diff --git a/.gitea/workflows/backed_build-image.yaml b/.gitea/workflows/backend_build-image.yaml
similarity index 95%
rename from .gitea/workflows/backed_build-image.yaml
rename to .gitea/workflows/backend_build-image.yaml
index 5542a1f..1552c03 100644
--- a/.gitea/workflows/backed_build-image.yaml
+++ b/.gitea/workflows/backend_build-image.yaml
@@ -2,6 +2,8 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - backend/**
 
 name: Build and push docker image
 
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 79dbe76..056c3a0 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -6,6 +6,12 @@ COPY Pipfile Pipfile.lock .
 RUN pip install pipenv
 RUN pipenv install --deploy --system
 
-COPY . /src
+COPY src src
 
-CMD ["pipenv", "run", "python", "/app/src/main.py"]
+EXPOSE 8000
+
+# Set environment variables used by the deployment. These can be overridden by the user using this image.
+ENV NUM_WORKERS=1
+ENV OSM_CACHE_DIR=/cache
+
+CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
diff --git a/backend/Pipfile b/backend/Pipfile
index 2757979..4197132 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -7,7 +7,6 @@ name = "pypi"
 numpy = "*"
 scipy = "*"
 fastapi = "*"
-osmpythontools = "*"
 pydantic = "*"
 shapely = "*"
 networkx = "*"
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index d3b6eef..d661287 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -30,14 +30,6 @@
             "markers": "python_version >= '3.8'",
             "version": "==4.4.0"
         },
-        "beautifulsoup4": {
-            "hashes": [
-                "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
-                "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"
-            ],
-            "markers": "python_full_version >= '3.6.0'",
-            "version": "==4.12.3"
-        },
         "certifi": {
             "hashes": [
                 "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b",
@@ -142,6 +134,102 @@
             "markers": "python_full_version >= '3.7.0'",
             "version": "==3.3.2"
         },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
+                "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
+                "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
+                "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
+                "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
+                "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
+                "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
+                "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
+                "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
+                "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
+                "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
+                "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
+                "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
+                "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
+                "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
+                "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
+                "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
+                "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
+                "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
+                "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
+                "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
+                "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
+                "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
+                "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
+                "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
+                "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269",
+                "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96",
+                "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d",
+                "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a",
+                "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4",
+                "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77",
+                "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d",
+                "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0",
+                "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
+                "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068",
+                "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac",
+                "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25",
+                "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8",
+                "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab",
+                "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26",
+                "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
+                "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
+                "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f",
+                "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5",
+                "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
+                "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c",
+                "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d",
+                "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
+                "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa",
+                "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a",
+                "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03",
+                "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b",
+                "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04",
+                "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c",
+                "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001",
+                "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458",
+                "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389",
+                "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99",
+                "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985",
+                "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537",
+                "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238",
+                "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f",
+                "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d",
+                "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796",
+                "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a",
+                "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143",
+                "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8",
+                "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c",
+                "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5",
+                "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5",
+                "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711",
+                "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4",
+                "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6",
+                "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c",
+                "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7",
+                "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4",
+                "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b",
