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Computer Vision Data | 3D Maps | 125k Global Locations | 60 Mln Images | SLAM | COLMAP | Depth product image in hero

Computer Vision Data | 3D Maps | 125k Global Locations | 60 Mln Images | SLAM | COLMAP | Depth

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Data Samples
#
uuid
dataset_uuid
sequence_name
sequence_number
device_os
device_name
geo_country_gps
geo_city_gps
geo_latitude_gps
geo_longitude_gps
geo_accuracy_gps
h3_12_gps
timestamp
timestamp_gps_timezone
orientation_rot_w
orientation_rot_x
orientation_rot_y
orientation_rot_z
position_pos_x
position_pos_y
position_pos_z
sfm_rot_w
sfm_rot_x
sfm_rot_y
sfm_rot_z
sfm_pos_x
sfm_pos_y
sfm_pos_z
xr_camera_intrinsics_focal_length_x
xr_camera_intrinsics_focal_length_y
xr_camera_intrinsics_resolution_x
xr_camera_intrinsics_resolution_y
xr_camera_intrinsics_principal_point_x
xr_camera_intrinsics_principal_point_y
compass_heading
compass_accuracy
compass_magnetic
satellite_view
satellite_used
speed_meters_per_second
input_acceleration_x
input_acceleration_y
input_acceleration_z
gyro_user_acceleration_x
gyro_user_acceleration_y
gyro_user_acceleration_z
ar_foundation_camera_stats_main_ar_foundation_current_speed
ar_foundation_camera_stats_main_ar_foundation_current_angle_speed
ar_foundation_camera_stats_main_ar_foundation_time_range_average
ar_foundation_camera_stats_main_ar_foundation_in_time_range_average_speed
ar_foundation_camera_stats_main_ar_foundation_in_time_range_average_angle_speed
ar_foundation_light_manager_main_last_intensity
light_estimation_data_average_brightness
light_estimation_data_average_color_temperature
light_estimation_data_color_correction_value_r
light_estimation_data_color_correction_value_g
light_estimation_data_color_correction_value_b
light_estimation_data_average_intensity_in_lumens
light_estimation_data_main_light_intensity_lumens
light_estimation_data_average_main_light_brightness
last_frame_event_args_exposure_duration
last_frame_event_args_exposure_offset
application_version
application_build_guid
exif_aperture_value
exif_brightness
exif_exposure_time
exif_shutter_speed
exif_exposure_bias
exif_f_number
exif_focal_length
exif_flash
exif_color_space
exif_photographic_sensitivity
exif_metering_mode
depth_camera_configured_depth_sensor_support
depth_last_rgb_width
depth_last_rgb_height
depth_last_depth_width
depth_last_depth_height
depth_last_depth_confidence_width
depth_last_depth_confidence_height
depth_last_depth_raw_width
depth_last_depth_raw_height
depth_last_depth_smoothed_width
