All of the datasets produced for this project are STAC compliant and hosted on Radiant MLHub. The training labels and associated imagery are released under a CC BY-N.C. 4.0 license.
Ramp training data all follows the same format and consists of two corresponding parts; very high resolution satellite and drone imagery in GeoTiff format and corresponding vector labels that overlay the imagery in GeoJSON format. These two components are combined and tiled out into 256 pixel by 256 pixel “chips” to become Ramp training data.
Due to limitations of time and resource, not all datasets have undergone a thorough and extensive review through our QA/QC pipeline. As a result we devised a two-tier system making it clear which datasets were reviewed thoroughly (Tier 1, 100% eyes on) and which datasets were not reviewed as thoroughly (Tier 2, undergone some review).
Tier 1
Tile Count: 2,891
Building Count: 30,652
Imagery: 40 cm sourced from Maxar ODP 10500100271BF800
Tier 1
Tile Count: 6,288
Building Count: 51,335
Imagery: 40 cm sourced from Maxar ODP 104001002CA32300
Tier 1
Tile Count: 1,430
Building Count: 28,549
Imagery: 47 cm sourced from Maxar ODP 10300100A450A500
Tier 1
Tile Count: 3,024
Building Count: 33,531
Imagery: 40 cm sourced from Maxar ODP 10500100236CC900
Tier 1
Tile Count: 3,357
Building Count: 92,918
Imagery: 45 cm sourced from Maxar ODP 10500100195A6700
Tier 1
Tile Count: 4,681
Building Count: 60,951
Imagery: 30 cm sourced from Maxar ODP 10400100472E6700
Tier 1
Tile Count: 7,622
Building Count: 68,189
Imagery: 32 cm sourced from Maxar ODP 1040010056B6FA00
Tier 1
Tile Count: 3,667
Building Count: 44,765
Imagery: 35 cm sourced from Maxar ODP 1040010033320500
Tier 1
Tile Count: 1,789
Building Count: 22,396
Imagery: 35 cm sourced from Maxar ODP 104001004DAECE00
Tier 1
Tile Count: 1,330
Building Count: 40,786
Imagery: 30 cm originally sourced from Open Cities AI Challenge
Tier 1
Tile Count: 566
Building Count: 40,786
Imagery: 30 cm originally sourced from Open Cities AI Challenge
Tier 1
Tile Count: 3,574
Building Count: 7,118
Imagery: 30 cm originally sourced from the SpaceNet2 AI Challenge
Tier 1
Tile Count: 1,027
Building Count: 3,468
Imagery: 30 cm originally sourced from the SpaceNet2 AI Challenge
Tier 1
Tile Count: 11,905
Building Count: 189,057
Imagery: 30 cm sourced from Maxar ODP BG_Dhaka_19Q3_V0_R6C3
Tier 1
Tile Count: 3,024
Building Count: 41,248
Imagery: 40 cm sourced from Maxar ODP 105001001597B00
Tier 1
Tile Count: 7,310
Building Count: 80,050
Imagery: 35 cm sourced from Maxar ODP 104001003BA7C900
Tier 1
Tile Count: 2,375
Building Count: 26,875
Imagery: 35 cm sourced from Maxar ODP 10400100546E3700
Tier 1
Tile Count: 5,229
Building Count: 38,096
Imagery: 40 cm sourced from Maxar ODP 105001001AC9800
Tier 2
Tile Count: 16,217
Building Count: 135,375
Imagery: 30 cm sourced from Maxar ODP 2022 Bangladesh Flooding Release
Tier 2
Tile Count: 3,044
Building Count: 124,208
Imagery: 45 cm sourced from Maxar ODP 10300100AA405C00
Tier 2
Tile Count: 8,498
Building Count: 148,459
Imagery: 30 cm sourced from Maxar ODP 1040010058041300
Tier 2
Tile Count: 1,195
Building Count: 24,707
Imagery: 30 cm sourced from Maxar ODP
Creative Commons Disclosure
CC BY-NC 4.0 license
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share this document, meaning copy and redistribute the material in any medium or format. You are also free to adapt this document, meaning remix, transform, and build upon the material. You may not use this work for commercial purposes. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Suggested Citation: DevGlobal Partners. (2022). Replicable AI for Microplanning (ramp): Data Labeling Specifications. https://rampml.global/
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