @classmethod
+    def set(cls, key, value):
+        """Set data in the current strategy's cache."""
+        if not cls.__strategy:
+            raise RuntimeError("Caching strategy has not been set.")
+        cls.__strategy.set(key, value)
+
+
diff --git a/backend/src/overpass/overpass.py b/backend/src/overpass/overpass.py
new file mode 100644
index 0000000..0155f85
--- /dev/null
+++ b/backend/src/overpass/overpass.py
@@ -0,0 +1,114 @@
+from typing import Literal, List
+import urllib
+import json
+import xml.etree.ElementTree as ET
+
+from .caching_strategy import get_cache_key, CachingStrategy
+
+
+ElementTypes = List[Literal['way', 'node', 'relation']]
+
+
+def build_query(area: tuple, element_types: ElementTypes, selector: str, 
+                         conditions=[], out='center'):
+    """
+    Constructs a query string for the Overpass API to retrieve OpenStreetMap (OSM) data.
+
+    Args:
+        area (tuple): A tuple representing the geographical search area, typically in the format 
+                      (radius, latitude, longitude). The first element is a string like "around:2000" 
+                      specifying the search radius, and the second and third elements represent 
+                      the latitude and longitude as floats or strings.
+        element_types (list[str]): A list of OSM element types to search for. Must be one or more of 
+                                   'Way', 'Node', or 'Relation'.
+        selector (str): The key or tag to filter the OSM elements (e.g., 'amenity', 'highway', etc.).
+        conditions (list, optional): A list of conditions to apply as additional filters for the 
+                                     selected OSM elements. The conditions should be written in 
+                                     the Overpass QL format, and they are combined with '&&' if 
+                                     multiple are provided. Defaults to an empty list.
+        out (str, optional): Specifies the output type, such as 'center', 'body', or 'tags'. 
+                             Defaults to 'center'.
+
+    Returns:
+        str: The constructed Overpass QL query string.
+
+    Notes:
+        - If no conditions are provided, the query will just use the `selector` to filter the OSM 
+          elements without additional constraints.
+        - The search area must always formatted as "(radius, lat, lon)".
+    """
+    if not isinstance(conditions, list) :
+        conditions = [conditions]
+
+    query = '('
+
+    # Round the radius to nearest 50 and coordinates to generate less queries
+    search_radius = round(area[0] / 50) * 50
+    loc = tuple((round(area[1], 2), round(area[2], 2)))
+    search_area = f"(around:{search_radius}, {str(loc[0])}, {str(loc[1])})"
+
+    if conditions :
+        conditions = '(if: ' + ' && '.join(conditions) + ')'
+    else :
+        conditions = ''
+
+    for elem in element_types :
+        query += elem + '[' + selector + ']' + conditions + search_area + ';'
+
+    query += ');' + f'out {out};'
+
+    return query
+
+
+def send_overpass_query(query: str, use_cache: bool = True) -> dict:
+    """
+    Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
+
+    Args:
+        query (str): The Overpass QL query to be sent to the Overpass API.
+
+    Returns:
+        dict: The parsed JSON response from the Overpass API, or None if the request fails.
+    """
+
+    # Generate a cache key for the current query
+    cache_key = get_cache_key(query)
+
+    # Try to fetch the result from the cache
+    cached_response = CachingStrategy.get(cache_key)
+    if cached_response:
+        print("Cache hit!")
+        return cached_response
+
+    # Define the Overpass API endpoint
+    overpass_url = "https://overpass-api.de/api/interpreter"
+
+    # Prepare the data to be sent as POST request, encoded as bytes
+    data = urllib.parse.urlencode({'data': query}).encode('utf-8')
+
+    # Create a custom header with a User-Agent
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (compatible; OverpassQuery/1.0; +http://example.com)',
+    }
+
+    try:
+        # Create a Request object with the specified URL, data, and headers
+        request = urllib.request.Request(overpass_url, data=data, headers=headers)
+        
+        # Send the request and read the response
+        with urllib.request.urlopen(request) as response:
+            # Read and decode the response
+            response_data = response.read().decode('utf-8')
+            root = ET.fromstring(response_data)
+
+            # Cache the response data as an ElementTree root
+            CachingStrategy.set(cache_key, root)
+
+            return root
+
+    except urllib.error.URLError as e:
+        print(f"Error connecting to Overpass API: {e}")
+        return None
+    except json.JSONDecodeError:
+        print("Error decoding the JSON response from Overpass API.")
+        return None
diff --git a/backend/src/tests/test_main.py b/backend/src/tests/test_main.py
index 6521e0d..046e3db 100644
--- a/backend/src/tests/test_main.py
+++ b/backend/src/tests/test_main.py
@@ -11,7 +11,7 @@ def client():
     """Client used to call the app."""
     return TestClient(app)
 