depth_last_depth_smoothed_height
depth_last_human_depth_width
depth_last_human_depth_height
depth_last_human_stencil_width
depth_last_human_stencil_height
dataset_images_number
dataset_images_rgb_byte_size
dataset_images_depth_byte_size
dataset_timestamp_first_image
dataset_timestamp_last_image
dataset_h3_12_declared
dataset_h3_12_gps_avg
dataset_h3_12_gps_first_image
dataset_h3_12_gps_last_image
dataset_geo_country_gps_avg
dataset_geo_city_gps_avg
dataset_geo_latitude_gps_avg
dataset_geo_longitude_gps_avg
dataset_geo_accuracy_gps_avg
dataset_geo_accuracy_gps_median
1 xxxxxxxxxx Xxxxxxxxx xxxxxx xxxxxxxxxx Xxxxx Xxxxxx Xxxxxxxxxx Xxxxxx Xxxxxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxxxxxx xxxxxxxxx Xxxxxxx xxxxxx Xxxxx xxxxxxxxxx xxxxxx Xxxxxxxxxx xxxxxx Xxxxx Xxxxxx xxxxx xxxxxxxx xxxxxxx Xxxxx Xxxxxxxx xxxxxxxxxx xxxxxx Xxxxxxxxx xxxxxx Xxxxxxxxx Xxxxxxxxx xxxxxxxxxx Xxxxxx Xxxxx xxxxxx xxxxxxx xxxxxxx Xxxxx xxxxxx Xxxxxxxxxx xxxxxxxx xxxxxx Xxxxx Xxxxxxx xxxxxx Xxxxxxxx Xxxxxxx Xxxxx xxxxxx xxxxxxxxxx Xxxxx xxxxxxxxxx xxxxxxxxx Xxxxxxx xxxxxxxx xxxxxxxx Xxxxxxxxxx Xxxxxxxx Xxxxxxxx xxxxxxxxx Xxxxxxxxxx Xxxxxx Xxxxxxxxx xxxxx xxxxxxx xxxxxxxxx Xxxxxx Xxxxxxx Xxxxxxxxx xxxxxxxxx xxxxxxxxx Xxxxx xxxxxxxx Xxxxxxx xxxxxxxxx Xxxxxxx xxxxx Xxxxxxx xxxxxxx Xxxxx xxxxxxxxxx Xxxxxxx Xxxxx xxxxxxxxxx Xxxxxx xxxxxx Xxxxxxxxx xxxxx Xxxxxxxxxx xxxxxx xxxxx xxxxxxxx Xxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxxxxxxx xxxxxxxx xxxxx Xxxxxx xxxxxxxxxx xxxxxxxxx xxxxx xxxxx
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... xxxxxxxx Xxxxxxxxx xxxxx Xxxxxxx xxxxxxx xxxxx Xxxxx Xxxxxxxxx Xxxxxxxx xxxxxxxxx xxxxxxxxxx Xxxxxxxxx Xxxxxxxxxx Xxxxxxxxx Xxxxxxxxxx Xxxxxxxx xxxxxxxxx Xxxxxxxxxx xxxxxxxx xxxxxxxx Xxxxx Xxxxxx xxxxxxxxx xxxxxxxx Xxxxxxx xxxxxx xxxxxx Xxxxxx Xxxxxxxxxx Xxxxxxxxxx Xxxxxxx Xxxxxx xxxxxxxx Xxxxx xxxxxxx Xxxxxxxx Xxxxxxxxxx xxxxxxx Xxxxxx Xxxxxx xxxxx xxxxxxxx Xxxxxxxxxx xxxxxxxxx xxxxxxxxxx xxxxx xxxxxxxx Xxxxxxxx Xxxxx Xxxxxxxxx Xxxxxx Xxxxxxxxxx Xxxxxxxxx Xxxxxxxxx xxxxxxxx Xxxxx xxxxxxxxxx Xxxxxxx xxxxxxxx xxxxx xxxxxxxxx xxxxxxxx Xxxxxx xxxxxxx xxxxxxx Xxxxxxxx Xxxxx xxxxxxx Xxxxxxxxxx xxxxxxx xxxxxxxxx xxxxx xxxxxxxxx xxxxxx Xxxxxxxxx Xxxxxxxxxx Xxxxxxxx xxxxxx Xxxxxxxxxx xxxxx Xxxxxxxxxx xxxxxxx xxxxx Xxxxxxxx Xxxxxx Xxxxxxx Xxxxxx xxxxx Xxxxxxxxx xxxxxx Xxxxxxxxxx Xxxxxxxx xxxxxxx xxxxx xxxxxx Xxxxxxxx Xxxxxxx xxxxx Xxxxxx Xxxxxx Xxxxxx Xxxxxxxxx xxxxxxxxxx xxxxxxxxx Xxxxx
Sign In To Preview Data
Volume
57.5M
Images and Metadata
Avail. Formats
.bin, .json, and .csv
File
Coverage
250
Countries
History
24
months