+                "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae",
+                "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12",
+                "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c",
+                "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae",
+                "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8",
+                "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887",
+                "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b",
+                "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4",
+                "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f",
+                "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5",
+                "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33",
+                "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519",
+                "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"
+            ],
+            "markers": "python_full_version >= '3.7.0'",
+            "version": "==3.3.2"
+        },
         "click": {
             "hashes": [
                 "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
@@ -150,64 +238,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==8.1.7"
         },
-        "contourpy": {
-            "hashes": [
-                "sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2",
-                "sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9",
-                "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9",
-                "sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4",
-                "sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce",
-                "sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7",
-                "sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f",
-                "sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922",
-                "sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4",
-                "sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e",
-                "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b",
-                "sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619",
-                "sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205",
-                "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480",
-                "sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965",
-                "sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c",
-                "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd",
-                "sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5",
-                "sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f",
-                "sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc",
-                "sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec",
-                "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd",
-                "sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b",
-                "sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9",
-                "sha256:a12a813949e5066148712a0626895c26b2578874e4cc63160bb007e6df3436fe",
-                "sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce",
-                "sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609",
-                "sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8",
-                "sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0",
-                "sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f",
-                "sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8",
-                "sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b",
-                "sha256:bd3db01f59fdcbce5b22afad19e390260d6d0222f35a1023d9adc5690a889364",
-                "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040",
-                "sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f",
-                "sha256:d31a63bc6e6d87f77d71e1abbd7387ab817a66733734883d1fc0021ed9bfa083",
-                "sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df",
-                "sha256:ddcb8581510311e13421b1f544403c16e901c4e8f09083c881fab2be80ee31ba",
-                "sha256:e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445",
-                "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da",
-                "sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3",
-                "sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72",
-                "sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02",
-                "sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==1.2.1"
-        },
-        "cycler": {
-            "hashes": [
-                "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30",
-                "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==0.12.1"
-        },
         "dnspython": {
             "hashes": [
                 "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50",
@@ -249,7 +279,7 @@
             "markers": "python_version >= '3.8'",
             "version": "==0.0.4"
         },
-        "fonttools": {
+        "geopandas": {
             "hashes": [
                 "sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122",
                 "sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397",
@@ -403,264 +433,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.1.4"
         },
-        "kiwisolver": {
-            "hashes": [
-                "sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf",
-                "sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e",
-                "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af",
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-                "sha256:cd9e78285da6c9ba2d5c769628f43ef66d96ac3085e59b10ad4f3707980710d3",
-                "sha256:cf2a978c795b54c539f47964ec05e35c05bd045db5ca1e8366988c7f2fe6b3ce",
-                "sha256:d14a0d029a4e176795cef99c056d58067c06195e0c7e2dbb293bf95c08f772a3",
-                "sha256:d237ba6664b8e60fd90b8549a149a74fcc675272e0e95539a00522e4ca688b04",
-                "sha256:d26a618ae1766279f2660aca0081b2220aca6bd1aa06b2cf73f07383faf48927",
-                "sha256:d28cb356f119a437cc58a13f8135ab8a4c8ece18159eb9194b0d269ec4e28083",
-                "sha256:d4ed0c7cbecde7194cd3228c044e86bf73e30a23505af852857c09c24e77ec5d",
-                "sha256:d83e2d94b69bf31ead2fa45f0acdef0757fa0458a129734f59f67f3d2eb7ef32",
-                "sha256:d8bbcd21769594dbba9c37d3c819e2d5847656ca99c747ddb31ac1701d0c0ed9",
-                "sha256:d9b342c76003c6b9336a80efcc766748a333573abf9350f4094ee46b006ec18f",
-                "sha256:dc911208b18842a3a57266d8e51fc3cfaccee90a5351b92079beed912a7914c2",
-                "sha256:dfa7c241073d8f2b8e8dbc7803c434f57dbb83ae2a3d7892dd068d99e96efe2c",
-                "sha256:e282aedd63c639c07c3857097fc0e236f984ceb4089a8b284da1c526491e3f3d",
-                "sha256:e290d79a4107d7d794634ce3e985b9ae4f920380a813717adf61804904dc4393",
-                "sha256:e3d9d13603410b72787579769469af730c38f2f25505573a5888a94b62b920f8",
-                "sha256:e481bba1e11ba585fb06db666bfc23dbe181dbafc7b25776156120bf12e0d5a6",
-                "sha256:e49b052b768bb74f58c7dda4e0bdf7b79d43a9204ca584ffe1fb48a6f3c84c66",
-                "sha256:eb00b549b13bd6d884c863554566095bf6fa9c3cecb2e7b399c4bc7904cb33b5",
-                "sha256:ec87c44f619380878bd49ca109669c9f221d9ae6883a5bcb3616785fa8f94c97",
-                "sha256:edcfa83e03370032a489430215c1e7783128808fd3e2e0a3225deee278585196",
-                "sha256:f11ae142f3a322d44513de1018b50f474f8f736bc3cd91d969f464b5bfef8836",
