GitHub - crowd-dataset/crowd: This repository contains code that extracts YouTube videos based on a mapping.csv file and performs object detection using YOLOv11. The primary objective of this work is to evaluate pedestrian behaviour in a cross-country or cross-cultural context using freely available YouTube videos.

City Road Observations With Dashcams (CROWD)

This repository contains code that extracts YouTube videos based on a mapping.csv file and performs object detection using YOLOv11. The primary objective of this work is to evaluate pedestrian behaviour in a cross-country or cross-cultural context using freely available YouTube videos.

This study presents a comprehensive cross-cultural evaluation of pedestrian behaviour during road crossings, examining variations between developed and developing states worldwide. As urban landscapes evolve and autonomous vehicles (AVs) become integral to future transportation, understanding pedestrian behaviour becomes paramount for ensuring safe interactions between humans and AVs. Through an extensive review of global pedestrian studies, we analyse key factors influencing crossing behaviour, such as cultural norms, socioeconomic factors, infrastructure development, and regulatory frameworks. Our findings reveal distinct patterns in pedestrian conduct across different regions. Developed states generally exhibit more structured and rule-oriented crossing behaviours, influenced by established traffic regulations and advanced infrastructure. In contrast, developing states often witness a higher degree of informal and adaptive behaviour due to limited infrastructure and diverse cultural influences. These insights underscore the necessity for AVs to adapt to diverse pedestrian behaviour on a global scale, emphasising the importance of incorporating cultural nuances into AV programming and decision-making algorithms. As the integration of AVs into urban environments accelerates, this research contributes valuable insights for enhancing the safety and efficiency of autonomous transportation systems. By recognising and accommodating diverse pedestrian behaviours, AVs can navigate complex and dynamic urban settings, ensuring a harmonious coexistence with human road users across the globe.

The dataset is available on kaggle. The dataset shall soon be made available on a permanent FAIR storage.

Dataset overview

Footage: 35,082.1 hours (1,461.8 days)
Videos: 78,702 unique videos split into 101,405 segments
Localities (cities): 10,143
Countries and territories: 238
Continents: 6
YOLO detection files: 86,151 CSV files (one per processed segment)
Detected objects (unique tracked objects, confidence ≥ 0.7): 31,055,197 persons, 81,904,094 cars, 2,434,824 bicycles, 10,321,545 motorcycles, 3,371,598 buses, 13,178,837 trucks
Last updated: 2026-10-17 07:39 UTC