Data Dictionary

[Sample] 93921bef-79a3-415b-b044-b687dd429140.csv
Attribute Type Example Mapping
uuid
String 80bb696a-3b44-47ff-acbf-b4c180a96ebb_1_0000
dataset_uuid
String 80bb696a-3b44-47ff-acbf-b4c180a96ebb
sequence_name
Integer 10000
sequence_number
Integer 0
device_os
String ios
device_name
String iPhone15,4
geo_country_gps
String Italy
geo_city_gps
String Udine
geo_latitude_gps
Float 46.06332
geo_longitude_gps
Float 13.2356424
geo_accuracy_gps
Float 27.0
h3_12_gps
Integer 631043095217792511
timestamp
String 2025-05-21 07:36:50 UTC
timestamp_gps_timezone
String Europe - Athens
orientation_rot_w
Float 0.2876765
orientation_rot_x
Float 0.05723609
orientation_rot_y
Float -0.9446321
orientation_rot_z
Float 0.1470938
position_pos_x
Float 2.007549
position_pos_y
Float 1.190128
position_pos_z
Float 2.880159
sfm_rot_w
Float
sfm_rot_x
Float
sfm_rot_y
Float
sfm_rot_z
Float
sfm_pos_x
Float
sfm_pos_y
Float
sfm_pos_z
Float
xr_camera_intrinsics_focal_length_x
Float 2737.746
xr_camera_intrinsics_focal_length_y
Float 2737.746
xr_camera_intrinsics_resolution_x
Integer 3840
xr_camera_intrinsics_resolution_y
Integer 2160
xr_camera_intrinsics_principal_point_x
Float 1912.398
xr_camera_intrinsics_principal_point_y
Float 1086.938
compass_heading
Float 200.7714
compass_accuracy
Float 11.27206
compass_magnetic
Float 193.0363
satellite_view
Integer
satellite_used
Integer
speed_meters_per_second
Float
input_acceleration_x
Float -0.03260803
input_acceleration_y
Float -0.9468536
input_acceleration_z
Float -0.3839569
gyro_user_acceleration_x
Float -0.05825843
gyro_user_acceleration_y
Float 0.006068945
gyro_user_acceleration_z
Float -0.08283016
ar_foundation_camera_stats_main_ar_foundation_current_speed
Float 0.4792112
ar_foundation_camera_stats_main_ar_foundation_current_angle_speed
Float 8.661532
ar_foundation_camera_stats_main_ar_foundation_time_range_average
Float 1.0
ar_foundation_camera_stats_main_ar_foundation_in_time_range_average_speed
Float 0.4002476
ar_foundation_camera_stats_main_ar_foundation_in_time_range_average_angle_speed
Float 13.75465
ar_foundation_light_manager_main_last_intensity
Float 1.160006
light_estimation_data_average_brightness
Float 0.5405627
light_estimation_data_average_color_temperature
Float 5784.545
light_estimation_data_color_correction_value_r
Float 0.0
light_estimation_data_color_correction_value_g
Float 0.0
light_estimation_data_color_correction_value_b
Float 0.0
light_estimation_data_average_intensity_in_lumens
Float 1081.125
light_estimation_data_main_light_intensity_lumens
Float 0.0
light_estimation_data_average_main_light_brightness
Float 0.0
last_frame_event_args_exposure_duration
Float 0.0166666666666667
last_frame_event_args_exposure_offset
Float 0.0937782
application_version
String v1.6.9
application_build_guid
String b8646ddd53d5c457187e7afa403b9213e
exif_aperture_value
Float 1.35614381022528
exif_brightness
Float 4.23416727686798
exif_exposure_time
Float 0.0166666666666667
exif_shutter_speed
Float 5.90798746070115
exif_exposure_bias
Float 0.0
exif_f_number
Float 1.6
exif_focal_length
Float 5.96
exif_flash
Boolean f
exif_color_space
String sRGB
exif_photographic_sensitivity
Integer 40
exif_metering_mode
String Pattern
depth_camera_configured_depth_sensor_support
Boolean t
depth_last_rgb_width
Integer 3840
depth_last_rgb_height
Integer 2160
depth_last_depth_width
String
depth_last_depth_height
String
depth_last_depth_confidence_width
String
depth_last_depth_confidence_height
String
depth_last_depth_raw_width
String
depth_last_depth_raw_height
String
depth_last_depth_smoothed_width
String
depth_last_depth_smoothed_height
String
depth_last_human_depth_width
Integer 256
depth_last_human_depth_height
Integer 144
depth_last_human_stencil_width
Integer 256
depth_last_human_stencil_height
Integer 144
dataset_images_number
Integer 1200
dataset_images_rgb_byte_size
Integer 3237532078
dataset_images_depth_byte_size
Integer 1835095
dataset_timestamp_first_image
String 2025-05-21 07:36:50 UTC
dataset_timestamp_last_image
String 2025-05-21 07:42:19 UTC
dataset_h3_12_declared
Integer 631043095256282111
dataset_h3_12_gps_avg
Integer 631043095256282111
dataset_h3_12_gps_first_image
Integer 631043095217792511
dataset_h3_12_gps_last_image
Integer 631043095256281599
dataset_geo_country_gps_avg
String Italy
dataset_geo_city_gps_avg
String Udine
dataset_geo_latitude_gps_avg
Float 46.063405431916664
dataset_geo_longitude_gps_avg
Float 13.235469743
dataset_geo_accuracy_gps_avg
Float 25.671666666666667
dataset_geo_accuracy_gps_median
Float 24.0