-                "sha256:f2a09f6184f17a80897172863a655467da2b11151ec98ba8d7af89f17bf63dae",
-                "sha256:f5b65529bb2f21ac7861a0e94fdbf5dc0daab41497d18223b46ee8515e5ad297",
-                "sha256:f60fdd125d85bf9c279ffb8e94c78c51b3b6a37711464e1f5f31078b45002421",
-                "sha256:f61efaf4bed1cc0860e567d2ecb2363974d414f7f1f124b1df368bbf183453a6",
-                "sha256:f90e552ecbad426eab352e7b2933091f2be77115bb16f09f78404861c8322981",
-                "sha256:f956196ef61369f1685d14dad80611488d8dc1ef00be57c0c5a03064005b0f30",
-                "sha256:fb91819461b1b56d06fa4bcf86617fac795f6a99d12239fb0c68dbeba41a0a30",
-                "sha256:fbc9d316552f9ef7bba39f4edfad4a734d3d6f93341232a9dddadec4f15d425f",
-                "sha256:ff69a9a0b4b17d78170c73abe2ab12084bdf1691550c5629ad1fe7849433f324",
-                "sha256:ffb2be176fed4457e445fe540617f0252a72a8bc56208fd65a690fdb1f57660b"
-            ],
-            "markers": "python_version >= '3.6'",
-            "version": "==5.2.2"
-        },
         "markdown-it-py": {
             "hashes": [
                 "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1",
@@ -874,10 +646,12 @@
         },
         "osmpythontools": {
             "hashes": [
-                "sha256:13ff721f760fdad5dd78b4d1461d286b78bba96ee151a7301ee8c11a0c258be9"
+                "sha256:22548d86d68d36edff3cf9ab76c45745cda86a4ea0b28442e107d6b42992a426",
+                "sha256:ac67bea77b521941af648ef641ae1d006101948d1112475c256ea23ef31b426a"
             ],
             "index": "pypi",
-            "version": "==0.3.5"
+            "markers": "python_version >= '3.8'",
+            "version": "==1.9.3"
         },
         "packaging": {
             "hashes": [
@@ -1121,13 +895,70 @@
             "markers": "python_version >= '3.8'",
             "version": "==2.18.0"
         },
-        "pyparsing": {
+        "pyogrio": {
             "hashes": [
-                "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad",
-                "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"
+                "sha256:019731a856a9abfe909e86f50eb13f8362f6742337caf757c54b7c8acfe75b89",
+                "sha256:083351b258b3e08b6c6085dac560bd321b68de5cb4a66229095da68d5f3d696b",
+                "sha256:13642608a1cd67797ae8b5d792b0518d8ef3eb76506c8232ab5eaa1ea1159dff",
+                "sha256:17420febc17651876d5140b54b24749aa751d482b5f9ef6267b8053e6e962876",
+                "sha256:1a495ca4fb77c69595747dd688f8f17bb7d2ea9cd86603aa71c7fc98cc8b4174",
+                "sha256:2829615cf58b1b24a9f96fea42abedaa1a800dd351c67374cc2f6341138608f3",
+                "sha256:2e98913fa183f7597c609e774820a149e9329fd2a0f8d33978252fbd00ae87e6",
+                "sha256:2f2ec57ab74785db9c2bf47c0a6731e5175595a13f8253f06fa84136adb310a9",
+                "sha256:30cbeeaedb9bced7012487e7438919aa0c7dfba18ac3d4315182b46eb3139b9d",
+                "sha256:3a2fcaa269031dbbc8ebd91243c6452c5d267d6df939c008ab7533413c9cf92d",
+                "sha256:3f964002d445521ad5b8e732a6b5ef0e2d2be7fe566768e5075c1d71398da64a",
+                "sha256:4a289584da6df7ca318947301fe0ba9177e7f863f63110e087c80ac5f3658de8",
+                "sha256:4da0b9deb380bd9a200fee13182c4f95b02b4c554c923e2e0032f32aaf1439ed",
+                "sha256:4e0f90a6c3771ee1f1fea857778b4b6a1b64000d851b819f435f9091b3c38c60",
+                "sha256:6a6fa2e8cf95b3d4a7c0fac48bce6e5037579e28d3eb33b53349d6e11f15e5a8",
+                "sha256:6dc94a67163218581c7df275223488ac9b31dc582ccd756da607c3338908566c",
+                "sha256:796e4f6a4e769b2eb6fea9a10546ea4bdee16182d1e29802b4d6349363c3c1d7",
+                "sha256:7fcafed24371fe6e23bcf5abebbb29269f8d79915f1dd818ac85453657ea714a",
+                "sha256:9440466c0211ac81f3417f274da5903f15546b486f76b2f290e74a56aaf0e737",
+                "sha256:959022f3ad04053f8072dc9a2ad110c46edd9e4f92352061ba835fc91df3ca96",
+                "sha256:d668cb10f2bf6ccd7c402f91e8b06290722dd09dbe265ae95b2c13db29ebeba0",
+                "sha256:e38c3c6d37cf2cc969407e4d051dcb507cfd948eb26c7b0840c4f7d7d4a71bd4",
+                "sha256:f47c9b6818cc0f420015b672d5dcc488530a5ee63e5ba35a184957b21ea3922a",
+                "sha256:f5d80eb846be4fc4e642cbedc1ed0c143e8d241653382ecc76a7620bbd2a5c3a",
+                "sha256:f8bf193269ea9d347ac3ddada960a59f1ab2e4a5c009be95dc70e6505346b2fc",
+                "sha256:fb04bd80964428491951766452f0071b0bc37c7d38c45ef02502dbd83e5d74a0"
             ],
-            "markers": "python_full_version >= '3.6.8'",
-            "version": "==3.1.2"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.9.0"
+        },
+        "pyproj": {
+            "hashes": [
+                "sha256:18faa54a3ca475bfe6255156f2f2874e9a1c8917b0004eee9f664b86ccc513d3",
+                "sha256:1e9fbaf920f0f9b4ee62aab832be3ae3968f33f24e2e3f7fbb8c6728ef1d9746",
+                "sha256:2d6ff73cc6dbbce3766b6c0bce70ce070193105d8de17aa2470009463682a8eb",
+                "sha256:36b64c2cb6ea1cc091f329c5bd34f9c01bb5da8c8e4492c709bda6a09f96808f",
+                "sha256:38a3361941eb72b82bd9a18f60c78b0df8408416f9340521df442cebfc4306e2",
+                "sha256:447db19c7efad70ff161e5e46a54ab9cc2399acebb656b6ccf63e4bc4a04b97a",
+                "sha256:44aa7c704c2b7d8fb3d483bbf75af6cb2350d30a63b144279a09b75fead501bf",
+                "sha256:4ba1f9b03d04d8cab24d6375609070580a26ce76eaed54631f03bab00a9c737b",
+                "sha256:4bc0472302919e59114aa140fd7213c2370d848a7249d09704f10f5b062031fe",
+                "sha256:50100b2726a3ca946906cbaa789dd0749f213abf0cbb877e6de72ca7aa50e1ae",
+                "sha256:5279586013b8d6582e22b6f9e30c49796966770389a9d5b85e25a4223286cd3f",
+                "sha256:6420ea8e7d2a88cb148b124429fba8cd2e0fae700a2d96eab7083c0928a85110",
+                "sha256:65ad699e0c830e2b8565afe42bd58cc972b47d829b2e0e48ad9638386d994915",
+                "sha256:6d227a865356f225591b6732430b1d1781e946893789a609bb34f59d09b8b0f8",
+                "sha256:7a27151ddad8e1439ba70c9b4b2b617b290c39395fa9ddb7411ebb0eb86d6fb0",
+                "sha256:80fafd1f3eb421694857f254a9bdbacd1eb22fc6c24ca74b136679f376f97d35",
+                "sha256:83039e5ae04e5afc974f7d25ee0870a80a6bd6b7957c3aca5613ccbe0d3e72bf",
+                "sha256:8b8acc31fb8702c54625f4d5a2a6543557bec3c28a0ef638778b7ab1d1772132",
+                "sha256:9274880263256f6292ff644ca92c46d96aa7e57a75c6df3f11d636ce845a1877",
+                "sha256:ab7aa4d9ff3c3acf60d4b285ccec134167a948df02347585fdd934ebad8811b4",
+                "sha256:c41e80ddee130450dcb8829af7118f1ab69eaf8169c4bf0ee8d52b72f098dc2f",
+                "sha256:db3aedd458e7f7f21d8176f0a1d924f1ae06d725228302b872885a1c34f3119e",
+                "sha256:e7e13c40183884ec7f94eb8e0f622f08f1d5716150b8d7a134de48c6110fee85",
+                "sha256:ebfbdbd0936e178091309f6cd4fcb4decd9eab12aa513cdd9add89efa3ec2882",
+                "sha256:fd43bd9a9b9239805f406fd82ba6b106bf4838d9ef37c167d3ed70383943ade1",
+                "sha256:fd93c1a0c6c4aedc77c0fe275a9f2aba4d59b8acf88cebfc19fe3c430cfabf4f",
+                "sha256:fffb059ba3bced6f6725961ba758649261d85ed6ce670d3e3b0a26e81cf1aa8d"
+            ],
+            "markers": "python_version >= '3.9'",
+            "version": "==3.6.1"
         },
         "python-dateutil": {
             "hashes": [
@@ -1340,14 +1171,6 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.3.1"
         },
-        "soupsieve": {
-            "hashes": [
-                "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690",
-                "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"
-            ],
-            "markers": "python_version >= '3.8'",
-            "version": "==2.5"
-        },
         "starlette": {
             "hashes": [