All 238 countries and territories

#	Country	Cities	Videos	Footage (h)	Persons	Cars	Bicycles	Motorcycles	Buses	Trucks
1	🇺🇸 United States	2,299	20,167	10,280.0	4,272,921	23,691,175	271,124	142,831	463,957	3,767,718
2	🇬🇧 United Kingdom	358	3,156	1,670.3	2,669,605	3,286,323	258,876	153,027	259,935	557,811
3	🇦🇺 Australia	206	3,897	1,654.9	576,405	3,844,979	39,023	39,877	87,915	632,309
4	🇷🇺 Russia	210	3,268	1,486.7	739,160	3,488,118	15,963	6,876	153,569	448,842
5	🇵🇭 Philippines	398	3,570	1,308.3	2,280,616	2,263,741	71,888	1,423,298	303,676	910,602
6	🇨🇦 Canada	229	3,537	1,231.8	746,694	2,850,553	68,576	19,519	55,088	371,391
7	🇻🇳 Vietnam	77	3,056	1,188.8	2,841,773	1,918,728	89,613	2,791,899	124,640	295,741
8	🇮🇩 Indonesia	176	3,095	997.2	1,868,154	1,870,317	24,313	1,816,459	108,349	471,997
9	🇰🇷 South Korea	84	850	819.8	350,283	1,627,830	20,330	52,974	103,482	176,255
10	🇫🇷 France	359	1,891	745.2	811,503	1,741,161	100,263	122,825	46,589	193,698
11	🇨🇳 China	148	1,227	692.2	477,193	1,757,527	50,356	190,159	101,437	205,625
12	🇩🇪 Germany	237	1,375	652.3	529,924	1,684,445	183,442	34,832	59,311	204,147
13	🇯🇵 Japan	219	1,710	602.9	344,686	1,055,513	49,372	21,845	77,232	280,424
14	🇧🇷 Brazil	129	1,415	590.4	519,148	1,513,850	33,536	149,969	54,774	192,901
15	🇳🇱 Netherlands	231	1,442	547.2	487,941	991,002	339,799	54,752	31,801	113,489
16	🇮🇳 India	294	1,495	478.6	1,189,100	1,005,688	63,802	686,967	124,472	335,999
17	🇺🇦 Ukraine	94	1,083	443.1	264,908	1,196,004	7,847	4,225	59,354	177,315
18	🇹🇭 Thailand	49	790	385.1	448,112	969,956	8,581	341,371	48,744	219,493
19	🇲🇲 Myanmar	19	536	371.5	377,596	1,482,283	43,641	41,165	98,912	251,716
20	🇦🇪 United Arab Emirates	10	864	360.0	101,661	781,706	3,974	15,699	42,363	109,916
21	🇵🇱 Poland	280	864	343.5	211,781	862,033	20,428	7,005	32,398	82,793
22	🇮🇷 Iran	108	980	317.3	157,668	1,165,662	3,260	29,859	13,082	104,765
23	🇲🇾 Malaysia	65	599	282.3	139,060	821,081	1,749	83,469	17,702	77,039
24	🇹🇷 Türkiye	87	565	268.6	268,694	842,597	4,209	36,123	55,842	130,713
25	🇸🇪 Sweden	167	961	266.5	126,789	431,124	21,530	2,783	18,518	50,698
26	🇮🇹 Italy	168	671	262.3	265,315	817,830	23,685	100,055	17,599	68,722
27	🇦🇷 Argentina	64	410	255.9	339,200	819,300	33,589	41,484	29,416	97,763
28	🇩🇰 Denmark	22	562	255.2	369,226	719,246	194,237	8,296	20,180	60,390
29	🇷🇴 Romania	91	633	242.6	192,777	1,091,080	6,707	5,071	27,553	98,506
30	🇫🇮 Finland	41	382	238.9	126,585	442,436	14,166	2,367	21,442	54,634
31	🇪🇸 Spain	251	652	228.1	265,869	579,582	15,145	61,646	22,167	63,447
32	🇰🇭 Cambodia	7	427	222.6	581,388	540,096	13,515	597,642	38,795	179,386
33	🇨🇿 Czechia	82	374	214.6	211,202	529,498	5,602	5,071	23,140	55,113
34	🇦🇹 Austria	45	849	202.5	126,204	504,171	17,752	14,288	23,036	82,722
35	🇺🇬 Uganda	19	516	193.1	210,110	97,690	5,333	116,814	9,743	31,876
36	🇭🇺 Hungary	34	469	171.8	302,570	1,021,996	14,884	8,336	50,838	124,246
37	🇵🇰 Pakistan	38	420	165.7	441,809	466,633	16,051	232,921	27,715	116,824
38	🇧🇬 Bulgaria	89	643	162.6	83,794	508,664	1,792	1,203	18,043	48,284
39	🇳🇿 New Zealand	160	596	159.6	76,612	365,330	4,575	3,368	13,541	53,467
40	🇸🇬 Singapore	1	399	155.2	81,730	342,138	5,203	20,569	33,441	60,162
41	🇻🇪 Venezuela	15	599	148.2	92,844	331,841	4,908	35,984	7,127	68,507
42	🇲🇽 Mexico	72	306	144.2	125,401	377,330	6,052	21,125	13,579	60,564
43	🇮🇪 Ireland	55	491	142.4	108,118	310,653	10,483	2,209	13,984	41,030
44	🇧🇾 Belarus	83	281	139.3	44,011	171,135	1,579	398	9,951	24,933
45	🇳🇴 Norway	68	421	139.3	85,184	283,525	7,453	1,997	13,853	38,237
46	🇿🇦 South Africa	55	448	134.0	90,590	325,224	1,701	4,647	12,966	60,022
47	🇸🇾 Syria	13	316	124.7	216,348	507,868	6,463	16,996	17,203	83,736
48	🇪🇬 Egypt	20	270	107.5	71,736	235,831	634	8,241	11,975	31,172
49	🇧🇩 Bangladesh	29	298	107.3	390,478	113,971	74,318	72,325	40,702	85,738
50	🇵🇹 Portugal	73	226	104.4	87,578	287,432	2,143	7,460	7,731	31,681
51	🇨🇭 Switzerland	94	299	98.7	103,933	188,046	21,150	13,360	9,502	24,085
52	🇬🇲 Gambia	29	361	95.4	226,529	285,640	14,671	20,858	8,740	64,562
53	🇳🇬 Nigeria	35	230	92.0	216,399	176,420	1,810	32,108	13,929	45,474
54	🇸🇱 Sierra Leone	47	184	79.3	170,227	48,806	1,877	49,358	4,591	23,204
55	🇯🇴 Jordan	13	250	78.3	46,155	311,691	181	983	7,319	39,417
56	🇭🇷 Croatia	89	281	71.9	52,249	228,297	4,591	4,309	5,509	19,685
57	🇨🇱 Chile	43	160	71.9	22,163	61,334	775	1,113	2,706	13,174
58	🇲🇳 Mongolia	4	130	71.4	88,118	304,976	1,881	1,654	9,124	25,710
59	🇬🇷 Greece	40	196	66.9	73,714	265,121	2,806	25,708	6,316	25,638
60	🇨🇾 Cyprus	17	252	66.5	26,967	202,441	1,081	5,007	4,165	28,218
61	🇪🇪 Estonia	6	302	65.3	33,674	167,480	1,206	418	8,464	16,981
62	🇭🇰 Hong Kong	11	163	63.1	100,646	99,158	1,443	2,991	19,375	19,435
63	🇧🇪 Belgium	61	190	62.9	65,468	167,638	14,859	3,856	7,204	23,728
64	🇧🇦 Bosnia and Herzegovina	60	291	62.0	50,620	157,116	865	576	4,488	13,869
65	🇨🇴 Colombia	36	128	60.1	86,062	89,103	5,878	39,551	6,101	17,256
66	🇺🇿 Uzbekistan	12	178	53.7	26,976	209,921	434	194	4,187	17,563
67	🇵🇪 Peru	29	94	53.1	84,003	147,775	5,452	12,966	9,566	30,312
68	🇱🇰 Sri Lanka	15	127	47.6	70,859	99,910	1,065	25,398	17,602	40,207
69	🇲🇦 Morocco	35	135	47.6	85,390	175,883	3,788	17,447	5,038	21,670
70	🇷🇸 Serbia	34	143	47.1	42,935	171,668	1,152	1,224	5,929	14,641
71	🇹🇼 Taiwan	13	100	45.9	49,007	115,619	2,523	44,935	4,493	12,517
72	🇦🇿 Azerbaijan	8	112	45.5	44,791	206,195	690	1,127	6,072	21,374
73	🇶🇦 Qatar	5	98	44.3	10,603	109,498	434	1,281	5,308	16,307
74	🇪🇹 Ethiopia	8	77	44.2	104,306	104,629	540	1,978	9,657	31,967
75	🇸🇦 Saudi Arabia	11	110	43.6	5,107	142,192	118	248	2,833	17,976
76	🇬🇪 Georgia	11	117	42.4	31,146	170,922	246	1,164	6,716	21,141
77	🇳🇵 Nepal	4	120	38.2	212,044	71,559	6,035	126,705	10,857	19,965
78	🇱🇻 Latvia	20	123	38.0	24,270	91,567	1,611	716	3,964	11,579
79	🇱🇧 Lebanon	60	154	35.2	22,732	146,170	182	7,506	2,695	17,820
80	🇨🇺 Cuba	25	168	34.6	24,271	5,125	2,110	3,773	719	2,701
81	🇱🇹 Lithuania	10	84	33.5	31,866	98,334	2,608	715	3,317	7,486
82	🇵🇷 Puerto Rico	60	159	33.1	3,778	30,191	82	125	280	4,859
83	🇲🇼 Malawi	51	113	32.4	67,538	30,370	9,781	5,080	1,788	8,925
84	🇰🇿 Kazakhstan	26	113	30.9	21,562	115,902	260	201	4,020	12,666
85	🇰🇪 Kenya	55	112	27.0	62,585	46,029	1,155	12,133	4,453	12,793
86	🇺🇾 Uruguay	23	90	25.3	31,932	90,573	2,748	5,257	1,626	12,618
87	🇲🇰 North Macedonia	21	72	24.4	25,462	86,433	1,764	1,066	2,506	7,806
88	🇲🇩 Moldova	14	107	24.2	25,864	102,739	302	217	5,075	12,436
89	🇦🇲 Armenia	10	77	23.9	6,209	40,203	35	64	1,393	5,562
90	🇱🇦 Laos	10	60	22.9	23,497	49,112	526	22,212	1,271	19,464
91	🇪🇨 Ecuador	15	55	22.8	27,801	68,164	685	4,730	3,522	12,731
92	🇧🇴 Bolivia	20	55	21.5	26,213	42,478	628	4,934	2,728	13,037
93	🇲🇨 Monaco	1	45	20.3	35,821	30,718	962	12,489	1,542	4,295
94	🇮🇱 Israel	18	67	19.5	14,470	65,564	2,335	3,088	2,706	5,634
95	🇽🇰 Kosovo	31	93	15.4	12,872	51,774	508	510	1,264	7,371
96	🇬🇹 Guatemala	9	23	15.3	13,328	32,754	527	7,132	1,411	9,393
97	🇦🇱 Albania	23	62	14.8	17,949	45,917	1,604	1,215	1,389	5,350
98	🇩🇿 Algeria	8	35	14.5	24,662	61,115	133	1,301	1,363	6,805
99	🇨🇬 Congo (Congo-Brazzaville)	5	38	13.9	26,483	22,471	229	3,047	1,058	5,202
100	🇱🇾 Libya	3	39	13.2	4,891	54,787	79	115	299	6,981
101	🇮🇸 Iceland	14	39	12.8	7,211	28,191	474	68	952	4,491
102	🇹🇲 Turkmenistan	1	85	12.0	3,731	22,595	47	6	812	2,774
103	🇬🇭 Ghana	10	39	11.4	22,184	27,168	670	4,453	2,213	8,526