Description

Comprehensive global 3D Maps dataset with 58 Mln smartphone-captured images including depth, poses, and Exif metadata, across 125 K diverse locations. Ideal for Geospatial and Vision AI Models Training.
Our dataset delivers unprecedented scale and diversity for geospatial AI training: - Massive scale: 125,000 unique 3D map sequences and locations, 57,500,000 images, 35 TB of Data, orders of magnitude larger than datasets currently used for SOTA Vision/Spatial Models - Constantly growing dataset: 12k new 3D Map sequences and locations monthly. - Full-frame, high-res captures: OVER retains full-resolution, dynamic aspect-ratio images with complete Exif metadata (GPS, timestamp, device orientation), multiple resolutions 1920x1080 - 3840x2880, pre-computed COLMAP poses. - Global diversity: Environments span urban, suburban, rural, and natural settings across 120+ countries, capturing architectural, infrastructural, and environmental variety. - Rich metadata: Per-image geolocation (±3 m accuracy), timestamps, device pose, COLMAP pose; per-map calibration data (camera intrinsics/extrinsics). - Ideal for: Spatial Models Training, Multi-view stereo & NeRF/3DGS training, semantic segmentation, novel view synthesis, 3D object detection, geolocation, urban planning, AR/VR, autonomous navigation.

Country Coverage

Africa (58)
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cabo Verde
Cameroon
Central African Republic
Chad
Comoros
Congo
Congo (Democratic Republic of the)
Côte d'Ivoire
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mayotte
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Réunion
Saint Helena, Ascension and Tristan da Cunha
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Swaziland
Tanzania, United Republic of
Togo
Tunisia
Uganda
Western Sahara
Zambia
Zimbabwe
Asia (51)
Afghanistan
Armenia
Azerbaijan
Bahrain
Bangladesh
Bhutan
Brunei Darussalam
Cambodia
China
Cyprus
Georgia
Hong Kong
India
Indonesia
Iran (Islamic Republic of)
Iraq
Israel
Japan
Jordan
Kazakhstan
Korea (Democratic People's Republic of)
Korea (Republic of)
Kuwait
Kyrgyzstan
Lao People's Democratic Republic
Lebanon
Macao
Malaysia
Maldives
Mongolia
Myanmar
Nepal
Oman
Pakistan
Palestine, State of
Philippines
Qatar
Saudi Arabia
Singapore
Sri Lanka
Syrian Arab Republic
Taiwan
Tajikistan
Thailand
Timor-Leste
Turkey
Turkmenistan
United Arab Emirates
Uzbekistan
Vietnam
Yemen
Europe (52)
Albania
Andorra
Austria
Belarus
Belgium
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
Denmark
Estonia
Faroe Islands
Finland
France
Germany
Gibraltar
Greece
Guernsey
Holy See
Hungary
Iceland
Ireland
Isle of Man
Italy
Jersey
Kosovo
Latvia
Liechtenstein
Lithuania
Luxembourg
Macedonia (the former Yugoslav Republic of)
Malta
Moldova (Republic of)
Monaco
Montenegro
Netherlands
Norway
Poland
Portugal
Romania
Russian Federation
San Marino
Serbia
Slovakia
Slovenia
Spain
Svalbard and Jan Mayen
Sweden
Switzerland
Ukraine
United Kingdom
Åland Islands
North America (13)
Belize
Bermuda
Canada
Costa Rica
El Salvador
Greenland
Guatemala
Honduras
Mexico
Nicaragua
Panama
Saint Pierre and Miquelon
United States of America
Oceania (25)
American Samoa
Australia
Cook Islands
Fiji
French Polynesia
Guam
Kiribati
Marshall Islands
Micronesia (Federated States of)
Nauru
New Caledonia
New Zealand
Niue
Norfolk Island
Northern Mariana Islands
Palau
Papua New Guinea
Pitcairn
Samoa
Solomon Islands
Tokelau
Tonga
Tuvalu
Vanuatu
Wallis and Futuna
Other (9)
Antarctica
Bouvet Island
British Indian Ocean Territory
Christmas Island
Cocos (Keeling) Islands
French Southern Territories
Heard Island and McDonald Islands
South Georgia and the South Sandwich Islands
United States Minor Outlying Islands
South America (42)
Anguilla
Antigua and Barbuda
Argentina
Aruba
Bahamas
Barbados
Bolivia (Plurinational State of)
Bonaire, Sint Eustatius and Saba
Brazil
Cayman Islands
Chile
Colombia
Cuba
Curaçao
Dominica
Dominican Republic
Ecuador
Falkland Islands (Malvinas)
French Guiana
Grenada
Guadeloupe
Guyana
Haiti
Jamaica
Martinique
Montserrat
Paraguay
Peru
Puerto Rico
Saint Barthélemy
Saint Kitts and Nevis
Saint Lucia
Saint Martin (French part)
Saint Vincent and the Grenadines
Sint Maarten (Dutch part)
Suriname
Trinidad and Tobago
Turks and Caicos Islands
Uruguay
Venezuela (Bolivarian Republic of)
Virgin Islands (British)
Virgin Islands (U.S.)