                 "sha256:6fe59f29268538e5d0d182f2791a479a0c64638e6935d1c6989e63fb2699c6ee",
@@ -1675,14 +1498,6 @@
                 "sha256:ffefa1374cd508d633646d51a8e9277763a9b78ae71324183693959cf94635a7"
             ],
             "version": "==12.0"
-        },
-        "xarray": {
-            "hashes": [
-                "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
-                "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
-            ],
-            "markers": "python_version >= '3.9'",
-            "version": "==2024.6.0"
         }
     },
     "develop": {}
diff --git a/backend/src/amenities/shopping.am b/backend/src/amenities/shopping.am
deleted file mode 100644
index 14504a0..0000000
--- a/backend/src/amenities/shopping.am
+++ /dev/null
@@ -1,2 +0,0 @@
-'shop'='department_store'
-'shop'='mall'
\ No newline at end of file
diff --git a/backend/src/constants.py b/backend/src/constants.py
new file mode 100644
index 0000000..d5b7b5a
--- /dev/null
+++ b/backend/src/constants.py
@@ -0,0 +1,19 @@
+from pathlib import Path
+import os
+import logging
+
+PARAMETERS_DIR = Path('src/parameters')
+AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
+LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
+OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
+
+
+
+cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
+OSM_CACHE_DIR = Path(cache_dir_string)
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(
+    level = logging.INFO,
+    format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
diff --git a/backend/src/example_landmarks_manager.py b/backend/src/example_landmarks_manager.py
new file mode 100644
index 0000000..30df6dc
--- /dev/null
+++ b/backend/src/example_landmarks_manager.py
@@ -0,0 +1,151 @@
+import yaml
+import logging
+from OSMPythonTools import cachingStrategy, overpass
+from structs.landmarks import Landmark, LandmarkType
+from structs.preferences import Preferences, Preference
+import constants
+
+SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
+NATURE = LandmarkType(landmark_type='nature')
+SHOPPING = LandmarkType(landmark_type='shopping')
+
+
+class LandmarkManager:
+    logger = logging.getLogger(__name__)
+
+    def __init__(self) -> None:
+        strategy = cachingStrategy.JSON(cacheDir=constants.OSM_CACHE_DIR)
+        self.query_builder = overpass.Overpass()
+        with constants.AMENITY_SELECTORS_PATH.open('r') as f:
+            self.amenity_selectors = yaml.safe_load(f)
+        with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+            self.parameters = yaml.safe_load(f)
+            # max_distance = parameters['city_bbox_side']
+
+
+    def get_landmark_lists(self, preferences: Preferences, center_coordinates: tuple[float, float]) -> tuple[list[Landmark], list[Landmark]]:
+        '''
+        Generate a list of landmarks based on the preferences of the user and the center (ie. start) coordinates.
+        The list is then used by the pathfinding algorithm to generate a path that goes through the most interesting landmarks.
+        :param preferences: the preferences specified by the user
+        :param center_coordinates: the coordinates of the starting point
+        '''
+
+        L = []
+
+        # List for sightseeing
+        if preferences.sightseeing.score != 0:
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])  
+            L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L1, preferences.sightseeing)
+            L += L1
+
+        # List for nature
+        if preferences.nature.score != 0:
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])  
+            L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L2, preferences.nature)
+            L += L2
+
+        # List for shopping
+        if preferences.shopping.score != 0:
+            score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']))
+            L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, center_coordinates, self.parameters['city_bbox_side'], score_func)
+            self.correct_score(L3, preferences.shopping)
+            L += L3
+
+        # remove duplicates
+        L = list(set(L))
+        L_constrained = self.take_most_important(L, self.parameters['N_important'])
+        self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
+        return L, L_constrained
+
+
+
+    # Take the most important landmarks from the list
+    def take_most_important(self, landmarks: list[Landmark], n_max: int) -> list[Landmark]:
+
+        landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
+        return landmarks_sorted[:n_max]
+
+
+
+    # Correct the score of a list of landmarks by taking into account preference settings
+    def correct_score(self, L: list[Landmark], preference: Preference):
+
+        if len(L) == 0 :
+            return
+
+        if L[0].type != preference.type :
+            raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
+
+        for elem in L :
+            elem.attractiveness = int(elem.attractiveness*preference.score/500)     # arbitrary computation
+
+
+    # Function to count elements within a certain radius of a location
+    def count_elements_within_radius(self, point: Point, radius: int) -> int:
+        
+        center_coordinates = (point.x, point.y)
+        try:
+            landmarks = ox.features_from_point(
+                center_point = center_coordinates,
+                dist = radius,
+                tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
+            )
+            return len(landmarks)
+        except ox._errors.InsufficientResponseError:
+            return 0    
+
+
+
+
+    def fetch_landmarks(
+        self,
+        amenity_selectors: list[dict],
+        landmarktype: LandmarkType,
+        center_coordinates: tuple[float, float],
+        distance: int,
+        score_function: callable
+    ) -> list[Landmark]:
+        
+        landmarks = ox.features_from_point(
+            center_point = center_coordinates,
+            dist = distance,
+            tags = amenity_selectors
+        )
+        self.logger.info(f'Fetched {len(landmarks)} landmarks around {center_coordinates}.')
+
+        # cleanup the list
+        # remove rows where name is None
+        landmarks = landmarks[landmarks['name'].notna()]
+        # TODO: remove rows that are part of another building
+
+        ret_landmarks = []
+        for element, description in landmarks.iterrows():
+            osm_type = element[0]
+            osm_id = element[1]
+            location = description['geometry']
+            n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
+
+            if type(location) == Point:
+                location = location
+            elif type(location) == Polygon or type(location) == MultiPolygon:
+                location = location.centroid
+            elif type(location) == LineString:
+                location = location.interpolate(location.length/2)
+
+            score = score_function(location, n_tags)
+            landmark = Landmark(
+                name = description['name'],
+                type = landmarktype,
+                location = (location.x, location.y),
+                osm_type = osm_type,
+                osm_id = osm_id,
+                attractiveness = score,
+                must_do = False,
+                n_tags = n_tags
+            )
+            ret_landmarks.append(landmark)
+
+        return ret_landmarks
diff --git a/backend/src/optimizer.py b/backend/src/example_optimizer.py
similarity index 92%
rename from backend/src/optimizer.py
rename to backend/src/example_optimizer.py
index 3d2ac29..922af99 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/example_optimizer.py
@@ -1,13 +1,12 @@
 import numpy as np
-import json, os
+import yaml
 