104	🇮🇶 Iraq	10	40	11.4	4,150	33,761	120	477	523	4,834
105	🇴🇲 Oman	10	32	10.9	2,967	35,947	156	60	742	3,513
106	🇸🇻 El Salvador	6	13	10.8	7,666	13,960	246	2,318	687	3,575
107	🇭🇳 Honduras	8	23	10.3	7,734	20,112	380	3,007	1,061	4,816
108	🇲🇪 Montenegro	22	65	10.2	8,812	38,620	172	751	963	4,063
109	🇹🇿 Tanzania	14	51	9.5	12,790	7,858	657	3,490	1,331	3,023
110	🇵🇦 Panama	7	22	9.4	1,682	10,262	55	117	183	2,349
111	🇬🇾 Guyana	6	20	9.0	8,363	22,593	662	826	1,087	6,297
112	🇧🇭 Bahrain	11	42	8.8	2,007	24,546	182	228	746	3,611
113	🇸🇮 Slovenia	25	47	8.5	9,937	25,347	1,570	356	897	2,050
114	🇰🇬 Kyrgyzstan	7	40	8.4	6,645	30,471	161	69	1,493	4,913
115	🇯🇲 Jamaica	10	30	8.3	8,835	19,261	220	346	1,458	3,469
116	🇵🇸 Palestine	10	31	8.3	5,348	26,384	95	244	807	3,880
117	🇬🇮 Gibraltar	1	21	7.9	7,384	14,720	384	3,883	630	2,258
118	🇲🇹 Malta	31	56	7.6	3,461	23,323	49	533	757	3,788
119	🇨🇮 Côte d'Ivoire	2	19	7.6	13,309	29,310	176	1,474	924	4,167
120	🇬🇳 Guinea	6	28	7.5	18,434	15,992	146	6,364	451	3,611
121	🇸🇰 Slovakia	13	26	7.4	5,559	22,126	292	193	879	2,484
122	🇱🇺 Luxembourg	12	24	7.0	8,121	15,294	468	316	914	1,638
123	🇰🇼 Kuwait	2	21	6.9	2,664	23,894	27	74	1,067	2,654
124	🇱🇷 Liberia	11	43	6.8	26,168	16,747	120	9,633	970	5,277
125	🇸🇷 Suriname	3	19	6.7	2,262	16,386	130	506	521	2,645
126	🇿🇼 Zimbabwe	4	26	6.3	16,477	25,223	110	148	1,292	6,010
127	🇦🇬 Antigua and Barbuda	9	21	6.1	4,292	10,555	43	25	270	1,908
128	🇵🇾 Paraguay	8	18	5.9	6,004	19,678	299	3,007	470	4,024
129	🇻🇨 Saint Vincent and the Grenadines	11	25	5.7	3,913	7,296	23	30	546	1,835
130	🇧🇯 Benin	10	16	5.6	19,911	10,339	325	12,657	291	2,243
131	🇨🇫 Central African Republic	3	18	5.4	33,260	7,691	373	14,433	187	2,431
132	🇦🇫 Afghanistan	8	26	5.3	10,698	15,305	996	694	858	3,172
133	🇱🇮 Liechtenstein	16	59	5.2	1,156	8,408	176	210	434	1,474
134	🇹🇳 Tunisia	5	15	5.1	9,163	23,032	173	672	506	3,303
135	🇹🇹 Trinidad and Tobago	3	5	4.6	1,657	8,274	3	11	228	1,770
136	🇪🇷 Eritrea	11	27	4.6	14,201	7,216	3,305	138	286	1,719
137	🇳🇮 Nicaragua	8	17	4.6	5,973	9,241	589	2,642	460	3,022
138	🇦🇩 Andorra	8	45	4.6	4,008	12,742	84	1,183	644	2,748
139	🇬🇺 Guam	10	41	4.5	510	14,313	14	33	154	3,039
140	🇲🇴 Macau	2	15	4.5	3,030	5,239	47	654	889	951
141	🇰🇵 North Korea	1	19	4.4	14,070	3,924	1,823	102	932	771
142	🇨🇷 Costa Rica	10	19	4.3	4,316	10,203	253	765	448	2,157
143	🇲🇬 Madagascar	3	11	4.3	19,088	6,412	2,000	2,699	1,016	3,360
144	🇵🇫 French Polynesia	11	17	4.3	2,002	10,477	210	975	196	2,538
145	🇸🇳 Senegal	6	16	4.3	11,200	12,515	159	2,982	463	2,408
146	🇰🇾 Cayman Islands	5	17	4.2	759	10,150	84	36	319	2,019
147	🇬🇵 Guadeloupe	11	30	4.1	1,311	12,588	38	117	259	1,792
148	🇦🇴 Angola	2	17	4.1	11,170	13,849	70	951	1,041	3,558
149	🇿🇲 Zambia	4	13	3.7	7,055	6,968	408	96	527	2,244
150	🇷🇼 Rwanda	7	14	3.5	10,556	4,987	638	4,322	326	1,543
151	🇬🇱 Greenland	3	11	3.5	1,349	2,888	39	2	162	772
152	🇸🇲 San Marino	4	13	3.5	1,032	8,866	11	245	155	890
153	🇨🇩 Congo (Democratic Republic)	3	16	3.3	9,462	7,610	66	2,604	289	1,534
154	🇨🇲 Cameroon	9	16	3.2	9,836	11,413	42	2,935	200	1,714
155	🇫🇯 Fiji	3	9	3.2	2,017	6,661	6	23	428	1,497
156	🇬🇬 Guernsey	8	20	3.1	1,510	6,086	113	166	186	974
157	🇱🇨 Saint Lucia	7	20	3.1	2,447	6,450	16	42	532	1,784
158	🇬🇦 Gabon	6	17	3.0	4,690	6,478	33	81	182	1,924
159	🇭🇹 Haiti	1	6	2.9	8,084	4,643	45	1,580	425	2,678
160	🇩🇴 Dominican Republic	7	9	2.9	3,782	6,949	172	1,872	298	1,352
161	🇸🇴 Somalia	4	15	2.8	7,097	4,498	21	170	864	2,487
162	🇲🇺 Mauritius	7	13	2.8	3,494	7,076	76	1,004	455	1,505
163	🇧🇲 Bermuda	4	5	2.7	658	3,199	24	737	189	700
164	🇳🇨 New Caledonia	1	5	2.6	675	5,424	49	42	103	1,377
165	🇯🇪 Jersey	10	18	2.5	889	4,543	101	67	148	956
166	🇾🇪 Yemen	3	12	2.5	7,388	9,522	96	2,496	347	2,781
167	🇰🇲 Comoros	3	5	2.5	3,578	4,854	55	1,564	87	770
168	🇧🇼 Botswana	4	9	2.3	1,589	4,779	5	10	174	1,077
169	🇻🇬 British Virgin Islands	7	17	2.2	680	5,237	3	49	101	1,193
170	🇰🇳 Saint Kitts and Nevis	3	9	2.1	1,762	3,245	26	25	174	743
171	🇼🇸 Samoa	1	7	2.0	392	2,715	0	0	224	588
172	🇷🇪 Réunion	4	6	2.0	1,205	4,903	31	89	107	632
173	🇹🇯 Tajikistan	2	9	1.9	2,537	8,973	30	72	355	855
174	🇸🇸 South Sudan	1	5	1.9	6,672	3,931	76	2,770	166	1,453
175	🇳🇦 Namibia	4	10	1.8	1,457	3,452	18	11	95	876
176	🇫🇴 Faroe Islands	21	36	1.8	287	2,560	12	4	124	456
177	🇧🇧 Barbados	3	4	1.8	1,506	2,706	41	7	177	500
178	🇬🇫 French Guiana	1	3	1.7	831	5,927	59	387	71	1,292
179	🇮🇲 Isle of Man	8	19	1.6	1,351	6,939	24	645	209	1,150
180	🇧🇹 Bhutan	2	6	1.6	1,646	3,041	14	40	91	671
181	🇩🇯 Djibouti	3	5	1.5	746	1,537	5	116	243	1,306
182	🇱🇸 Lesotho	10	15	1.5	1,730	2,222	5	11	95	619
183	🇬🇩 Grenada	4	8	1.4	577	1,762	8	43	97	611
184	🇲🇫 Saint Martin	6	9	1.3	461	4,156	13	130	100	1,155
185	🇧🇮 Burundi	6	9	1.3	5,541	2,270	433	313	173	492
186	🇲🇿 Mozambique	5	8	1.3	1,953	1,850	34	149	119	462
187	🇲🇷 Mauritania	1	3	1.3	1,854	3,898	74	116	38	937
188	🇪🇭 Western Sahara	3	4	1.3	726	2,450	34	100	56	564
189	🇹🇬 Togo	6	11	1.2	2,940	1,458	59	1,696	55	370
190	🇵🇬 Papua New Guinea	2	6	1.2	1,095	1,656	0	0	216	443
191	🇲🇸 Montserrat	1	3	1.2	4,465	1,127	267	4,014	383	835
192	🇦🇸 American Samoa	9	13	1.1	266	2,385	5	1	127	1,119
193	🇧🇸 Bahamas	2	5	1.1	257	1,383	21	22	55	248
194	🇹🇨 Turks and Caicos Islands	2	4	1.0	102	1,469	0	2	23	212
195	🇻🇺 Vanuatu	1	5	1.0	1,038	1,438	14	3	156	601
196	🇹🇩 Chad	1	4	1.0	3,975	2,890	74	1,962	52	748
197	🇻🇮 United States Virgin Islands	3	5	1.0	399	2,765	0	7	72	779
198	🇲🇱 Mali	1	2	0.9	4,487	2,330	29	3,053	173	696
199	🇳🇷 Nauru	11	18	0.9	329	1,067	4	341	49	571
200	🇧🇫 Burkina Faso	1	5	0.8	2,932	2,972	321	2,752	55	460
201	🇸🇽 Sint Maarten	4	5	0.8	570	2,892	7	42	67	613
202	🇦🇼 Aruba	2	3	0.8	312	2,342	6	9	44	272
203	🇨🇼 Curaçao	1	3	0.8	32	1,635	0	0	15	195
204	🇦🇽 Åland Islands	1	1	0.8	110	617	26	4	11	98
205	🇲🇭 Marshall Islands	3	3	0.8	114	333	0	0	13	95
206	🇧🇳 Brunei	3	4	0.8	74	1,391	1	4	27	145
207	🏳️ Svalbard and Jan Mayen	1	3	0.7	60	497	5	5	39	169
208	🇧🇿 Belize	3	5	0.7	404	1,571	61	91	49	519
209	🇧🇶 Bonaire, Sint Eustatius and Saba	1	1	0.7	236	1,737	21	82	27	695
210	🇬🇶 Equatorial Guinea	1	5	0.7	805	2,063	12	9	51	373
211	🇫🇲 Micronesia	3	3	0.6	164	661	0	0	6	148
212	🇳🇪 Niger	1	2	0.6	2,224	1,023	66	266	7	126
213	🇵🇼 Palau	4	5	0.6	23	521	3	3	9	126
214	🇦🇮 Anguilla	4	5	0.6	57	769	0	1	15	178
215	🇸🇩 Sudan	2	6	0.6	1,080	3,294	8	181	140	654
216	🇧🇱 Saint Barthélemy	2	3	0.5	214	1,343	0	222	22	510
217	🇲🇵 Northern Mariana Islands	2	5	0.5	109	1,729	3	0	20	300
218	🇲🇻 Maldives	2	4	0.5	1,597	751	57	1,488	36	187
219	🇵🇲 Saint Pierre and Miquelon	1	1	0.4	76	1,119	2	5	18	372
220	🇹🇴 Tonga	1	1	0.4	92	810	1	1	41	256
221	🇸🇿 Eswatini	3	3	0.4	595	784	1	1	37	156
222	🇩🇲 Dominica	1	2	0.4	192	446	1	8	19	141
223	🇹🇻 Tuvalu	1	2	0.4	109	65	3	191	6	38
224	🇫🇰 Falkland Islands	1	4	0.3	37	82	0	0	18	93
225	🇨🇰 Cook Islands	2	5	0.3	88	329	7	125	10	73
226	🇸🇨 Seychelles	3	3	0.3	275	603	10	12	42	175
227	🇸🇭 Saint Helena	2	3	0.3	59	333	0	6	28	107
228	🇹🇱 Timor-Leste	5	5	0.3	274	124	0	83	31	59
229	🇳🇫 Norfolk Island	1	2	0.3	4	16	0	0	3	8
230	🇨🇻 Cabo Verde	2	2	0.2	301	546	17	16	29	144
231	🇲🇶 Martinique	3	4	0.2	99	545	1	6	7	69
232	🇸🇹 Sao Tome and Principe	1	1	0.2	491	196	20	193	6	91
233	🇬🇼 Guinea-Bissau	1	1	0.2	721	463	4	40	23	93
234	🇾🇹 Mayotte	2	2	0.2	125	253	0	25	20	50
235	🇸🇧 Solomon Islands	1	2	0.1	166	379	0	0	37	135
236	🇨🇽 Christmas Island	1	1	0.1	56	127	0	1	7	32
237	🇳🇺 Niue	3	3	0.1	0	39	0	0	3	9
238	🇰🇮 Kiribati	2	2	0.1	71	96	5	17	8	30