-'''
+
 def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     """
     Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
@@ -54,7 +54,7 @@ def test_turckheim(client, request):    # pylint: disable=redefined-outer-name
     assert len(landmarks) > 2           # check that there is something to visit
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     # assert 2==3
-'''
+
 
 def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     """
@@ -97,7 +97,7 @@ def test_bellecour(client, request) :   # pylint: disable=redefined-outer-name
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
     # assert 2 == 3
 
-'''
+
 def test_cologne(client, request) :   # pylint: disable=redefined-outer-name
     """
     Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
@@ -336,7 +336,7 @@ def test_shopping(client, request) :   # pylint: disable=redefined-outer-name
     assert response.status_code == 200  # check for successful planning
     assert comp_time < 30, f"Computation time exceeded 30 seconds: {comp_time:.2f} seconds"
     assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
-'''
+
 
 # def test_new_trip_single_prefs(client):
 #     response = client.post(
diff --git a/backend/src/utils/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
index 0d8e116..7c28e35 100644
--- a/backend/src/utils/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -1,12 +1,15 @@
 """Module used to import data from OSM and arrange them in categories."""
 import logging
 import yaml
+import xml.etree.ElementTree as ET
+
 
 from ..structs.preferences import Preferences
 from ..structs.landmark import Landmark
 from .take_most_important import take_most_important
 from .cluster_manager import ClusterManager
-from .overpass import OverpassQueryBuilder, send_overpass_query, parse_result
+from ..overpass.overpass import build_query, send_overpass_query
+from ..overpass.caching_strategy import CachingStrategy
 
 from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
 
@@ -53,8 +56,7 @@ class LandmarkManager:
             self.walking_speed = parameters['average_walking_speed']
             self.detour_factor = parameters['detour_factor']
 
-        # self.overpass = Overpass()
-        # CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
+        CachingStrategy.use('XML', cache_dir=OSM_CACHE_DIR)
 
         self.logger.info('LandmakManager successfully initialized.')
 
@@ -84,35 +86,32 @@ class LandmarkManager:
         all_landmarks = set()
 
         # Create a bbox using the around technique, tuple of strings
-        bbox = tuple((f"around:{min(2000, reachable_bbox_side/2)}", str(center_coordinates[0]), str(center_coordinates[1])))
+        bbox = tuple((min(2000, reachable_bbox_side/2), center_coordinates[0], center_coordinates[1]))
 
         # list for sightseeing
         if preferences.sightseeing.score != 0:
             self.logger.debug('Fetching sightseeing landmarks...')
-            score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, preferences.sightseeing.score)
             all_landmarks.update(current_landmarks)
             self.logger.debug('Fetching sightseeing clusters...')
 
             # special pipeline for historic neighborhoods
-            neighborhood_manager = ClusterManager(bbox, 'sightseeing')
-            historic_clusters = neighborhood_manager.generate_clusters()
-            all_landmarks.update(historic_clusters)
-            self.logger.debug('Sightseeing clusters done')
+            # neighborhood_manager = ClusterManager(bbox, 'sightseeing')
+            # historic_clusters = neighborhood_manager.generate_clusters()
+            # all_landmarks.update(historic_clusters)
+            # self.logger.debug('Sightseeing clusters done')
 
         # list for nature
         if preferences.nature.score != 0:
             self.logger.debug('Fetching nature landmarks...')
-            score_function = lambda score: score * 10 * self.nature_coeff * preferences.nature.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, preferences.nature.score)
             all_landmarks.update(current_landmarks)
 
 
         # list for shopping
         if preferences.shopping.score != 0:
             self.logger.debug('Fetching shopping landmarks...')
-            score_function = lambda score: score * 10 * preferences.shopping.score / 5
-            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
+            current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, preferences.shopping.score)
             self.logger.debug('Fetching shopping clusters...')
 