 from typing import List, Tuple
 from scipy.optimize import linprog
 from math import radians, sin, cos, acos
-from shapely import Polygon
 
 from structs.landmarks import Landmark
-
+import constants
     
 # Function to print the result
 def print_res(L: List[Landmark], L_tot):
@@ -161,10 +160,11 @@ def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float
 # We want to maximize the sightseeing :  max(c) st. A*x < b   and   A_eq*x = b_eq
 def init_ub_dist(landmarks: List[Landmark], max_steps: int):
     
-    with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
-        parameters = json.loads(f.read())
-        detour = parameters['detour factor']
-        speed = parameters['average walking speed']
+    # Read the parameters from the file
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        detour = parameters['detour_factor']
+        speed = parameters['average_walking_speed']
     
     # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
     c = []
@@ -194,9 +194,9 @@ def respect_number(L:int, A_ub, b_ub):
         b_ub.append(1)
 
     # 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())
-        max_landmarks = parameters['max landmarks']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        max_landmarks = parameters['max_landmarks']
 
     A_ub = np.vstack((A_ub, ones*L))
     b_ub.append(max_landmarks+1)
@@ -300,13 +300,14 @@ def respect_order(N: int, A_eq, b_eq):
 
 
 # Computes the time to reach from each landmark to the next
-def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+def link_list(order: List[int], landmarks: 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']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        
+    detour_factor = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
     
     L =  []
     j = 0
@@ -329,10 +330,11 @@ def link_list(order: List[int], landmarks: List[Landmark])->List[Landmark] :
 def link_list_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']
+    with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+        parameters = yaml.safe_load(f)
+        
+    detour_factor = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
 