Top 200 cities by footage

#	City	Country	Videos	Footage (h)	Persons	Cars	Bicycles	Motorcycles	Buses	Trucks
1	New York, NY	🇺🇸 United States	4,153	2,268.6	1,706,401	5,052,177	129,512	64,473	183,174	787,499
2	Chicago, IL	🇺🇸 United States	1,896	1,725.3	557,039	4,661,254	30,176	9,676	71,368	609,361
3	London	🇬🇧 United Kingdom	2,005	1,339.0	2,453,710	2,498,403	247,264	144,524	234,765	459,569
4	Los Angeles, CA	🇺🇸 United States	2,033	1,159.2	537,523	3,357,175	28,873	13,856	53,100	474,778
5	Hanoi	🇻🇳 Vietnam	2,149	874.7	2,271,937	1,607,428	63,791	2,164,743	100,239	215,239
6	Manila, Metro Manila	🇵🇭 Philippines	2,140	780.0	1,145,457	1,371,466	36,235	601,253	162,779	342,650
7	Sydney, NSW	🇦🇺 Australia	1,415	676.4	272,297	1,895,441	20,370	23,080	48,958	331,199
8	Saint Petersburg, Saint Petersburg	🇷🇺 Russia	689	566.0	290,101	1,405,043	6,951	1,923	68,078	166,481
9	Seoul	🇰🇷 South Korea	293	542.9	229,316	902,725	13,254	38,006	74,720	86,184
10	Jakarta	🇮🇩 Indonesia	1,603	448.3	746,405	985,480	6,244	663,413	59,104	232,593
11	Paris	🇫🇷 France	974	435.2	552,903	1,036,028	67,843	87,489	31,039	121,109
12	Toronto, ON	🇨🇦 Canada	924	399.7	463,377	1,279,686	42,527	12,535	26,332	146,625
13	Yangon	🇲🇲 Myanmar	488	357.1	350,381	1,461,843	40,642	20,587	95,870	243,748
14	Moscow, Moscow	🇷🇺 Russia	505	327.7	198,432	972,537	4,619	3,304	33,888	137,535
15	Philadelphia, PA	🇺🇸 United States	562	315.7	142,077	960,721	8,349	4,166	14,431	144,502
16	Dubai	🇦🇪 United Arab Emirates	745	311.6	81,499	623,331	2,874	13,974	36,942	89,583
17	Bangkok	🇹🇭 Thailand	605	302.4	378,377	806,126	6,909	266,949	40,210	164,900
18	Cotabato, Maguindanao del Norte	🇵🇭 Philippines	693	287.1	729,903	576,485	15,873	519,204	96,566	403,247
19	Melbourne, VIC	🇦🇺 Australia	572	280.2	199,141	994,924	12,797	10,296	18,731	115,711
20	Sao Paulo, SP	🇧🇷 Brazil	567	251.5	236,579	682,482	11,189	61,981	21,273	88,328
21	København	🇩🇰 Denmark	526	243.6	363,427	699,064	192,476	8,042	19,605	58,416
22	Kyiv, Kyiv	🇺🇦 Ukraine	569	240.9	163,823	890,032	4,840	2,486	39,375	123,287
23	Warsaw	🇵🇱 Poland	365	223.6	151,696	647,305	14,195	5,468	23,763	54,370
24	Berlin	🇩🇪 Germany	457	218.4	252,279	781,310	81,816	20,448	27,597	102,307
25	Buenos Aires, CABA	🇦🇷 Argentina	285	216.1	303,287	708,316	30,223	34,209	27,282	78,056
26	Townsville, QLD	🇦🇺 Australia	436	207.5	959	15,490	61	115	259	3,646
27	Detroit, MI	🇺🇸 United States	558	205.9	25,570	548,493	1,192	808	5,802	92,523
28	Guangzhou, Guangdong	🇨🇳 China	245	205.8	129,697	565,446	13,030	48,679	36,144	74,742
29	Montreal, QC	🇨🇦 Canada	470	202.5	114,906	570,680	13,299	3,268	10,961	50,197
30	Phnom Penh	🇰🇭 Cambodia	387	202.1	547,864	502,465	12,588	564,323	35,663	166,564
31	Helsinki	🇫🇮 Finland	282	198.1	107,865	388,813	10,614	1,719	19,427	48,452
32	Prague	🇨🇿 Czechia	239	187.1	198,447	462,120	5,078	4,823	20,212	45,241
33	Miami, FL	🇺🇸 United States	467	186.8	96,891	433,052	5,809	7,981	6,482	59,999
34	Rio de Janeiro, RJ	🇧🇷 Brazil	419	184.4	157,425	441,215	11,282	27,791	23,171	47,546
35	Kampala	🇺🇬 Uganda	466	176.4	210,110	97,690	5,333	116,814	9,743	31,876
36	Vienna	🇦🇹 Austria	714	173.7	103,254	436,287	11,849	12,577	20,222	74,380
37	Eindhoven	🇳🇱 Netherlands	329	171.4	157,940	402,576	109,956	15,504	10,035	39,405
38	Yogyakarta	🇮🇩 Indonesia	298	171.0	505,006	332,763	7,633	519,508	13,346	70,159
39	Tokyo, Tokyo	🇯🇵 Japan	253	170.2	176,774	250,287	19,589	9,299	26,281	76,096
40	Istanbul	🇹🇷 Türkiye	314	169.9	160,023	494,905	1,408	20,086	34,516	76,076
41	Stockholm	🇸🇪 Sweden	545	169.6	84,579	273,053	13,027	1,853	12,836	33,755
42	Perth, WA	🇦🇺 Australia	408	167.8	18,366	242,204	1,209	1,154	5,082	44,734
43	Bucharest	🇷🇴 Romania	325	166.7	126,986	768,567	4,781	4,178	19,656	68,103
44	Budapest	🇭🇺 Hungary	428	164.4	298,078	1,007,501	14,340	8,214	50,379	122,677
45	Sacramento, CA	🇺🇸 United States	413	159.5	17,385	548,148	1,273	972	6,082	97,520
46	Atlanta, GA	🇺🇸 United States	372	159.4	31,624	380,748	1,267	941	4,942	79,393
47	Kuala Lumpur, KUL	🇲🇾 Malaysia	325	159.1	95,099	460,754	718	49,102	10,679	38,508
48	Houston, TX	🇺🇸 United States	238	158.9	9,135	87,402	303	110	1,334	15,737
49	Vancouver, BC	🇨🇦 Canada	665	158.9	37,238	127,551	4,288	745	2,760	18,383
50	Singapore	🇸🇬 Singapore	399	155.2	81,730	342,138	5,203	20,569	33,441	60,162
51	Oldenburg	🇩🇪 Germany	172	152.7	80,109	221,155	43,435	3,352	7,404	22,413
52	Tyumen, Tyumen oblast	🇷🇺 Russia	306	147.5	92,426	428,881	1,999	537	19,867	49,241
53	Winnipeg, MB	🇨🇦 Canada	503	137.2	18,868	158,186	1,061	373	2,979	25,269
54	San Jose, CA	🇺🇸 United States	288	134.9	25,398	580,106	1,714	1,093	6,796	82,755
55	Sofia	🇧🇬 Bulgaria	429	132.4	68,315	419,963	1,316	910	15,132	36,911
56	San Francisco, CA	🇺🇸 United States	194	128.0	151,893	375,262	8,128	6,349	10,175	49,048
57	Den Haag	🇳🇱 Netherlands	259	126.8	17,237	73,901	11,683	2,891	2,087	7,860
58	Anchorage, AK	🇺🇸 United States	347	125.1	5,832	174,761	276	177	2,765	43,598
59	Dallas, TX	🇺🇸 United States	295	124.9	13,586	211,948	365	345	2,841	37,571
60	Washington, DC	🇺🇸 United States	217	123.9	80,735	353,897	6,526	2,341	8,202	47,413
61	Tehran	🇮🇷 Iran	266	122.4	40,265	267,292	1,103	15,061	4,567	24,450
62	Busan	🇰🇷 South Korea	143	121.7	66,773	304,102	2,522	8,513	12,317	38,578
63	Maracaibo, ZU	🇻🇪 Venezuela	505	119.6	80,947	307,185	4,467	30,463	6,089	63,208
64	Dublin	🇮🇪 Ireland	386	118.7	94,809	261,693	9,941	2,021	12,669	34,841
65	Las Vegas, NV	🇺🇸 United States	167	118.6	39,565	173,761	326	591	3,586	22,099
66	Oslo	🇳🇴 Norway	312	116.5	70,501	249,450	6,475	1,640	11,863	33,043
67	Portland, OR	🇺🇸 United States	183	113.1	55,775	215,779	3,747	873	4,360	27,908
68	Phoenix, AZ	🇺🇸 United States	235	110.1	9,745	96,185	457	292	1,412	19,898
69	Amsterdam	🇳🇱 Netherlands	204	109.3	238,339	214,152	173,948	28,103	10,986	30,853
70	Nashville, TN	🇺🇸 United States	290	100.3	29,278	58,625	293	243	1,472	9,800
71	Mumbai, MH	🇮🇳 India	261	98.0	232,809	317,101	6,219	94,627	18,328	71,425
72	Seattle, WA	🇺🇸 United States	199	96.0	0	0	0	0	0	0
73	Cairo	🇪🇬 Egypt	206	91.6	59,798	204,076	503	6,928	9,885	25,205
74	Volsk, Saratov oblast	🇷🇺 Russia	402	90.0	0	0	0	0	0	0
75	Kermanshah	🇮🇷 Iran	244	88.5	58,357	510,160	573	3,502	3,744	42,344
76	Cape Town	🇿🇦 South Africa	229	86.5	55,930	233,672	1,332	3,815	7,796	39,276
77	Darwin, NT	🇦🇺 Australia	253	85.6	12,507	95,060	454	930	1,832	19,780
78	Minsk	🇧🇾 Belarus	115	84.8	18,245	94,211	457	216	4,464	11,568
79	München	🇩🇪 Germany	169	81.8	80,877	260,636	26,811	4,455	10,029	33,084
80	Baltimore, MD	🇺🇸 United States	210	80.6	21,870	240,536	474	424	3,940	36,626
81	Serrekunda	🇬🇲 Gambia	267	77.0	179,757	252,094	10,645	17,150	6,831	52,811
82	Sylhet	🇧🇩 Bangladesh	194	74.5	302,421	78,883	58,803	56,760	26,739	63,853
83	Ho Chi Minh City	🇻🇳 Vietnam	139	73.7	168,431	72,961	3,064	173,005	6,858	17,849
84	Ulaanbaatar	🇲🇳 Mongolia	127	70.2	87,943	304,331	1,879	1,639	9,117	25,617
85	Gamagori, Aichi	🇯🇵 Japan	261	68.8	12,856	144,912	3,164	1,480	6,700	38,038
86	San Diego, CA	🇺🇸 United States	94	68.3	50,800	209,892	2,225	1,785	3,279	32,191
87	Denver, CO	🇺🇸 United States	196	68.1	16,772	113,558	1,325	582	2,031	20,464
88	Brisbane, QLD	🇦🇺 Australia	176	67.5	31,363	187,883	1,308	1,918	4,481	34,722
89	Damascus	🇸🇾 Syria	148	65.6	100,239	279,804	4,492	11,104	10,387	43,459
90	Karachi, SD	🇵🇰 Pakistan	145	65.4	186,698	238,894	6,397	95,433	16,302	55,090
91	Amman	🇯🇴 Jordan	199	61.9	35,564	263,325	107	881	5,889	32,157
92	Semarang	🇮🇩 Indonesia	322	61.7	85,584	91,843	1,616	85,609	6,698	31,419
93	Auckland	🇳🇿 New Zealand	142	61.5	43,978	159,198	2,536	1,631	6,578	19,959
94	Denpasar	🇮🇩 Indonesia	28	60.6	125,556	57,117	2,188	142,913	2,004	15,020
95	Tallinn	🇪🇪 Estonia	281	60.4	31,328	158,890	1,075	374	8,114	15,862
96	Hong Kong	🇭🇰 Hong Kong	142	60.2	95,898	94,454	1,185	2,867	18,406	18,096
97	Calgary, AB	🇨🇦 Canada	170	60.0	15,237	130,767	699	195	1,970	22,194
98	Ninh Binh	🇻🇳 Vietnam	197	58.2	61,841	55,887	6,806	71,260	3,687	15,457
99	Beijing, Beijing	🇨🇳 China	130	57.2	45,355	162,551	11,268	11,586	10,206	8,844
100	Catania	🇮🇹 Italy	144	56.5	17,124	125,837	199	8,707	1,660	6,666
101	Delhi, DL	🇮🇳 India	163	55.9	51,595	94,506	2,173	20,504	11,917	17,356
102	Boston, MA	🇺🇸 United States	108	54.0	53,953	96,954	2,750	769	2,391	14,297
103	Milan	🇮🇹 Italy	101	52.9	73,742	184,386	10,189	16,478	3,902	15,866
104	Orlando, FL	🇺🇸 United States	113	52.1	13,575	112,764	1,122	583	1,983	16,372
105	Shenzhen, Guangdong	🇨🇳 China	141	51.0	37,599	113,673	4,293	13,064	9,837	13,954
106	Aleppo	🇸🇾 Syria	137	51.0	109,909	209,868	1,733	4,556	6,149	36,402
107	Honolulu, HI	🇺🇸 United States	118	49.7	15,235	58,589	959	667	1,444	8,390
108	Shanghai, Shanghai	🇨🇳 China	65	49.1	53,160	86,157	6,478	14,750	6,013	6,637
109	Santiago	🇨🇱 Chile	85	48.1	3,844	8,094	171	358	608	919
110	Barcelona	🇪🇸 Spain	98	47.3	66,762	92,102	7,090	28,619	5,776	12,996
111	Mexico City, CDMX	🇲🇽 Mexico	75	47.2	52,019	120,584	2,579	4,669	4,066	12,100
112	Kolkata, WB	🇮🇳 India	149	45.9	119,686	74,046	21,248	34,029	18,923	35,086
113	Ben Tre	🇻🇳 Vietnam	87	44.6	140,156	31,560	4,697	138,142	3,292	11,874
114	Ninohe, Iwate	🇯🇵 Japan	247	44.2	7,095	74,697	390	333	5,606	24,760
115	Lviv, Lviv oblast	🇺🇦 Ukraine	145	44.2	25,400	44,522	386	202	2,937	6,041
116	Sarajevo	🇧🇦 Bosnia and Herzegovina	158	44.0	37,053	107,527	389	316	3,220	8,656
117	Colombo	🇱🇰 Sri Lanka	105	42.8	63,356	88,764	843	21,477	15,464	34,393
118	Toyohashi, Aichi	🇯🇵 Japan	153	42.6	10,689	87,598	2,765	875	4,699	24,684
119	Adelaide, SA	🇦🇺 Australia	123	42.5	9,433	128,216	915	567	2,805	17,240
120	Addis Ababa	🇪🇹 Ethiopia	68	42.3	97,122	102,712	472	1,800	8,940	30,449
121	Mashhad	🇮🇷 Iran	224	41.4	16,596	134,177	402	3,860	2,156	6,318
122	Nam Dinh	🇻🇳 Vietnam	128	40.9	63,624	44,482	5,773	74,649	1,685	8,048
123	Zaozhuang, Shandong	🇨🇳 China	122	39.9	23,493	126,774	1,344	13,859	5,983	19,760
124	Doha	🇶🇦 Qatar	78	39.5	10,088	100,428	407	1,230	4,807	14,696
125	Zürich, ZH	🇨🇭 Switzerland	76	39.4	53,676	80,646	9,908	5,253	4,172	10,945
126	Austin, TX	🇺🇸 United States	69	39.0	18,344	88,770	1,449	789	1,443	18,723
127	Madrid	🇪🇸 Spain	75	38.7	49,386	92,021	1,480	8,534	5,202	9,817
128	Jersey City, NJ	🇺🇸 United States	104	38.6	13,242	96,385	1,040	613	1,785	12,427
129	Baku	🇦🇿 Azerbaijan	91	38.3	37,213	189,258	524	1,048	5,479	17,773
130	Surabaya	🇮🇩 Indonesia	98	38.2	71,753	59,969	1,193	62,451	1,969	10,740
131	Rome	🇮🇹 Italy	73	37.7	57,411	145,068	2,234	12,687	3,480	10,865
132	Lisbon	🇵🇹 Portugal	70	37.3	30,103	111,823	1,071	3,689	3,349	11,815
133	Brussels	🇧🇪 Belgium	82	37.1	43,354	104,356	6,663	2,438	4,921	14,586
134	Nicosia	🇨🇾 Cyprus	126	37.1	10,741	121,456	466	2,889	2,331	16,521
135	Freetown	🇸🇱 Sierra Leone	90	36.7	93,685	39,617	676	21,317	3,852	18,564
136	Zagreb	🇭🇷 Croatia	94	35.8	27,055	131,851	2,828	958	3,139	9,776
137	Kathmandu	🇳🇵 Nepal	112	35.8	207,593	69,547	5,489	123,918	10,451	18,978
138	Kharkiv, Kharkiv oblast	🇺🇦 Ukraine	75	35.1	1,736	13,439	43	22	377	1,815
139	Lagos, Lagos	🇳🇬 Nigeria	73	35.1	150,463	103,955	1,369	17,007	9,246	27,667
140	Wellington	🇳🇿 New Zealand	113	34.8	17,968	92,977	986	1,071	3,114	12,604
141	Genoa	🇮🇹 Italy	71	34.3	46,192	126,323	928	38,965	3,751	12,946
142	Asaba, Delta	🇳🇬 Nigeria	77	34.3	1,748	2,602	8	88	354	986
143	Chennai, TN	🇮🇳 India	46	34.0	86,371	43,707	1,507	39,207	14,592	23,969
144	Riga	🇱🇻 Latvia	97	33.9	22,370	85,284	1,482	673	3,787	10,979
145	Thessaloniki	🇬🇷 Greece	81	33.8	42,570	158,855	1,217	12,761	3,361	13,565
146	Belgrade	🇷🇸 Serbia	96	32.8	31,196	132,090	446	949	4,988	10,467
147	Bengaluru, KA	🇮🇳 India	92	32.6	69,020	64,403	1,173	44,504	10,869	23,190
148	Tampa, FL	🇺🇸 United States	52	32.4	9,708	77,906	599	383	907	12,090
149	Birmingham	🇬🇧 United Kingdom	115	31.4	15,647	99,371	452	331	3,131	12,724
150	Rotterdam	🇳🇱 Netherlands	78	30.7	28,374	79,600	15,394	3,276	2,296	8,649
151	Edmonton, AB	🇨🇦 Canada	56	30.4	6,735	90,209	385	216	1,400	13,357
152	Riyadh	🇸🇦 Saudi Arabia	68	29.9	2,912	92,150	52	182	2,073	12,038
153	Tbilisi	🇬🇪 Georgia	75	29.6	21,518	126,225	124	1,027	4,603	14,487
154	Chengdu, Sichuan	🇨🇳 China	54	29.1	17,124	108,684	2,470	5,470	4,274	8,164
155	Kansas City, KS	🇺🇸 United States	53	29.1	3,353	60,130	91	68	826	10,550
156	Johor Bahru, JHR	🇲🇾 Malaysia	26	29.1	10,728	110,190	179	7,636	2,193	9,741
157	Goiânia, GO	🇧🇷 Brazil	64	28.8	15,464	75,395	807	8,488	1,145	8,074
158	Toshkent	🇺🇿 Uzbekistan	85	28.2	19,361	169,479	198	76	3,115	12,382
159	Milton Keynes	🇬🇧 United Kingdom	50	27.7	1,573	56,289	173	185	751	5,888
160	Lahore, PB	🇵🇰 Pakistan	88	26.8	37,406	45,089	1,435	23,507	1,479	8,244
161	Havana	🇨🇺 Cuba	132	26.3	6,648	1,806	219	1,028	238	768
162	Manchester	🇬🇧 United Kingdom	60	26.2	18,914	68,504	978	268	2,393	8,171
163	Pittsburgh, PA	🇺🇸 United States	54	25.8	13,181	49,044	580	242	1,098	7,794
164	Cherepovets, Vologda oblast	🇷🇺 Russia	176	25.0	25,909	71,495	65	25	4,084	10,354
165	Thiruvananthapuram, KL	🇮🇳 India	73	24.7	62,049	56,188	1,007	59,061	6,669	21,952
166	Lima, LI	🇵🇪 Peru	36	24.3	42,349	99,765	4,224	6,467	4,832	12,191
167	Frankfurt	🇩🇪 Germany	20	24.3	0	0	0	0	0	0
168	Vilnius	🇱🇹 Lithuania	60	24.2	26,719	69,153	2,302	624	2,342	4,938
169	Daejeon	🇰🇷 South Korea	60	24.0	9,482	70,925	1,046	1,140	2,785	5,925
170	Islamabad, ICT	🇵🇰 Pakistan	55	23.9	29,256	69,216	1,253	13,868	2,143	9,481
171	Stavropol, Stavropol krai	🇷🇺 Russia	79	23.9	13,437	84,585	262	83	3,340	8,224
172	New Orleans, LA	🇺🇸 United States	34	23.7	23,581	52,522	1,217	356	1,248	11,118
173	Samarkand	🇺🇿 Uzbekistan	81	23.6	5,900	34,462	121	104	871	4,017
174	Charleston, SC	🇺🇸 United States	38	23.0	24,457	54,823	932	308	853	12,763
175	Sendai, Miyagi	🇯🇵 Japan	56	22.5	6,181	53,941	896	511	3,157	12,835
176	Ōsaka, Ōsaka	🇯🇵 Japan	52	22.5	29,007	41,703	7,570	1,635	3,054	9,231
177	Porto	🇵🇹 Portugal	27	22.4	29,125	56,586	360	1,671	1,825	6,486
178	Hangzhou, Zhejiang	🇨🇳 China	28	22.3	6,651	23,852	411	2,726	1,181	1,997
179	San Antonio, TX	🇺🇸 United States	26	22.2	8,981	21,950	350	146	423	4,316
180	Kyoto, Kyōto	🇯🇵 Japan	45	22.0	26,428	38,391	3,395	2,054	4,409	10,184
181	Taipei	🇹🇼 Taiwan	49	21.8	25,569	49,857	1,741	20,972	3,186	6,147
182	Cheongju	🇰🇷 South Korea	64	21.4	6,168	57,371	316	676	2,049	7,607
183	Ottawa, ON	🇨🇦 Canada	37	21.3	25,772	53,063	2,916	395	1,347	5,874
184	Chiang Mai	🇹🇭 Thailand	53	21.0	15,632	41,695	507	15,811	1,510	12,962
185	Halifax, NS	🇨🇦 Canada	82	20.8	8,200	48,062	297	138	898	5,361
186	Sapporo, Hokkaidō	🇯🇵 Japan	60	20.8	7,760	35,309	523	92	2,059	4,800
187	Beirut	🇱🇧 Lebanon	68	20.5	16,855	101,182	106	6,178	1,850	10,127
188	Monaco	🇲🇨 Monaco	45	20.3	35,821	30,718	962	12,489	1,542	4,295
189	Yerevan	🇦🇲 Armenia	61	20.1	4,343	32,103	30	60	1,184	3,979
190	Manaus, AM	🇧🇷 Brazil	75	19.8	18,745	67,239	201	9,711	2,259	10,500
191	Chișinău	🇲🇩 Moldova	70	19.7	22,351	92,843	170	173	4,535	10,741
192	Ankara	🇹🇷 Türkiye	47	19.3	16,485	89,827	48	855	4,883	9,579
193	Malibu, CA	🇺🇸 United States	34	19.2	4,065	67,359	276	452	785	14,381
194	Medellín, ANT	🇨🇴 Colombia	50	19.2	5,392	7,277	141	1,864	478	962
195	Nice	🇫🇷 France	32	19.1	31,342	47,380	3,513	7,303	1,300	5,498
196	Skopje	🇲🇰 North Macedonia	39	18.9	21,132	71,350	1,425	755	2,142	6,384
197	Pattaya	🇹🇭 Thailand	34	18.8	16,998	32,964	210	14,355	1,891	10,355
198	Johannesburg	🇿🇦 South Africa	76	18.6	14,194	42,414	66	428	2,293	8,813
199	Roseville, CA	🇺🇸 United States	46	18.4	1,251	69,292	107	123	559	12,479
200	Borovskii, Tyumen oblast	🇷🇺 Russia	221	17.8	1,770	10,800	118	57	671	3,583