             # set time for all shopping activites :
@@ -121,10 +120,10 @@ class LandmarkManager:
             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)
-            self.logger.debug('Shopping clusters done')
+            # shopping_manager = ClusterManager(bbox, 'shopping')
+            # shopping_clusters = shopping_manager.generate_clusters()
+            # all_landmarks.update(shopping_clusters)
+            # self.logger.debug('Shopping clusters done')
 
 
 
@@ -133,8 +132,19 @@ class LandmarkManager:
 
         return all_landmarks, landmarks_constrained
 
-    def set_score(self, landmark: Landmark, landmarktype: str, preference_level: int) :
+    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
@@ -144,11 +154,13 @@ class LandmarkManager:
             score *= self.wikipedia_bonus
         if landmark.is_place_of_worship :
             score *= self.church_coeff
+        if landmarktype == 'nature' :
+            score *= self.nature_coeff
 
-        landmark.attractiveness = int(score * preference_level)
+        landmark.attractiveness = int(score * preference_level * 2)
 
-    '''
-    def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
+
+    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.
 
@@ -183,165 +195,7 @@ class LandmarkManager:
                 query_conditions = []
                 element_types.append('node')
 
-            query = OverpassQueryBuilder(
-                area = bbox,
-                element_types = element_types,
-                selector = sel,
-                conditions = query_conditions,        # except for nature....
-                out = 'center'
-                )
-            self.logger.debug(f"Query: {query}")
-
-            try:
-                result = self.overpass.query(query)
-            except Exception as e:
-                self.logger.error(f"Error fetching landmarks: {e}")
-                continue
-
-            for elem in result.elements():
-
-                name = elem.tag('name')
-                location = (elem.centerLat(), elem.centerLon())
-                osm_type = elem.type()              # Add type: 'way' or 'relation'
-                osm_id = elem.id()                  # Add OSM id
-
-                # TODO: exclude these from the get go
-                # handle unprecise and no-name locations
-                if name is None or location[0] is None:
-                    if osm_type == 'node' and 'viewpoint' in elem.tags().values():
-                        name = 'Viewpoint'
-                        name_en = 'Viewpoint'
-                        location = (elem.lat(), elem.lon())
-                    else :
-                        continue
-
-                # skip if part of another building
-                if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
-                    continue
-
-                elem_type = landmarktype                # Add the landmark type as 'sightseeing,
-                n_tags = len(elem.tags().keys())        # Add number of tags
-                score = n_tags**self.tag_exponent       # Add score
-                duration = 5                            # Set base duration to 5 minutes
-                # skip = False                            # Set skipping parameter to false
-                tag_values = set(elem.tags().values())  # Store tag values
-
-
-                # Retrieve image, name and website :
-                image_url = elem.tag('image')
-                website_url = elem.tag('website')
-                if website_url is None :
-                    website_url = elem.tag('wikipedia')
-                name_en = elem.tag('name:en')
-
-                if elem_type != "nature" and elem.tag('leisure') == "park":
-                    elem_type = "nature"
-
-                if elem.tag('wikipedia') is not None :
-                    score += self.wikipedia_bonus
-
-                # Skip element if it is an administrative boundary or a disused thing or it is an appartement and useless amenities
-                if elem.tag('boundary') is not None or elem.tag('disused') is not None:
-                    continue
-                if 'apartments' in elem.tags().values():
-                    continue
-                if elem.tag('historic') is not None and elem.tag('historic') in ['manor', 'optical_telegraph', 'pound', 'shieling', 'wayside_cross']:
-                    continue
-
-                # Adjust scoring, browse through tag keys using wildcards
-                for tag_key in elem.tags().keys():
-                    if "pay" in tag_key:
-                        # payment options are misleading and should not count for the scoring.
-                        score += self.pay_bonus
-
-                    if "building:" in tag_key:
-                        # do not count the building description as being particularly useful
-                        n_tags -= 1
-
-                    # if landmarktype != "shopping":
-                    #     if "shop" in tag_key:
-                    #         skip = True
-                    #         break
-                        # if tag_key == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
-                        #     skip = True
-                        #     break
-
-                # if skip:
-                #     continue
-
-                score = score_function(score)
-
-                if "place_of_worship" in tag_values :
-                    if 'cathedral' in tag_values :
-                        duration = 10
-                    else :
-                        score *= self.church_coeff
-
-                elif 'viewpoint' in tag_values :
-                    # viewpoints must count more
-                    score = score * self.viewpoint_bonus
-
-                elif "museum" in tag_values or "aquarium" in tag_values or "planetarium" in tag_values:
-                    duration = 60
-
-                # finally create our own landmark object
-                landmark = Landmark(
-                    name = name,
-                    type = elem_type,
-                    location = location,
-                    osm_type = osm_type,
-                    osm_id = osm_id,
-                    attractiveness = int(score),
-                    must_do = False,
-                    n_tags = int(n_tags),
-                    duration = int(duration),
-                    name_en = name_en,
-                    image_url = image_url,
-                    website_url = website_url
-                )
-                return_list.append(landmark)
-
-        self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
-
-        return return_list
-    '''
-
-    def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> 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').
-            score_function (callable): The function to compute the score of the landmark based on its attributes.
-
-        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.
-            - Scores are assigned to landmarks based on their attributes and surrounding elements.
-        """
-        return_list = []
-
-        if landmarktype == 'nature' : query_conditions = []
-        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}")
-
-            element_types = ['way', 'relation']
-
-            if 'viewpoint' in sel :
-                query_conditions = []
-                element_types.append('node')
-
-            query = OverpassQueryBuilder(
+            query = build_query(
                 area = bbox,
                 element_types = element_types,
                 selector = sel,
@@ -356,12 +210,110 @@ class LandmarkManager:
                 self.logger.error(f"Error fetching landmarks: {e}")
                 continue
                 