     L =  []
     j = 0
diff --git a/backend/src/example_refiner.py b/backend/src/example_refiner.py
new file mode 100644
index 0000000..4b1c306
--- /dev/null
+++ b/backend/src/example_refiner.py
@@ -0,0 +1,303 @@
+from collections import defaultdict
+from heapq import heappop, heappush
+from itertools import permutations
+import os
+import yaml
+
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
+from typing import List, Tuple
+from math import pi
+
+from structs.landmarks import Landmark
+from landmarks_manager import take_most_important
+from backend.src.example_optimizer import solve_optimization, link_list_simple, print_res, get_distance
+import constants
+
+
+# Create corridor from tour
+def create_corridor(landmarks: List[Landmark], width: float) :
+
+  corrected_width = (180*width)/(6371000*pi)
+  
+  path = create_linestring(landmarks)
+  obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
+
+  return obj
+
+
+# Create linestring from tour
+def create_linestring(landmarks: List[Landmark])->List[Point] :
+
+  points = []
+
+  for landmark in landmarks :
+    points.append(Point(landmark.location))
+
+  return LineString(points)
+
+
+# Check if some coordinates are in area. Used for the corridor 
+def is_in_area(area: Polygon, coordinates) -> bool :
+  point = Point(coordinates)
+  return point.within(area)
+
+
+# Function to determine if two landmarks are close to each other 
+def is_close_to(location1: Tuple[float], location2: Tuple[float]):
+    """Determine if two locations are close by rounding their coordinates to 3 decimals."""
+    absx = abs(location1[0] - location2[0])
+    absy = abs(location1[1] - location2[1])
+
+    return absx < 0.001 and absy < 0.001
+    #return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
+
+
+# Rearrange some landmarks in the order of visit to group visit 
+def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
+  
+  i = 1
+  while i < len(landmarks):
+    j = i+1
+    while j < len(landmarks):
+      if is_close_to(landmarks[i].location, landmarks[j].location) and landmarks[i].name not in ['start', 'finish'] and landmarks[j].name not in ['start', 'finish']:
+        # If they are not adjacent, move the j-th element to be adjacent to the i-th element
+        if j != i + 1:
+          landmarks.insert(i + 1, landmarks.pop(j))
+        break  # Move to the next i-th element after rearrangement
+      j += 1
+    i += 1
+  
+  return landmarks
+
+
+  # Step 1: Build the graph
+  graph = defaultdict(list)
+  for i in range(len(landmarks)):
+    for j in range(len(landmarks)):
+      if i != j:
+        distance = get_distance(landmarks[i].location, landmarks[j].location, detour, speed)[1]
+        graph[i].append((distance, j))
+
+  # Step 2: Dijkstra's algorithm to find the shortest path from start to finish
+  start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
+  finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
+
+  distances = {i: float('inf') for i in range(len(landmarks))}
+  previous_nodes = {i: None for i in range(len(landmarks))}
+  distances[start_idx] = 0
+  priority_queue = [(0, start_idx)]
+
+  while priority_queue:
+    current_distance, current_index = heappop(priority_queue)
+
+    if current_distance > distances[current_index]:
+      continue
+
+    for neighbor_distance, neighbor_index in graph[current_index]:
+      distance = current_distance + neighbor_distance
+
+      if distance < distances[neighbor_index]:
+        distances[neighbor_index] = distance
+        previous_nodes[neighbor_index] = current_index
+        heappush(priority_queue, (distance, neighbor_index))
+
+  # Step 3: Backtrack from finish to start to find the path
+  path = []
+  current_index = finish_idx
+  while current_index is not None:
+    path.append(landmarks[current_index])
+    current_index = previous_nodes[current_index]
+  path.reverse()
+
+  return path
+"""
+def total_path_distance(path: List[Landmark], detour, speed) -> float:
+  total_distance = 0
+  for i in range(len(path) - 1):
+    total_distance += get_distance(path[i].location, path[i + 1].location, detour, speed)[1]
+  return total_distance
+"""
+
+
+def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
+  # Read the parameters from the file
+  with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+
+    detour = parameters['detour_factor']
+    speed = parameters['average_walking_speed']
+    
+  # Step 1: Find 'start' and 'finish' landmarks
+  start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
+  finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
+  
+  start_landmark = landmarks[start_idx]
+  finish_landmark = landmarks[finish_idx]
+  
+
+  # Step 2: Create a list of unvisited landmarks excluding 'start' and 'finish'
+  unvisited_landmarks = [lm for i, lm in enumerate(landmarks) if i not in [start_idx, finish_idx]]
+  
+  # Step 3: Initialize the path with the 'start' landmark
+  path = [start_landmark]
+  coordinates = [landmarks[start_idx].location]
+
+  current_landmark = start_landmark
+  
+  # Step 4: Use nearest neighbor heuristic to visit all landmarks
+  while unvisited_landmarks:
+    nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location, detour, speed))
+    path.append(nearest_landmark)
+    coordinates.append(nearest_landmark.location)
+    current_landmark = nearest_landmark
+    unvisited_landmarks.remove(nearest_landmark)
+
+  # Step 5: Finally add the 'finish' landmark to the path
+  path.append(finish_landmark)
+  coordinates.append(landmarks[finish_idx].location)
+
+  path_poly = Polygon(coordinates)
+
+  return path, path_poly
+
+
+# Returns a list of minor landmarks around the planned path to enhance experience
+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.name not in visited_names:
+      second_order_landmarks.append(landmark)
+  
+  with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+  return take_most_important(second_order_landmarks, parameters, len(visited_landmarks))
+
+
+# Try fix the shortest path using shapely
+def fix_using_polygon(tour: List[Landmark])-> List[Landmark] :
+
+  coords = []
+  coords_dict = {}
+  for landmark in tour :
+    coords.append(landmark.location)
+    if landmark.name != 'finish' :
+      coords_dict[landmark.location] = landmark
+
+  tour_poly = Polygon(coords)
+  
+  better_tour_poly = tour_poly.buffer(0)
+  try :
+    xs, ys = better_tour_poly.exterior.xy
+
+    if len(xs) != len(tour) :
+      better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+      xs, ys = better_tour_poly.exterior.xy
+
+  except :
+    better_tour_poly = concave_hull(MultiPoint(coords))  # Create concave hull with "core" of tour leaving out start and finish
+    xs, ys = better_tour_poly.exterior.xy
+
+
+  # reverse the xs and ys
+  xs.reverse()
+  ys.reverse()
+
+  better_tour = []   # 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) tour
+  for i,x in enumerate(xs[:-1]) :
+    y = ys[i]
+    better_tour.append(coords_dict[tuple((x,y))])
+    name_index[coords_dict[tuple((x,y))].name] = i
+
+
+  # Scroll the list to have start in front again
+  start_index = name_index['start']
+  better_tour = better_tour[start_index:] + better_tour[:start_index]
+
+  # Append the finish back and correct the time to reach
+  better_tour.append(tour[-1])
+
+  # Rearrange only if polygon still not simple
+  if not better_tour_poly.is_simple :
+    better_tour = rearrange(better_tour)
+  
+  return better_tour
+
+
+# Second stage of the optimization. Use linear programming again to refine the path
+def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, detour: int, print_infos: bool) -> List[Landmark] :
+
+  # Read the parameters from the file
+  with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
+    parameters = yaml.safe_load(f)
+    max_landmarks = parameters['max_landmarks']
+  
+  if len(base_tour)-2 >= max_landmarks :
+    return base_tour
+  
+
+  # No need to refine if no detour is taken
+  # if detour == 0 :
+  if False :
+    new_tour = base_tour
+  
+  else :
+    minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+    if print_infos : print("Using " + 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 tour
+    new_tour = solve_optimization(full_set, max_time+detour, False, max_landmarks)
+    if new_tour is None :
+      new_tour = base_tour
+
+  # Link the new tour
+  new_tour, new_dist = link_list_simple(new_tour)
+
+  # If the tour contains only one landmark, return
+  if len(new_tour) < 4 :
+    return new_tour
+  
+  # Find shortest path using the nearest neighbor heuristic
+  better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
+
+  # Fix the tour using Polygons if the path looks weird
+  if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
+    better_tour = fix_using_polygon(better_tour)
+  
+  # Link the tour again
+  better_tour, better_dist = link_list_simple(better_tour)
+
+  # Choose the better tour depending on walked distance
+  if new_dist < better_dist :
+    final_tour = new_tour
+  else :
+    final_tour = better_tour
+
+  if print_infos :
+    print("\n\n\nRefined tour (result of second stage optimization): ")
+    print_res(final_tour)
+    total_score = 0
+    for elem in final_tour : 
+      total_score += elem.attractiveness
+
+    print("\nTotal score : " + str(total_score))
+
+
+  
+  return final_tour
+
+
diff --git a/backend/src/example_tester.py b/backend/src/example_tester.py
new file mode 100644
index 0000000..71d82e7
--- /dev/null
+++ b/backend/src/example_tester.py
@@ -0,0 +1,80 @@
+import pandas as pd
+
+from typing import List
+from landmarks_manager import LandmarkManager
+from fastapi.encoders import jsonable_encoder
+
+from backend.src.example_optimizer import solve_optimization
+# from refiner import refine_optimization
+from structs.landmarks import Landmark
+from structs.landmarktype import LandmarkType
+from structs.preferences import Preferences, Preference
+
+
+# Helper function to create a .txt file with results
+def write_data(L: List[Landmark], file_name: str): 
+
+    data = pd.DataFrame()
+    i = 0
+
+    for landmark in L :
+        data[i] = jsonable_encoder(landmark)
+        i += 1
+
+    data.to_json(file_name, indent = 2, force_ascii=False)
+
+def main(coordinates: tuple[float, float]) -> List[Landmark]:
+
+    manager = LandmarkManager()
+
+    preferences = Preferences(
+        sightseeing=Preference(
+            name='sightseeing', 
+            type=LandmarkType(landmark_type='sightseeing'),
+            score = 5
+            ),
+        nature=Preference(
+            name='nature', 
+            type=LandmarkType(landmark_type='nature'),
+            score = 5
+            ),
+        shopping=Preference(
+            name='shopping', 
+            type=LandmarkType(landmark_type='shopping'),
+            score = 5
+            )
+        )
+
+
+    # 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 = manager.get_landmark_lists(preferences=preferences, center_coordinates=start.location)
+    print([l.name for l in landmarks_short])
+
+    #write_data(landmarks, "landmarks.txt")
+
+    # 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 = 3    # hours
+    detour = 30             # minutes
+
+    # First stage optimization
+    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 base_tour
+
+
+
+if __name__ == '__main__':
+    start = (48.847132, 2.312359) # Café Chez César
+    # start = (47.377859, 8.540585) # Zurich HB
+    main(start)
diff --git a/backend/src/main.py b/backend/src/main.py
index 89b49fa..8a76288 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -1,51 +1,45 @@
-from optimizer import solve_optimization
-from refiner import refine_optimization
-from landmarks_manager import generate_landmarks
+from backend.src.example_optimizer import solve_optimization
+# from refiner import refine_optimization
+from landmarks_manager import LandmarkManager
 from structs.landmarks import Landmark
 from structs.landmarktype import LandmarkType
-from structs.preferences import Preferences, Preference
+from structs.preferences import Preferences
 from fastapi import FastAPI, Query, Body
-from typing import List
 