Citation and usage of code

If you use this work for academic work please cite the following paper:

Alam, M. S., Bazilinska, O., & Bazilinskyy, P. (2026). A global dataset of continuous urban dashcam driving. arXiv preprint arXiv:2604.01044. Under review. Available at https://arxiv.org/abs/2604.01044

What the dataset contains, how it was collected and its known biases are described in the datasheet. Citation metadata is in CITATION.cff.

The code is open-source and free to use. It is aimed for, but not limited to, academic research. We welcome forking of this repository, pull requests, and any contributions in the spirit of open science and open-source code. For inquiries about collaboration, you may contact Md Shadab Alam (md_shadab_alam@outlook.com) or Pavlo Bazilinskyy (pavlo.bazilinskyy@gmail.com).

Repository layout

top folder: the analysis (main.py, analysis.py), its shared modules (common.py, helper_script.py, logmod.py, custom_logger.py, add_video.py) and the channel pipeline (run_channels.py, propose_segments.py, review_hub.py, ...); utils/ holds the plotting and analytics code
release/: the weekly metadata update and the release files (update_info.py, make_crowd_jsonl.py, split_large_files.py, validation_release.py, weekly_metadata.sh)
tools/: one-off maintenance scripts for the mapping and the data
dashboards/: pages and widgets that follow the pipeline (its own README)
analysis_data/: data files the analysis reads and writes: country tables (countries.csv, height_data.csv), the YOLO detection counts (detection_counts.csv.gz) and who added which video (video_contributors.csv); the mapping and metadata files (mapping.csv, mapping-NAME.csv, mapping_metadata.csv, crowd_index.jsonl, ...) and cut_times.csv stay in the top folder
logs/: records kept by the scripts (traffic_index_log.csv, appended by tools/update_traffic_index.py)
archive/: old files no code uses any more (mappingsubmitted.csv)
tests/

Run the scripts with the virtual environment active (see Step 11).

Getting started

Tested with Python 3.12.13 and the uv package manager.

The project is configured to automatically select the appropriate PyTorch backend. On systems with a supported NVIDIA GPU and CUDA driver, uv installs a CUDA enabled version of PyTorch. If no supported GPU is detected, it automatically falls back to the CPU version.

Step 1: Install `uv`

uv is a fast Python package and environment manager.

macOS / Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows PowerShell:

irm https://astral.sh/uv/install.ps1 | iex

Alternative, if Python and pip are already installed:

pip install uv

Step 2: Fix permissions if needed

In some environments, uv may need to create directories under:

macOS / Linux:

~/.local/share/uv/python

Windows:

%LOCALAPPDATA%\uv\python

If these directories were previously created by another user or with elevated privileges, you may encounter an error such as:

error: failed to create directory ... Permission denied (os error 13)

macOS / Linux

mkdir -p ~/.local/share/uv
chown -R "$(id -un)":"$(id -gn)" ~/.local/share/uv
chmod -R u+rwX ~/.local/share/uv

Windows PowerShell

New-Item -ItemType Directory -Force "$env:LOCALAPPDATA\uv"

icacls "$env:LOCALAPPDATA\uv" /grant "$($env:UserName):(OI)(CI)F"

Step 3: Verify `uv`

Check that uv is available:

uv --version

Step 4: Clone the repository

git clone https://github.com/crowd-dataset/crowd.git
cd crowd

Step 5: Install Python 3.12.13

The project is tested with Python 3.12.13.

Install it using uv:

uv python install 3.12.13

The repository contains a .python-version file so that uv can automatically select the correct Python version.

Step 6: Create the virtual environment

Create a virtual environment in .venv using Python 3.12.13:

uv venv --python 3.12.13

Step 7: Activate the virtual environment

macOS / Linux:

source .venv/bin/activate

Windows PowerShell:

.\.venv\Scripts\Activate.ps1

Windows Command Prompt:

.\.venv\Scripts\activate.bat

Step 8: Install the dependencies

Install the project dependencies from pyproject.toml:

uv pip install -r pyproject.toml

The project uses:

[tool.uv.pip]
torch-backend = "auto"

This allows uv to automatically select the appropriate PyTorch backend for the current machine.

For example:

NVIDIA GPU with a compatible CUDA driver → CUDA enabled PyTorch
No supported GPU → CPU PyTorch

No manual selection of a CUDA version should normally be required.

Note: Do not use uv sync for the initial installation if automatic PyTorch backend detection is required. Automatic torch-backend selection currently applies to the uv pip interface.

Step 9: Verify the PyTorch backend

Check which PyTorch backend was installed:

python -c "import torch; print('PyTorch:', torch.__version__); print('CUDA available:', torch.cuda.is_available()); print('CUDA version:', torch.version.cuda); print('Device:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU')"

On an NVIDIA CUDA system, the output should look similar to:

PyTorch: 2.8.0+cu129
CUDA available: True
CUDA version: 12.9
Device: NVIDIA GeForce RTX 5090

On a machine without a supported GPU, CUDA available will be False and the CPU version of PyTorch will be used.

Step 10: Create `config` and `secret`

mapping.csv (the videos, segments and localities) is in the top folder of the repository. Copy the two templates in the top folder:

cp default.config config
cp default.secret secret

In config, set data to the folders that hold (or will hold) the YOLO output (bbox/*.csv in each), videos to the folders with the videos, and mapping to the mapping file (mapping.csv); the other parameters are described in Configuration of project. Without a config, default.config is used. Fill in only the keys of secret you need (The secret file); the analysis needs none.

Step 11: Run the scripts

Run the scripts from the top folder of the repository with the virtual environment active:

python main.py       # download the videos and run YOLO on their segments (runs in a loop)
python analysis.py   # tables and figures from the YOLO output in the data folders

The scripts in tools/ and release/ can be run as a file or as a module, and from any folder (they switch to the top folder, where mapping.csv and the other files are):

python tools/check_lists.py
python -m tools.check_lists

The tests run with python -m pytest tests (after uv pip install pytest).

Configuration of project

Configuration of the project needs to be defined in config. Please use the default.config file for the required structure of the file. If no custom config file is provided, default.config is used. The config file has the following parameters:

data: Directory containing data (CSV output from YOLO).
videos: Directories containing the videos used to generate the data.
mapping: CSV file that contains mapping data for the cities referenced in the data.
prediction_mode: Configures YOLO for object detection.
tracking_mode: Configures YOLO for object tracking.
always_analyse: Always conduct analysis even when pickle files are present (good for testing).
display_frame_tracking: Displays the frame tracking during analysis.
save_annotated_img: Saves the annotated frames produced by YOLO.
delete_labels: Deletes label files from YOLO output.
delete_frames: Deletes frames from YOLO output.
delete_youtube_video: Deletes saved YouTube videos.
compress_youtube_video: Compresses YouTube videos (using the H.255 codec by default).
delete_runs_files: Deletes files containing YOLO output after analysis.
check_missing_mapping: Identifies all the missing csv files.
min_max_videos: Gives snippets of the fastest and slowest crossing pedestrian.
track_buffer_sec: Keep tracks longer (in seconds).
analysis_level: Specifies the analysis level; supported versions include city and country.
client: Specifies the client type for downloading YouTube videos; accepted values are "WEB", "ANDROID" or "ios".
model: Specifies the YOLO model to use; supported/tested versions include v8x and v11x.
population_threshold: Specifies the minimum population a city must have to be included in the analysis.
footage_threshold: Specifies the minimum amount of footage required for a city to be included in the analysis.
min_city_population_percentage: Specifies the minimum proportion of a country’s population that a city must have to be included in the analysis.
countries_analyse: Lists the countries to be analysed.
confidence: Sets the confidence threshold parameter for YOLO.
update_ISO_code: Updates the ISO code of the country in the mapping file during analysis.
update_pop_country: Updates the country’s population in the mapping file during analysis.
update_gini_value: Updates the GINI value of the country in the mapping file during analysis.
update_pytubefix: Updates the pytubefix library each time analysis starts.
font_family: Specifies the font family to be used in outputs.
font_size: Specifies the font size to be used in outputs.
plotly_template: Defines the template for Plotly figures.
open_html_figures (optional): Set to true to open every saved HTML figure in the browser. Off when absent.
logger_level: Level of console output. Can be: debug, info, warning, error.
sleep_sec: Amount of seconds of pause in the end of the loop in main.py.
git_pull: Pull changes from git repository in the end of the loop in main.py.
email_send: Send email about completion of the job in the end of the loop in main.py. See the following paragraph for the additional parameters in the secret file.
email_sender: Email address of the the "sender" of the email.
email_recipients: List of emails for sending the message.
max_workers: Specifies the maximum number of segment-processing worker threads (i.e., how many segments can be analysed in parallel). Increasing this increases concurrent segment processing, subject to GPU/CPU and I/O limits.
download_workers: Specifies the maximum number of concurrent video download/prepare workers. Increasing this allows multiple videos to be downloaded/prepared in parallel (useful when network/FTP is the bottleneck).
max_active_segments_per_video: Specifies the maximum number of segments from the same video that are allowed to be processed concurrently.
- If set to 1, the scheduler tends to distribute workers across different videos (e.g., with max_workers=3, it will try to process 3 different videos at once).
- If set to 2+, multiple workers may process segments from the same video simultaneously, which can improve throughput when one video has many segments but reduces “video diversity” across workers.

The `secret` file

API keys, passwords and usernames live in a file named secret (no extension) in the root of the project. It is ignored by git, so it is never committed. To create it, copy the template default.secret to secret and fill in the values you need. Any value can stay empty ("") if you do not use the feature it belongs to. Running the analysis on the released dataset needs none of them; running main.py needs at least an empty secret copied from the template.