-            return_list = parse_result(result, landmarktype)
+            return_list += self.parse_overpass_result(result, landmarktype, preference_level)
 
         self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
 
         return return_list
 
+
+    def parse_overpass_result(self, root: ET.Element, landmarktype, preference_level) -> list[Landmark]:
+        """
+        Parse the Overpass API result and extract landmarks.
+
+        This method processes the XML root element 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:
+        root (ET.Element): The root element of the XML 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 XML data.
+        """
+        if root is None :
+            return []
+
+        landmarks = []
+        for osm_type in ['node', 'way', 'relation'] :
+            for elem in root.findall(osm_type):
+                # self.logger.debug('new landmark')
+
+                # Extract basic info from the landmark.
+                name = elem.find("tag[@k='name']").get('v') if elem.find("tag[@k='name']") is not None else None
+                center = elem.find('center')
+                tags = elem.findall('tag')
+
+                # Extract the center latitude and longitude if available.
+                if name is not None and center is not None:
+                    lat = float(center.get('lat'))
+                    lon = float(center.get('lon'))
+                    coords = tuple((lat, lon))
+                else :
+                    continue
+                
+                # Convert this to Landmark object
+                landmark = Landmark(name=name,
+                                    type=landmarktype,
+                                    location=coords,
+                                    osm_id=elem.get('id'), 
+                                    osm_type=osm_type,
+                                    attractiveness=0,
+                                    n_tags=len(tags))
+
+                # Browse through tags to add information to landmark.
+                for tag in tags:
+                    key = tag.get('k')
+                    value = tag.get('v')
+
+                    # 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 key == '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
+                    else :
+                        landmark.duration = 5
+
+                else: 
+                    self.set_landmark_score(landmark, landmarktype, preference_level)
+                    landmarks.append(landmark)
+                    # self.logger.debug('new landmark added')
+                continue
+
+        return landmarks
+
 def dict_to_selector_list(d: dict) -> list:
     """
     Convert a dictionary of key-value pairs to a list of Overpass query strings.
diff --git a/backend/src/utils/overpass.py b/backend/src/utils/overpass.py
deleted file mode 100644
index 2dccb30..0000000
--- a/backend/src/utils/overpass.py
+++ /dev/null
@@ -1,199 +0,0 @@
-from typing import Literal, List
-import urllib
-import json
-import xml.etree.ElementTree as ET
-
-from ..structs.landmark import Landmark
-
-ElementTypes = List[Literal['way', 'node', 'relation']]
-
-
-
-def OverpassQueryBuilder(area: tuple, element_types: ElementTypes, selector: str, 
-                         conditions=[], out='center'):
-    """
-    Constructs a query string for the Overpass API to retrieve OpenStreetMap (OSM) data.
-
-    Args:
-        area (tuple): A tuple representing the geographical search area, typically in the format 
-                      (radius, latitude, longitude). The first element is a string like "around:2000" 
-                      specifying the search radius, and the second and third elements represent 
-                      the latitude and longitude as floats or strings.
-        element_types (list[str]): A list of OSM element types to search for. Must be one or more of 
-                                   'Way', 'Node', or 'Relation'.
-        selector (str): The key or tag to filter the OSM elements (e.g., 'amenity', 'highway', etc.).
-        conditions (list, optional): A list of conditions to apply as additional filters for the 
-                                     selected OSM elements. The conditions should be written in 
-                                     the Overpass QL format, and they are combined with '&&' if 
-                                     multiple are provided. Defaults to an empty list.
-        out (str, optional): Specifies the output type, such as 'center', 'body', or 'tags'. 
-                             Defaults to 'center'.
-
-    Returns:
-        str: The constructed Overpass QL query string.
-
-    Notes:
-        - If no conditions are provided, the query will just use the `selector` to filter the OSM 
-          elements without additional constraints.
-        - The search area must always formatted as "(radius, lat, lon)".
-    """
-    if not isinstance(conditions, list) :
-        conditions = [conditions]
-
-    query = '('
-    search_area = f"({', '.join(map(str, area))})"
-
-    if conditions :
-        conditions = '(if: ' + ' && '.join(conditions) + ')'
-    else :
-        conditions = ''
-
-    for elem in element_types :
-        query += elem + '[' + selector + ']' + conditions + search_area + ';'
-
-    query += ');' + f'out {out};'
-
-    return query
-
-
-def send_overpass_query(query: str) -> dict:
-    """
-    Sends the Overpass QL query to the Overpass API and returns the parsed JSON response.
-
-    Args:
-        query (str): The Overpass QL query to be sent to the Overpass API.
-
-    Returns:
-        dict: The parsed JSON response from the Overpass API, or None if the request fails.
-    """
-
-    # Define the Overpass API endpoint
-    overpass_url = "https://overpass-api.de/api/interpreter"
-
-    # Prepare the data to be sent as POST request, encoded as bytes
-    data = urllib.parse.urlencode({'data': query}).encode('utf-8')
-
-    # Create a custom header with a User-Agent
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (compatible; OverpassQuery/1.0; +http://example.com)',
-    }
-
-    try:
-        # Create a Request object with the specified URL, data, and headers
-        request = urllib.request.Request(overpass_url, data=data, headers=headers)
-        
-        # Send the request and read the response
-        with urllib.request.urlopen(request) as response:
-            # Read and decode the response
-            response_data = response.read().decode('utf-8')
-            return ET.fromstring(response_data)
-
-    except urllib.error.URLError as e:
-        print(f"Error connecting to Overpass API: {e}")
-        return None
-    except json.JSONDecodeError:
-        print("Error decoding the JSON response from Overpass API.")
-        return None
-
-
-def parse_result(root: ET.Element, elem_type) -> List[Landmark]:
-
-    landmarks = []
-    if root is None :
-        return landmarks
-
-    for osm_type in ['node', 'way', 'relation'] :
-        for elem in root.findall(osm_type):
-
-            # Extract basic info from the landmark.
-            name = elem.find("tag[@k='name']").get('v') if elem.find("tag[@k='name']") is not None else None
-            center = elem.find('center')
-            tags = elem.findall('tag')
-
-            # Extract the center latitude and longitude if available.
-            if name is not None and center is not None:
-                lat = float(center.get('lat'))
-                lon = float(center.get('lon'))
-                coords = tuple((lat, lon))
-            else :
-                continue
-            
-            # Convert this to Landmark object
-            landmark = Landmark(name=name,
-                                type=elem_type,
-                                location=coords,
-                                osm_id=elem.get('id'), 
-                                osm_type=osm_type,
-                                attractiveness=0,
-                                n_tags=len(tags))
-
-            # Browse through tags to add information to landmark.
-            for tag in tags:
-                key = tag.get('k')
-                value = tag.get('v')
-
-                # 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 elem_type != '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 key == '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
-                else :
-                    landmark.duration = 5
-
-            else: 
-                set_score(landmark, elem_type)
-                landmarks.append(landmark)
-            continue
-
-    return landmarks
-
-
-
-def set_score(landmark: Landmark, landmarktype: str) :
-
-        score = landmark.n_tags**1.15
-        if landmark.wiki_url :
-            score *= 1.1
-        if landmark.image_url :
-            score *= 1.1
-        if landmark.website_url :
-            score *= 1.1
-        if landmark.is_place_of_worship :
-            score *= 0.65
-        if landmark.is_viewpoint :
-            # print(f"{landmark.name}:  n_tags={landmark.n_tags} and score={score*3*1.35*10}")
-            score *= 3
-        if landmarktype == 'nature' :
-            score *= 1.35
-
-        landmark.attractiveness = int(score * 10)