 app = FastAPI()
-
+manager = LandmarkManager()
 
 # TODO: needs a global variable to store the landmarks accross function calls
 # linked_tour = []
 
 
-# Assuming frontend is calling like this : 
-#"http://127.0.0.1:8000/process?param1={param1}&param2={param2}"
-@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
-def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
-    
+@app.post("/route/new")
+def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] = None) -> str:
+    '''
+    Main function to call the optimizer.
+    :param preferences: the preferences specified by the user as the post body
+    :param start: the coordinates of the starting point as a tuple of floats (as url query parameters)
+    :param end: the coordinates of the finishing point as a tuple of floats (as url query parameters)
+    :return: the uuid of the first landmark in the optimized route
+    '''
     if preferences is None :
         raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
-    if bool(start_lat) ^ bool(start_lon) :
-        raise ValueError("Please provide both latitude and longitude for the starting point")
-    if bool(finish_lat) ^ bool(finish_lon) :
-        raise ValueError("Please provide both latitude and longitude for the finish point")
+    if start is None:
+        raise ValueError("Please provide the starting coordinates as a tuple of floats.")
+    if end is None:
+        end = start
 
-    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)
+    start_landmark = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+    end_landmark = Landmark(name='end', type=LandmarkType(landmark_type='end'), location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
     
-    if bool(finish_lat) and bool(finish_lon) :
-        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)
-    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)
-    
-    
-    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)
-
+    landmarks, landmarks_short = LandmarkManager.get_landmark_lists(preferences=preferences, coordinates=start.location)
+    print([l.name for l in landmarks_short])
     # insert start and finish to the landmarks list
-    landmarks_short.insert(0, start)
-    landmarks_short.append(finish)
+    landmarks_short.insert(0, start_landmark)
+    landmarks_short.append(end_landmark)
     
-
-    # TODO use these parameters in another way
+    # TODO infer these parameters from the preferences
     max_walking_time = 4    # hours
     detour = 30             # minutes
 
@@ -53,32 +47,11 @@ def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(..
     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)
+    # refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
 
-
-    # TODO: should look something like this 
-    # # set time to reach and transform into fully functional linked list
-    # linked_tour += link(refined_tour)
-    # return {
-    #     'city_name': 'Paris',
-    #     'n_stops': len(linked_tour),
-    #     'first_landmark_uuid': linked_tour[0].uuid,
-    # }
-    
-    return refined_tour
-
-
-
-
-# input city, country in the form of 'Paris, France'
-@app.post("/test2/{city_country}")
-def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
-
-    landmarks = generate_landmarks(city_country, preferences)
-
-    max_steps = 9000000
-
-    visiting_order = solve_optimization(landmarks, max_steps, True)
+    # linked_tour = ...
+    # return linked_tour[0].uuid
+    return base_tour[0].uuid
 
 
 