The file is a single JSON object:

{
  "geonames_username": "",
  "bea_api_key": "",
  "tomtom_api_key": "",
  "restcountries_api_key": "",
  "email_smtp": "",
  "email_account": "",
  "email_password": "",
  "ftp_username": "",
  "ftp_password": "",
  "carto_api_key": "",
  "youtube_api_key": "",
  "locationiq_api_key": ""
}

Which keys you need depends on what you run:

analysis of the released dataset (analysis.py): none; carto_api_key is optional.
adding videos (add_video.py): geonames_username, restcountries_api_key, tomtom_api_key and youtube_api_key; bea_api_key for US localities.
proposing segments from channels (propose_segments.py): youtube_api_key; geonames_username and locationiq_api_key help.
updating the traffic index (tools/update_traffic_index.py): tomtom_api_key.
processing videos (main.py): ftp_username and ftp_password for the VideoFiles server; the email keys only if email_send is on in config.

Key	Used by	What for	Where to get it
`geonames_username`	`add_video.py`, `propose_segments.py`	locality population, a fallback for coordinates, and place lookups	free account at GeoNames; see below
`bea_api_key`	`add_video.py`	GDP of US localities	free key from BEA; see below
`tomtom_api_key`	`add_video.py`, `tools/update_traffic_index.py`	traffic index from TomTom traffic flow	free account at TomTom; see below
`restcountries_api_key`	`add_video.py`	country population, continent and Gini	free account at REST Countries; see below
`email_smtp`, `email_account`, `email_password`	`main.py`	email when a job finishes (`email_send` in `config`)	your email provider; see below
`ftp_username`, `ftp_password`	`main.py`	downloading videos from the VideoFiles server	the maintainers (see Contact)
`carto_api_key`	`utils/plotting/maps.py`	optional: basemap tiles for the Mapbox-style maps without CARTO's watermark; without a key, CARTO's free basemap is used	CARTO; see below
`youtube_api_key`	`propose_segments.py`, `add_video.py`	listing a channel's videos and their length, upload date and channel, without downloading them	see below
`locationiq_api_key`	`propose_segments.py`	optional: faster place lookups (localities from titles, descriptions and chapters, and the towns along a drive "from A to B")	see below

Getting a YouTube Data API key

The key is free; the default quota of 10,000 units a day covers several large channels (listing a channel's search results costs 100 units per 50 videos, video details 1 unit per 50 videos).

Go to the Google Cloud console and sign in with the Google account you want the key to belong to.
Create a project: project selector at the top → New project → give it a name (e.g. crowd) → Create, then select it.
Open APIs & Services → Library, search for YouTube Data API v3 and click Enable.
Open APIs & Services → Credentials → Create credentials → API key. Copy the key that appears.
Recommended: click the new key → under API restrictions choose Restrict key → select YouTube Data API v3 → Save, so the key cannot be used for anything else.
Put the key in secret as "youtube_api_key": "...".

Getting a LocationIQ key (optional)

propose_segments.py looks places up on OpenStreetMap: to guess a video's locality from a named place ("Al Khan Beach" is in Sharjah) and to split a drive "from A to B" into the towns along its route (about 25 lookups per such drive). Without a key it uses the public Nominatim server, which allows one request a second and refuses requests for a while when a busy day goes over that. LocationIQ runs the same OpenStreetMap search with a free key: 5,000 requests a day at two a second, with no payment details. With a key in secret, lookups go to LocationIQ; once its daily requests are used up, they fall back to the public server.

Sign up for the free plan at locationiq.com and confirm your email address.
Open the dashboard's Access Tokens page and copy the token there.
Put it in secret as "locationiq_api_key": "...". Nothing else needs changing; running channel batches pick it up from their next chunk.

Getting a GeoNames username

GeoNames is free; an account allows 10,000 requests a day and 1,000 an hour.

Create an account at geonames.org/login and confirm your email address with the link GeoNames sends.
Sign in and open your account page. At the bottom, click Click here to enable the free web services. Requests are refused until this is done.
Put your username (not your email address) in secret as "geonames_username": "...".

Getting a BEA API key

The U.S. Bureau of Economic Analysis key is free and is used only for localities in the USA.

Fill in your name and email address at apps.bea.gov/API/signup.
BEA emails you the key (a 36-character code); click the activation link in that email.
Put the key in secret as "bea_api_key": "...".

Getting a TomTom API key

The traffic index uses TomTom's Traffic Flow API (segment data). Its free tier allows 20,000 requests a month, with no payment details needed.

Create an account at developer.tomtom.com and confirm your email address.
Open the Dashboard: a key is created with the account (Keys section). Copy it.
Put it in secret as "tomtom_api_key": "...".

tools/update_traffic_index.py uses one request per locality and only queries localities where it is local daytime; requests beyond the free tier are refused unless payment details are added to the TomTom account.

Getting a REST Countries API key

add_video.py uses version 5 of the REST Countries API, which needs a key. The free plan allows 1,000 requests a month, with no payment details.

Sign up at restcountries.com/sign-up and confirm your email address.
Create an API key in the dashboard and copy it.
Put it in secret as "restcountries_api_key": "...".

Email settings

main.py can email you when a job finishes (email_send, email_sender and email_recipients in config). It connects to the SMTP server over SSL on port 465.

email_smtp: the SMTP server of your provider, e.g. smtp.gmail.com for Gmail.
email_account: the full email address you send from.
email_password: for Gmail, an app password, not your normal password. Turn on 2-Step Verification for the Google account, then create an app password at myaccount.google.com/apppasswords and paste the 16 characters. Other providers have similar app passwords or use the account password.

Getting a CARTO key (optional)

The Mapbox-style maps (mapbox_map_*) use CARTO's free basemap, which shows a CARTO watermark. With a key, maps.py loads CARTO's basemap tiles with the key instead.

Create an account at carto.com.
In the CARTO Workspace, create an API access token under Developers → Credentials and copy it.
Put it in secret as "carto_api_key": "...".

Example of YOLO output for a video with dashcam footage

Video: https://www.youtube.com/watch?v=_Wyg213IZDI.

Selection procedure

Verification of segments

After segments were added, they were checked again by hand. The checks were done by Md Shadab Alam, on the main branch, for the segments of mapping.csv, including the videos that other contributors of this repository added (see Who added the videos).

1. Candidate cuts. Points inside segments where the footage may break (a jump in time, a different clip, or a part that should not be analysed) are listed in cut_times.csv, with the video, the segment start and the time of the candidate cut.

2. Reviewing. For each candidate cut, the YouTube video was played for a few seconds around the cut. The reviewer marked the candidate as yes (a real cut), no (not a cut) or unavailable (YouTube no longer plays or embeds the video), and could add a note. The decision and the time of the review are saved in cut_times.csv.

In total, 52,838 cuts were analysed.

3. What the reviewer looked for.

Cuts in videos added by other contributors. The segments of the videos added by the other authors of the repository were checked for cuts, in the same way as the others.
Videos that are not at normal speed. Videos that play faster or slower than 1x (for example fast-forwarded footage or time-lapses) are not suitable, because speeds and crossing times in them are wrong. Such cases are noted, for example as fast forward.
Advertisements in the footage. An advertisement that was cut into the middle of a video is not footage of the road. Where one was found, it was removed from the segments by the reviewer.

4. Applying the result. Each candidate marked yes splits its segment in mapping.csv into the part before and the part after the cut, and the detection CSV of the segment is split at the same time, so that the footage around the cut is not analysed. Segments that become too short are removed. Detection CSVs that no longer match a segment in mapping.csv are removed from the data folder.

Description and analysis of dataset

Note: The interactive figures are displayed using htmlpreview.github.io, which may be blocked on some institutional or education networks. If an interactive figure does not open, try another network or download the corresponding .html file from the figures directory and open it locally in a web browser.

Description of dataset

Locations of cities with footage in dataset. Note: continents are based on geography, i.e., the cities in Russia east from Ural mountains are shown as Asia.

Locations of cities with footage in dataset with a density overlay of population. Note: continents are based on geography, i.e., the cities in Russia east from Ural mountains are shown as Asia.

Locations of cities with footage in dataset with a density overlay of the number of videos in the dataset. Note: continents are based on geography, i.e., the cities in Russia east from Ural mountains are shown as Asia.

Locations of cities with footage in dataset with a density overlay of the total number of seconds of footage in the dataset. Note: continents are based on geography, i.e., the cities in Russia east from Ural mountains are shown as Asia.

Amount of footage (hours) per country in Africa. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per country in Asia. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per country in Europe. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per country in North America. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per country in Oceania. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per country in South America. Note: the colour scale is logarithmic; countries are grouped by the continent of their cities in the dataset.

Amount of footage (hours) per US state. Note: the colour scale is logarithmic.

Footage per US county (log colour scale): localities are assigned to the county they lie in. Hover over a county for its localities.

Hours of footage per million people per US county (population: US Census Bureau estimates, 2024).

Share of footage recorded at night per US county (counties with at least an hour of footage).

Footage per European region: Eurostat NUTS 2 regions for the EU, EFTA, the United Kingdom and candidate countries, and geoBoundaries regions for European Russia (west of the Urals), Ukraine, Belarus and Bosnia and Herzegovina; Moldova, Kosovo and small countries as a whole.

Hours of footage per million people per European region (population: Eurostat, each region's latest year; for Russia, Ukraine, Belarus and Bosnia and Herzegovina, Wikidata's latest figure).

Share of footage recorded at night per European region (regions with at least an hour of footage).

One spike per locality in the United States; height and colour show its hours of footage (log scale).

One spike per locality in Russia.

One spike per locality in Japan.

Where the footage is in Europe: hours of footage per 10,000 km², smoothed over about 50 km (log colour scale); grey: no footage nearby. Callouts mark the densest places.

Where the footage is in North America.

Where the footage is in Asia.

Blind spots of the dataset in Europe: distance from every place to the nearest locality with at least an hour of footage. Callouts mark the places furthest from any footage.

Blind spots of the dataset in North America.

Blind spots of the dataset in Asia.

Share of footage recorded at night in Europe, smoothed over about 50 km and weighted by footage; grey where there is less than an hour of footage within 100 km.

Average year of upload of the footage in Europe (weighted by footage): where the footage is recent and where it is older.

Number of videos against the amount of footage per locality (log scales), coloured by continent, with every country and its flag in the legend on the right, in alphabetical order (scroll it in the HTML version; click a country to hide or show it); hover over a point for its details. The 40 localities with the most footage are labelled, the top 12 in larger black text; in the HTML version, zoom in to label every point.

The same on linear scales, which show how far the localities with the most footage lead, with the same country legend. The 40 localities with the most footage are labelled, the top 12 in larger black text; in the HTML version, zoom in to label every point.

Number of videos against the amount of footage per country (log scales), coloured by continent; hover over a point for its details. Countries are labelled with their flag and ISO3 code, the 30 with the most footage in larger black text; in the HTML version, zoom in to label every point.

The same on linear scales, which show how far the countries with the most footage lead. The 30 countries with the most footage are labelled with their flag and ISO3 code; in the HTML version, zoom in to label every point.

Distribution of videos by continent. Note: continents are based on geography, i.e., the cities in Russia east from Ural mountains are shown as Asia.

Number of videos by quarter of upload and continent. The current quarter (hatched) is not complete yet.

Distribution of segments (parts of videos included in dataset) by type of vehicle.

Amount of footage per locality against its population. Large localities with little footage are candidates for adding videos.

Footage per locality against its population (log scales), coloured by whether a locality has more than three times, about, or less than a third of the footage its size predicts (the dotted least-squares line). Labelled: the most populous localities, those with the most footage, and the most under-covered cities of over a million people, which are the next ones to collect.

Amount of footage per locality against its gross metropolitan product (GMP, known for about 200 large cities), with the Spearman rank correlation: whether economically large cities dominate the data.

Amount of footage per locality against its traffic index (how much slower than free flow traffic is, from one TomTom reading per locality), for localities with a reading above 0, with the Spearman rank correlation.

Amount of footage per country against country indicators, with the Spearman rank correlation and a trend line per panel, showing whether some kinds of countries are over- or under-represented.

Share of footage recorded at night per country. Countries with less than 10 hours of footage are grey, as their share is not reliable.

Hours of footage per million inhabitants per country: coverage relative to country size (log scale).

Hours of footage per million people against GDP per person (both log scales; circle size: population), coloured by whether a country has more than twice, about, or less than half the footage its wealth predicts (the dotted least-squares line). GDP per person at purchasing-power parity is the World Bank's latest value.

Share of each country's footage that comes from its largest YouTube channel. Where it is high, one uploader's routes and camera dominate that country's data. Countries with less than 10 hours of footage are grey.