History

24 months of historical data

Volume

125,000 3D Maps and Locations
57.5 million Images and Metadata
35 TB of Raw Data

Pricing

License Starts at
One-off purchase Not available
Monthly License Not available
Yearly License Not available
Usage-based
$2.50 / Per 3D Map

Suitable Company Sizes

Small Business
Medium-sized Business
Enterprise

Delivery

Methods
SFTP
REST API
Frequency
monthly
Format
.bin
.json
.csv

Use Cases

Artificial Intelligence (AI) Deep Learning Earth Observation
Autonomous Driving
Location Visualization

Categories

Related Searches

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Frequently asked questions

What is Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth?

Comprehensive global 3D Maps dataset with 58 Mln smartphone-captured images including depth, poses, and Exif metadata, across 125 K diverse locations. Ideal for Geospatial and Vision AI Models Training.

What is Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth used for?

This product has 5 key use cases. Over The Reality recommends using the data for Artificial Intelligence (AI), Deep Learning, Earth Observation, Autonomous Driving, and Location Visualization. Global businesses and organizations buy Map Data from Over The Reality to fuel their analytics and enrichment.

Who can use Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth?

This product is best suited if you’re a Enterprise looking for Map Data. Get in touch with Over The Reality to see what their data can do for your business and find out which integrations they provide.

How far back does the data in Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth go?

This product has 24 months of historical coverage. It can be delivered on a monthly basis.

Which countries does Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth cover?

This product includes data covering 250 countries like USA, China, Japan, Germany, and India. Over The Reality is headquartered in Italy.

How much does Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth cost?

Pricing for Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth starts at USD2.50 per Per 3D Map. Connect with Over The Reality to get a quote and arrange custom pricing models based on your data requirements.

How can I get Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth?

Businesses can buy Map Data from Over The Reality and get the data via SFTP and REST API. Depending on your data requirements and subscription budget, Over The Reality can deliver this product in .bin, .json, and .csv format.

What is the data quality of Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth?

You can compare and assess the data quality of Over The Reality using Datarade’s data marketplace.

What are similar products to Computer Vision Data 3D Maps 125k Global Locations 60 Mln Images SLAM COLMAP Depth?

This product has 3 related products. These alternatives include Global Geospatial & GIS Data 230M+ POIs with Location Coordinates, Mapping Metadata & 5000 Categories, Mapping Data 164M+ Geocoded Places Worldwide, and Geospatial Data: Places Data Global Location Data on 56M+ Places. You can compare the best Map Data providers and products via Datarade’s data marketplace and get the right data for your use case.

Starts at
$2.50 / Per 3D Map
License Starts at
One-off purchase Not available
Monthly License Not available
Yearly License Not available
Usage-based
$2.50 / Per 3D Map