@@ -86,5 +59,3 @@ def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landm
 def get_landmark(landmark_uuid: str) -> Landmark:
     #cherche dans linked_tour et retourne le landmark correspondant
     pass
-
-
diff --git a/backend/src/parameters/amenity_selectors.yaml b/backend/src/parameters/amenity_selectors.yaml
new file mode 100644
index 0000000..4f6b223
--- /dev/null
+++ b/backend/src/parameters/amenity_selectors.yaml
@@ -0,0 +1,32 @@
+nature:
+  leisure: park
+  geological: ''
+  natural:
+    - geyser
+    - hot_spring
+    - arch
+    - volcano
+    - stone
+  tourism:
+    - alpine_hut
+    - viewpoint
+    - zoo
+  waterway: waterfall
+
+shopping:
+  shop:
+    - department_store
+    - mall
+
+sightseeing:
+  tourism:
+    - museum
+    - attraction
+    - gallery
+  historic: ''
+  amenity:
+    - planetarium
+    - place_of_worship
+    - fountain
+  water:
+    - reflecting_pool
diff --git a/backend/src/parameters/landmark_parameters.yaml b/backend/src/parameters/landmark_parameters.yaml
new file mode 100644
index 0000000..b85c89a
--- /dev/null
+++ b/backend/src/parameters/landmark_parameters.yaml
@@ -0,0 +1,6 @@
+city_bbox_side: 5000 #m
+radius_close_to: 30
+church_coeff: 0.6
+park_coeff: 1.5
+tag_coeff: 100
+N_important: 40
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
new file mode 100644
index 0000000..515d028
--- /dev/null
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -0,0 +1,3 @@
+detour_factor: 1.4
+average_walking_speed: 4.8
+max_landmarks: 40
diff --git a/backend/src/structs/landmarks.py b/backend/src/structs/landmarks.py
index 2448236..71111a1 100644
--- a/backend/src/structs/landmarks.py
+++ b/backend/src/structs/landmarks.py
@@ -1,6 +1,5 @@
 from typing import Optional
 from pydantic import BaseModel, Field
-
 from .landmarktype import LandmarkType
 
 from uuid import uuid4
@@ -29,3 +28,6 @@ class Landmark(BaseModel) :
     
     time_to_reach_next : Optional[int] = 0                      # TODO fix this in existing code
     next_uuid : Optional[str] = None                            # TODO implement this ASAP
+
+    def __hash__(self) -> int:
+        return self.uuid.int
\ No newline at end of file
diff --git a/backend/src/tester.py b/backend/src/tester.py
index acc84d6..cea6527 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -1,7 +1,7 @@
 import pandas as pd
 
 from typing import List
-from landmarks_manager import generate_landmarks
+from landmarks_manager import LandmarkManager
 from fastapi.encoders import jsonable_encoder
 
 from optimizer_v4 import solve_optimization
@@ -26,6 +26,7 @@ def write_data(L: List[Landmark], file_name: str):
 
 def test4(coordinates: tuple[float, float]) -> List[Landmark]:
 
+    manager = LandmarkManager()
 
     preferences = Preferences(
                     sightseeing=Preference(
@@ -77,4 +78,4 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
 #test4(tuple((47.377859, 8.540585)))         # Zurich HB
 #test4(tuple((45.7576485, 4.8330241)))      # Lyon Bellecour
 test4(tuple((48.5848435, 7.7332974)))      # Strasbourg Gare
-#test4(tuple((48.2067858, 16.3692340)))      # Vienne
\ No newline at end of file
+#test4(tuple((48.2067858, 16.3692340)))      # Vienne
diff --git a/deployment/deployment.yaml b/deployment/deployment.yaml
new file mode 100644
index 0000000..aabc6fa
--- /dev/null
+++ b/deployment/deployment.yaml
@@ -0,0 +1,40 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: nav-backend
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: nav-backend
+  template:
+    metadata:
+      labels:
+        app: nav-backend
+    spec:
+      containers:
+      - name: worker
+        image: backend-image
+        ports:
+        - containerPort: 8000
+        env:
+        - name: NUM_WORKERS
+          value: "3"
+        - name: OSM_CACHE_DIR
+          value: "/osm-cache"
+        volumeMounts:
+        - name: osm-cache
+          mountPath: /osm-cache
+        resources:
+          requests:
+            cpu: 100m
+            memory: 100Mi
+          limits:
+            cpu: 4
+            memory: 10Gi
+      volumes:
+      - name: osm-cache
+        persistentVolumeClaim:
+          claimName: osm-cache
+
+    
\ No newline at end of file
diff --git a/deployment/ingress.yaml b/deployment/ingress.yaml
new file mode 100644
index 0000000..8fa3b41
--- /dev/null
+++ b/deployment/ingress.yaml
@@ -0,0 +1,15 @@
+kind: IngressRoute
+apiVersion: traefik.io/v1alpha1
+metadata:
+  name: nav-ingress
+spec:
+  entryPoints:
+    - websecure
+  routes:
+    - match: Host(`nav.kluster.moll.re`)
+      kind: Rule
+      services:
+        - name: nav-service
+          port: 8000
+  tls:
+    certResolver: default-tls
diff --git a/deployment/kustomization.yaml b/deployment/kustomization.yaml
index e69de29..bbb5d0c 100644
--- a/deployment/kustomization.yaml
+++ b/deployment/kustomization.yaml
@@ -0,0 +1,16 @@
+apiVersion: kustomize.config.k8s.io/v1beta1
+kind: Kustomization
+
+
+namespace: nav
+resources:
+  - namespace.yaml
+  - pvc.yaml
+  - deployment.yaml
+  - service.yaml
+  - ingress.yaml
+
+images:
+  - name: backend-image
+    newName: git.kluster.moll.re/remoll/fast_network_navigation/backend
+    newTag: latest
\ No newline at end of file
diff --git a/deployment/namespace.yaml b/deployment/namespace.yaml
new file mode 100644
index 0000000..0a074bd
--- /dev/null
+++ b/deployment/namespace.yaml
@@ -0,0 +1,4 @@
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: placeholder
diff --git a/deployment/pvc.yaml b/deployment/pvc.yaml
new file mode 100644
index 0000000..a7e747f
--- /dev/null
+++ b/deployment/pvc.yaml
@@ -0,0 +1,11 @@
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: osm-cache
+spec:
+  storageClassName: "nfs-client"
+  accessModes:
+    - ReadWriteOnce
+  resources:
+    requests:
+      storage: "5Gi"
diff --git a/deployment/service.yaml b/deployment/service.yaml
new file mode 100644
index 0000000..c4e5de3
--- /dev/null
+++ b/deployment/service.yaml
@@ -0,0 +1,11 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: nav-service
+spec:
+  selector:
+    app: nav-backend
+  ports:
+    - protocol: TCP
+      port: 8000
+      targetPort: 8000