Type of vehicle the footage is recorded from, as a share of footage per continent.

Share of footage coming from the largest YouTube channels.

Distribution of the length of segments (parts of videos included in the dataset).

Every locality in the dataset as a dot coloured by continent; the dot area is proportional to the hours of footage.

The same localities on a globe. The HTML version spins until you grab it; drag to turn it.

The localities on a 3D satellite globe (NASA Blue Marble), with a spike for each locality whose height shows its hours of footage on a log scale, so localities with little footage are visible too. In the HTML version, a button merges neighbouring localities into one spike per area of about 200 km, which is easier to read where localities are dense. Each spike is orange for the share of its footage recorded by day and blue for the share at night. The HTML version spins until you grab it; drag to turn it, scroll to zoom.

The spinning globe of the CROWD dashboards: every locality as a square that grows with its hours of footage, and the places with segments added in the last week (red) and month (amber). The HTML version loads the latest data when it opens (from the globe on bazilinskyy.github.io, rebuilt every night) and turns until you grab it; drag to turn it, hover over a place for its footage and new segments, and click it to watch its longest segment on YouTube.

Footage by continent, country and locality, coloured by the share recorded at night. In the HTML version, click a box to zoom in down to the localities.

Share of footage against share of population (of all countries in the dataset) for the 25 largest countries by either share (log scale). Where the orange dot is left of the blue one, the country is under-represented.

Share of footage by population of the locality, per continent and overall: what "urban" means in each part of the dataset.

Effective number of YouTube channels per country (inverse Herfindahl index of the channels' shares of footage): 1 means a single uploader; n means as diverse as n channels with equal shares. Countries with less than 10 hours of footage are grey.

Effective number of YouTube channels against hours of footage per country (log scales; circle size: population), coloured by whether a country has more than twice, about, or less than half the channels its amount of footage predicts. Countries below the line rest on few uploaders, so their footage reflects few channels' routes, cameras and styles.

Where the 20 YouTube channels with the most footage film, numbered by footage, with the number of countries and hours of each (dot area proportional to hours). Travel channels span many countries; most large channels film in one country or city. In the HTML version, each channel's number links to the channel on YouTube.

Length of segments by continent and by type of vehicle (boxes: quartiles; whiskers: 5th and 95th percentiles).

Detections in the footage (YOLO)

Objects detected in the footage by YOLO (yolo11x with tracking): unique tracked persons, cars, bicycles, motorcycles, buses and trucks, counted with a confidence of at least 0.7 and expressed per minute of analysed footage. The counts per segment are kept in analysis_data/detection_counts.csv.gz (video, start of the segment and the unique tracked objects of each class), which python count_detections.py --data <folder with bbox/> writes where the YOLO detection CSV files are (the first run reads about 900 GB of CSVs, in parallel with --workers; later runs read only new or changed files, cached in _output/). analysis.py, also in the GitHub Action, makes the tables and figures below from it, matched to the current mapping, so they stay up to date without the CSV files. Every Sunday at 20:00 (Amsterdam time), tools/weekly_detection_counts.sh on the machine with the data drive counts newly analysed footage and pushes analysis_data/detection_counts.csv.gz if it changed, ahead of the weekly refresh of the figures.

The detections are automatic outputs, not ground truth. What the detector sees depends on the camera, resolution and viewpoint, so the figures by vehicle type and by year of upload show these effects next to the comparisons between countries.

Based on 86,151 detection CSV files, last updated 2026-10-17 07:39 UTC.

Tables

Detections per continent

#	Continent	Analysed (h)	Of footage (%)	Localities	Persons / min	Cars / min	Bicycles / min	Motorcycles / min	Buses / min	Trucks / min
1	Africa	756.9	73.8	540	37.21	43.48	1.16	7.93	2.24	9.40
2	Asia	9,283.9	96.3	2,141	24.27	40.55	1.03	15.65	2.73	8.10
3	Europe	8,448.8	89.3	3,651	17.12	44.72	2.61	1.35	1.94	5.53
4	North America	9,045.5	76.4	2,287	9.69	50.06	0.65	0.39	1.00	7.85
5	Oceania	1,333.9	72.5	436	8.28	53.25	0.55	0.56	1.29	8.73
6	South America	1,127.1	88.1	398	18.47	47.96	1.32	4.45	1.78	7.03
7	All	29,996.1	85.5	9,453	17.26	45.51	1.35	5.73	1.87	7.32

Top 20 countries by pedestrians per minute (at least 10 analysed hours in at least 3 localities)

#	Country	Pedestrians / min	95% interval	Analysed (h)	Localities
1	🇳🇵 Nepal	92.85	25.71–96.55	38.1	4
2	🇳🇬 Nigeria	63.33	38.92–70.41	56.9	34
3	🇧🇩 Bangladesh	60.73	41.68–66.12	107.2	29
4	🇰🇪 Kenya	52.53	49.14–59.82	19.9	55
5	🇵🇰 Pakistan	51.73	38.66–67.73	142.4	37
6	🇮🇳 India	44.01	40.06–49.22	450.3	293
7	🇰🇭 Cambodia	43.54	24.40–45.14	222.5	7
8	🇻🇳 Vietnam	42.64	25.36–45.04	1,110.8	68
9	🇪🇹 Ethiopia	41.02	39.50–73.56	42.4	8
10	🇬🇲 Gambia	39.59	32.25–51.09	95.4	29
11	🇲🇼 Malawi	37.69	29.68–49.66	29.9	48
12	🇨🇬 Congo (Congo-Brazzaville)	37.40	5.83–38.06	11.8	3
13	🇸🇱 Sierra Leone	35.88	22.87–41.69	79.1	46
14	🇨🇴 Colombia	34.63	31.58–38.46	41.4	35
15	🇬🇭 Ghana	32.50	26.84–60.01	11.4	10
16	🇮🇩 Indonesia	32.11	25.54–41.46	969.6	175
17	🇲🇦 Morocco	30.32	26.77–35.61	46.9	34
18	🇬🇧 United Kingdom	29.72	9.56–33.09	1,496.8	353
19	🇸🇾 Syria	29.49	14.75–35.58	122.3	11
20	🇭🇺 Hungary	29.41	7.98–30.01	171.5	34

Top 20 localities by pedestrians per minute (at least 1 analysed hour)

#	Locality	Pedestrians / min	Analysed (h)
1	🇮🇳 Varanasi	126.64	1.0
2	🇨🇫 Bangui	103.73	5.3
3	🇮🇳 Anantapur	103.57	1.2
4	🇵🇰 Dera Ismail Khan	98.21	1.2
5	🇵🇰 Sialkot	98.18	3.4
6	🇳🇵 Kathmandu	97.07	35.6
7	🇲🇬 Toamasina	89.05	1.3
8	🇵🇰 Multan	88.86	9.8
9	🇺🇬 Kampala	87.35	40.1
10	🇸🇱 Kenema	86.35	2.4
11	🇸🇱 Makeni	80.86	1.2
12	🇵🇰 Mansehra	76.74	2.1
13	🇮🇳 Gaya	76.16	3.0
14	🇳🇬 Lagos	73.20	34.3
15	🇳🇬 Ibadan	71.18	5.2
16	🇮🇳 Dehradun	71.17	1.3
17	🇵🇰 Abbottabad	70.47	1.9
18	🇵🇰 Faisalabad	70.15	1.8
19	🇸🇱 Bo	68.89	2.6
20	🇧🇩 Sylhet	67.63	74.5

Figures

Share of each country's footage that has been analysed with YOLO (countries with less than 10 hours of footage are grey).

Pedestrians per minute of footage per country (unique tracked persons).

Pedestrians per minute of footage per country, with a 95% bootstrap interval over its localities (countries with at least 10 analysed hours in at least 3 localities).

How much footage a stable estimate of a locality needs: the width of its 95% bootstrap interval of pedestrians per minute over its segments (as a share of the rate) against its analysed hours (localities with at least 5 segments; segments of one drive are alike, so the true intervals are somewhat wider).

Pedestrians against cars per minute per locality (at least an analysed hour), split at the medians into walkable, busy, car-dominated and quiet streets.

Pedestrians per minute by population of the locality, per continent, with a 95% bootstrap interval over localities (groups with at least 3 localities and an analysed hour).

Road-user mix of the 30 localities with the most analysed footage, each as 100 squares (ordered by the share of pedestrians).

Detected road users per minute of footage, per class, in the 40 countries with the most analysed footage (log colour scale).

Pedestrians per minute of footage per locality, by continent.

Distribution of pedestrians per minute over segments of at least a minute (log scale).

Mix of detected road users per continent.

Mix of detected road users in the 30 countries with the most analysed footage.

Share of two-wheelers (motorcycles and bicycles) among the detected vehicles per country (countries with less than 10 analysed hours are grey).

Detected road users per minute by day and by night, per class and continent (log scale).

Pedestrians per minute of footage by day and by night per country (countries with at least an hour of each).

Pedestrians per minute by the type of vehicle the footage is filmed from: the viewpoint changes what the detector sees.

Detected road users per minute by year of upload (years with at least 10 analysed hours): newer cameras and higher resolutions can change what the detector sees.

Pedestrians per minute of footage per country against country indicators: the mapping's, GDP per person, urban population, density and jobs in agriculture (World Bank), pedestrians among road deaths, registered vehicles, physical inactivity and obesity (WHO), mean temperature and rainfall (ERA5, World Bank climate portal) and the footage-weighted TomTom traffic index.

Pedestrians per minute of footage against GDP per person (countries with at least 10 analysed hours; circle size: population), coloured by whether a country has more than 1.5 times, about, or less than two-thirds of the pedestrians its wealth predicts.

Motorcycles as a share of detected vehicles against GDP per person, coloured by whether a country has more than twice, about, or less than half the share its wealth predicts.

Bicycles per minute against GDP per person: cycling cultures stand out above the line.

Cars per minute against GDP per person: motorisation on the street, relative to wealth.

Road traffic deaths per 100,000 people against pedestrians per minute: countries with more or fewer deaths than their number of pedestrians on the street predicts.

Pedestrian road deaths per 100,000 people (road deaths times the WHO's pedestrian share of them, 2016) against pedestrians per minute: where walking is more or less deadly than the number of pedestrians on the street predicts.

Pedestrians per minute per US county (counties with at least an hour of analysed footage).

Pedestrians per minute per European region (NUTS 2, and first-level regions of European Russia, Ukraine, Belarus and Bosnia and Herzegovina; regions with at least an hour of analysed footage).

Bicycles per minute per European region: cycling regions stand out.

Pedestrians per minute in Europe, smoothed over about 50 km; grey where there is less than an analysed hour within 100 km.

Walkable street minutes in Europe: the share of analysed minutes with at least 2 pedestrians and at most 10 cars per minute, smoothed over about 50 km.

Motorcycles as a share of detected vehicles in Europe, smoothed over about 50 km.

Bicycles per minute in Europe, smoothed over about 50 km.

Pedestrians per minute per locality against its population (localities with at least an hour of analysed footage): busy and quiet streets for a city's size.

Pedestrians per minute of footage per locality against its population.

Adding videos to dataset

To add videos to the mapping file by hand, run python add_video.py. It opens a web form (a Flask app on a local port) for one video at a time. Fill in the locality, the state (optional, recommended for the USA 🇺🇸 and Canada 🇨🇦), the country (mandatory, chosen from a list) and the YouTube link, and click Fetch data. While you type the locality, the form suggests localities that are already in the mapping file.

Adding a video to Delft, Netherlands 🇳🇱 (with no state), before fetching its data. While the locality is typed, the form suggests the matching locality already in the mapping file.

Fetch data checks whether the locality is already in the mapping file and looks up the rest. For the video: its upload date and channel from YouTube. For a new locality: its coordinates, population and gross metropolitan product (GDP), the country's population, road deaths, continent, literacy rate, average height, median age and Gini index, and the traffic index (see The secret file for the services used). The video plays next to the form, with the current second of the player, the last second of the video and a map of the locality.

The same video after fetching its data, from before Delft was in the mapping file: with no entry for Delft yet, the form adds it as a new locality. Its coordinates are shown on the map under the video; dragging the marker adjusts them.

Kyiv, Ukraine 💙💛 is in the mapping file (with 569 videos), so the form adds the video to it. This video is in the mapping file already: the form shows its existing segments in red (here from second 1 to 1408) next to the player, so a new segment is added after them or the existing ones can be checked.

For each segment (part of a video), add the Time of day (day or night), the Vehicle the footage is filmed from, the Start time (seconds) and the End time (seconds) (larger than the start), and click Submit. A video can have several segments, each starting at or after the end of the previous one; the values of the video (upload date, channel) and of the locality are kept, so for each further segment only its start, end, time of day and vehicle need to be filled in. All values can be corrected in the form before submitting, and by hand in the mapping file later.

Shortcuts and click events

The form accepts the following shortcuts (when the cursor is not in a text field; they also work with non-Latin keyboard layouts):

A: the current second of the video into "Start time (seconds)".
S: the current second of the video into "End time (seconds)".
D: the whole video: "Start time (seconds)" 0 and "End time (seconds)" the last second of the video. Clicking on "Last second" under the video does the same (and copies it).
Q: "Day" for "Time of day".
W: "Night" for "Time of day".

Proposing segments from YouTube channels

Instead of finding segments by hand in add_video.py, whole YouTube channels can be processed automatically: propose_segments.py downloads each video of a channel at low resolution, proposes segments, localities, time of day and vehicle type, and a curator reviews the proposals on a web page and applies the approved ones to the mapping file. Nothing is written to the mapping before a curator applies it.

1. Channels to process. run_channels.py takes channels from a Google Sheet (the sheet URL is set in run_channels.py): column A is the channel URL, column B its status and column C its country. It works from the bottom of the sheet up, through channels with an empty status, and proposes segments for each in chunks of videos (--chunk, default 15), each chunk in a new process, so changes to the code apply from the next chunk:

python run_channels.py

It runs until stopped (--max-minutes N stops after N minutes) and continues where it stopped when restarted. It cannot write to the sheet: it logs lines starting with SHEET: (set row N to Processing, Processed or Rejected) for the curator to copy into the sheet. Without a country in column C, it uses the channel's country on YouTube. A single channel can also be processed directly:

python propose_segments.py --channel https://www.youtube.com/@CHANNEL --country "Netherlands" --limit 20

The country must be written as in the add_video.py form. --single-city treats all of a channel's videos as one city (no drives from A to B), and --no-download proposes whole videos from YouTube metadata only. Downloading needs yt-dlp, ffmpeg and node; when YouTube asks to sign in, put a cookies.txt exported from your browser in the root of the project. The youtube_api_key in the secret file is needed to list channels (see The secret file).

2. What is proposed automatically. Videos are left out from their metadata when they are shorter than 5 minutes, live, portrait, or walking tours, time-lapses, compilations, crash videos, highway drives or road trips. For the others, a 160x90 copy is analysed at 2 frames per second:

segments: stationary stretches at the start and end are trimmed and stops longer than 3 minutes are cut out; hard cuts (edits), blank frames and title cards end a segment; footage with the camera facing the driver, not facing forward or shaking, and rural or highway stretches (from YOLO detections) are left out; segments shorter than 30 seconds are dropped. Whole videos are left out for picture-in-picture or skipped footage, and a channel where ten videos in a row have skips is rejected;
time of day: night when the sky or the street is dark for at least a minute, with a hint at dusk;
vehicle: the most common vehicle type of the channel's videos already in the mapping (a car, with a note, for a new channel);
locality: from the title, then the description, then the chapters (a video with chapters for several places is split), and otherwise a named place looked up on OpenStreetMap; drives "from A to B" are split into the towns along the route; a town within 30 km of a city with over a million people counts as that city unless it has 250,000 people itself;
re-uploads of a video already in the mapping are recognised and left out.

The proposals of a channel are kept in _output/proposals/<channel>/plan.json, with frames and signals of each video. Videos left out are listed on the review page with the reason:

Videos of the same channel as the Kyiv example of add_video.py, left out automatically: a video under 5 minutes, a walk, and a drive that skips footage (an edit cut).

3. Reviewing. Run python review_hub.py and open http://127.0.0.1:8770: it lists the channels with videos to review, and clicking one opens its review page (also available directly with python propose_segments.py --review _output/proposals/<channel>/plan.json).

The review hub: channels with videos still to decide. For each video, the page shows the player, the title and description, a contact sheet of frames, a route map, and the proposed segments with the reasons for each cut. Per segment, the curator can change the start and end, switch day and night, approve, reject, split it at a time, move it to another locality, or add a new segment; per video, change the vehicle, locality, state and country (with a map for a new locality), approve the rest of the video or reject it. Shortcuts: A sets the start and S the end of a segment to the current time of the player, D takes the whole video, Q and W set day and night, and F puts the current time in the split box.

The proposal for the Kyiv video used in the add_video.py example above: one segment from 0:01 to 23:30 in Kyiv, Ukraine (the locality guessed from the title, the vehicle from the channel's other videos in the mapping), with a contact sheet of frames and the video's description; it matches the segment added by hand (seconds 1 to 1408).

4. Applying. Apply adds the approved segments to the mapping file through the same code as the add_video.py form: the video, its segments, time of day, vehicle type, upload date and channel, and for a new locality a new row with its coordinates, population and other values. Applied segments are marked in plan.json and cannot be changed on the page any more. Overlapping segments are refused, and writes to the mapping file are locked, so several review pages and a running channel batch can be used at once. Do not stop a review page while it is applying.

5. Merging towns into cities. python review_merges.py serves a page at http://127.0.0.1:8772 with towns of under 250,000 people within 30 km of a city of over a million in the same state, side by side on maps: Y merges a town into the city, N keeps it separate. python review_merges.py --apply merges the accepted towns: their videos move to the city and their name is added to the city's other names.

Weekly update of video metadata

mapping_metadata.csv holds the YouTube metadata of every video in the mapping file (title, upload date, channel, views, description, chapters), and crowd_index.jsonl is the same dataset as JSON Lines, one object per segment, built from mapping.csv and mapping_metadata.csv without the free-text fields (titles, descriptions, chapters). Both are refreshed every Monday at 02:00 (Amsterdam time) by weekly_metadata.sh, run by cron on a Mac mini at TU/e in a clone used only by this job:

it resets the clone to the latest main on GitHub;
python -m release.update_info fetches the metadata of videos added since the last run and retries the videos with missing values (offline videos keep being retried and are never removed);
python -m release.make_crowd_jsonl rebuilds crowd_index.jsonl;
python -m release.split_large_files split splits a file over 99 MB into NAME_part1.EXT, NAME_part2.EXT, ... (each under 99 MB; CSV parts repeat the header), as GitHub refuses files over 100 MB;
the two files (or their parts) are committed as "Weekly update of video metadata" and pushed. A file over 99 MB is never pushed: the job stops instead. When someone pushed in the meantime, the commit is replayed on top of theirs.

When the files are split, the whole file is not in the repository. release/update_info.py, release/make_crowd_jsonl.py and release/validation_release.py rebuild it from its parts when needed, or run python -m release.split_large_files join mapping_metadata.csv crowd_index.jsonl. The log of the job is in ~/repos/crowd-metadata.log on the mini. To run it elsewhere, clone the repository with push access and add 0 2 * * 1 /path/to/clone/weekly_metadata.sh >> /path/to/crowd-metadata.log 2>&1 to crontab -e (set PY to the Python of the environment if it is not ~/micromamba/envs/crowd/bin/python).

Who added the videos

Videos are added by several people, each in their own copy of the mapping file: mapping.csv is Pavlo's, mapping-olena.csv Olena's, mapping-shadab.csv and mapping-epfl.csv Shadab's and, in general, mapping-NAME.csv NAME's. Videos added to mapping.csv in commits from a contributor's own account (currently Shadab's and Faye's) are credited to that contributor. Each video is credited to the file it first appeared in. The credits are kept in analysis_data/video_contributors.csv: one row per video with the contributor, the time of the commit that added it (UTC) and the mapping file it first appeared in. It was built from the git history with python -m utils.analytics.contributors, and analysis.py (also in the GitHub Action) adds new videos with the time of the last commit to their mapping file. It is the only record of who added what once the history of the mapping files is gone, so keep it when rewriting the repository's history. A video that appeared in mapping.csv and another file at the same time is credited to mapping.csv, as the other files are refreshed with copies of it. The table counts the videos in the dataset now.

#	Contributor	Videos	Footage (h)	Localities	Countries	First added	Last added
1	Pavlo	71,032	31,848.6	7,491	232	2025-01-19	2026-10-17
2	Olena	4,718	1,727.6	943	90	2025-05-03	2026-10-13
3	Shadab	2,818	1,447.9	499	96	2023-12-17	2026-07-22
4	Margarida	54	25.0	34	4	2025-05-02	2025-06-05
5	Aloysia	38	18.3	17	2	2025-04-29	2025-07-08
6	Faye	42	14.8	29	11	2026-06-30	2026-07-12

Footage in the dataset over time, by who added it (cumulative hours of the videos in the dataset now, by the date they were added).

Contact

If you have any questions or suggestions, feel free to reach out to Md Shadab Alam (md_shadab_alam@outlook.com) or Pavlo Bazilinskyy (p.bazilinskyy@tue.nl).

Licence

Code: MIT License
CROWD dataset: Creative Commons Attribution 4.0 International (CC BY 4.0)

The dataset licence covers CROWD generated structured data, annotations, and derived computer vision outputs. It does not cover the underlying YouTube videos or other third party content.

CROWD does not redistribute the original YouTube videos, extracted frames, or images. Source videos remain subject to the rights of their respective owners and YouTube's Terms of Service.

Name		Name	Last commit message	Last commit date
.github		.github
analysis_data		analysis_data
archive		archive
dashboards		dashboards
figures		figures
logs		logs
readme		readme
release		release
screenshots		screenshots
templates		templates
tests		tests
tools		tools
utils		utils
.gitignore		.gitignore
.jscpd.json		.jscpd.json
.python-version		.python-version
CITATION.cff		CITATION.cff
DATASHEET.md		DATASHEET.md
DATA_LICENSE.md		DATA_LICENSE.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
__init__.py		__init__.py
add_video.py		add_video.py
analysis.py		analysis.py
bbox_custom_tracker.yaml		bbox_custom_tracker.yaml
common.py		common.py
count_detections.py		count_detections.py
crowd_index.jsonl		crowd_index.jsonl
custom_logger.py		custom_logger.py
cut_times.csv		cut_times.csv
default.config		default.config
default.secret		default.secret
helper_script.py		helper_script.py
logmod.py		logmod.py
main.py		main.py
mapping-olena.csv		mapping-olena.csv
mapping.csv		mapping.csv
mapping_metadata.csv		mapping_metadata.csv
mapping_remaining.csv		mapping_remaining.csv
propose_segments.py		propose_segments.py
pyproject.toml		pyproject.toml
refresh_queue.py		refresh_queue.py
review_hub.py		review_hub.py
review_lessons.py		review_lessons.py
review_merges.py		review_merges.py
run_channels.py		run_channels.py
sync_workers.py		sync_workers.py
uv.lock		uv.lock

Latest commit

History

Folders and files

Repository files navigation

City Road Observations With Dashcams (CROWD)

Dataset overview

Citation and usage of code

Repository layout

Getting started

Step 1: Install uv

Step 2: Fix permissions if needed

macOS / Linux

Windows PowerShell

Step 3: Verify uv

Step 4: Clone the repository

Step 5: Install Python 3.12.13

Step 6: Create the virtual environment

Step 7: Activate the virtual environment

Step 8: Install the dependencies

Step 9: Verify the PyTorch backend

Step 10: Create config and secret

Step 11: Run the scripts

Configuration of project

The secret file

Getting a YouTube Data API key

Getting a LocationIQ key (optional)

Getting a GeoNames username

Getting a BEA API key

Getting a TomTom API key

Getting a REST Countries API key

Email settings

Getting a CARTO key (optional)

Example of YOLO output for a video with dashcam footage

Selection procedure

Segments of videos were not selected if frames are skipped

Snippets of videos are not analysed during the movement of camera

Videos are excluded from analysis if the camera is unstable or shaking

Snippets of videos captured in parking areas were excluded from analysis

Videos are excluded from analysis if another video is a part of main video

Verification of segments

Description and analysis of dataset

Description of dataset

Detections in the footage (YOLO)

Tables

Figures

Adding videos to dataset

Shortcuts and click events

Proposing segments from YouTube channels

Weekly update of video metadata

Who added the videos

Contact

Licence

About

Topics

Resources

Security policy

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

Step 1: Install `uv`

Step 3: Verify `uv`

Step 10: Create `config` and `secret`

The `secret` file