This data is freely available for download and use and contains
- 54,484,737 computer generated roads in all US states except Alaska.
- 5,931,242 computer generated roads in all US states except Alaska that are missing in OpenStreetMaps roads drop from 02-May-2020
This data is licensed by Microsoft under the Open Data Commons Open Database License (ODbL)
- 54,484,737 computer generated roads in all US states except Alaska.
- 5,931,242 computer generated roads in all US states except Alaska that are missing in OpenStreetMaps (OSM) roads drop from 02-May-2020
GeoJSON is a format for encoding a variety of geographic data structures. For Intensive Documentation and Tutorials, Refer to GeoJson Blog
The road extraction is done in four stages (first dataset went through two stages and second went through all four):
- Semantic Segmentation – Recognizing road pixels on the aerial image using Convolutional Neural Network (CNN).
- Geometry Generation - A series of algorithms and processes transforming output of semantic segmentation into roads in geometry format.
- Conflation & Cutting - Excluding roads and parts of roads that already exist in the road network (OSM).
- Classification - A classifier to filter out low-confidence roads and predict a road type.
Our network was based on UNet and ResNet and the following papers [U-Net] (https://arxiv.org/abs/1505.04597), [Res U-Net] (https://arxiv.org/pdf/1512.03385.pdf), [Res U-Net] (https://arxiv.org/pdf/1711.10684.pdf). The model was trained on 512x512 images, it is fully-convolutional, meaning that the model can be applied to an image of any size (constrained by GPU memory, 1088x1088 in our case).
The training set consists of 16800 labeled images. Majority of the satellite images cover diverse residential areas in the US. For the sake of good set representation, we have enriched the set with samples from various areas covering mountains, glaciers, forests, deserts, beaches, coasts, etc. Images in the set are of 1024x1024 pixel size with 1 meter/pixel resolution. The training is done with Keras toolkit.
These are the intermediate stage metrics we use to track CNN model improvements and they are pixel based.
Pixel precision/recall = 83.06%/80.74%
Geometry generation consists of the following steps
- Image postprocessing
- Thinning
- Connectivity improvement
- Graph construction
- Finalizing road shapes and network quality
- Stiching road geojsons between neighboring images where needed
We use APLS metric to evaluate connectivity. It is measured over images with scale 200x200 meters.
APLS precision/recall = 77.61%/71.52%
The vintage of the roads depends on the vintage of the underlying imagery. Because Bing Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data.
The Osm Missing Data went through a final classifier to ensure that the precision is at least 90%. Here is another measurement with human OSM editors before the final classifier:
Label | % |
---|---|
Roads added without editing | 77% |
Roads added with minor editing | 18% |
Incorrect roads | 5% |
Yes, we are working on adding more countries. Next targets are South America and Europe
Microsoft has a continued interest in supporting a thriving OpenStreetMap ecosystem.
This dataset was shared with Facebook, owner or RapID - a tool for adding mined roads to OSM.
Full drop of all USA mined roads | OSM missing roads (USA 02-May-2020) | ||||||
---|---|---|---|---|---|---|---|
State | Number of Roads | Length km | Unzipped MB | State | Number of Roads | Length km | Unzipped MB |
USA | 54484737 | 9308940 | 13459 | USA | 5931242 | 817761 | 2924 |
Alabama | 1030015 | 196256 | 268 | Alabama | 169926 | 22311 | 83 |
Arizona | 1180273 | 160617 | 263 | Arizona | 66934 | 10377 | 32 |
Arkansas | 767321 | 174286 | 208 | Arkansas | 154038 | 26759 | 76 |
California | 4009971 | 470500 | 889 | California | 252553 | 29645 | 121 |
Colorado | 1082238 | 181699 | 268 | Colorado | 76741 | 11584 | 38 |
Connecticut | 427923 | 51203 | 103 | Connecticut | 38427 | 3154 | 18 |
Delaware | 141175 | 17592 | 32 | Delaware | 10225 | 969 | 4 |
Florida | 2712701 | 346693 | 577 | Florida | 147038 | 19932 | 69 |
Georgia | 1749917 | 283913 | 437 | Georgia | 191975 | 24302 | 94 |
Hawaii | 137705 | 14953 | 29 | Hawaii | 13962 | 1334 | 6 |
Idaho | 562235 | 114038 | 162 | Idaho | 73548 | 11416 | 37 |
Illinois | 1840754 | 309918 | 407 | Illinois | 174399 | 21305 | 83 |
Indiana | 1206595 | 213000 | 276 | Indiana | 146195 | 16725 | 69 |
Iowa | 820387 | 222015 | 203 | Iowa | 94436 | 11551 | 44 |
Kansas | 885228 | 253328 | 219 | Kansas | 93413 | 13423 | 43 |
Kentucky | 965199 | 171887 | 269 | Kentucky | 190964 | 24101 | 96 |
Louisiana | 761323 | 150911 | 187 | Louisiana | 127622 | 20813 | 61 |
Maine | 464244 | 89907 | 140 | Maine | 80845 | 12431 | 41 |
Maryland | 800548 | 88361 | 183 | Maryland | 39237 | 3399 | 18 |
Massachusetts | 758191 | 83294 | 178 | Massachusetts | 34552 | 2758 | 16 |
Michigan | 1712266 | 279825 | 404 | Michigan | 177966 | 19051 | 85 |
Minnesota | 1393249 | 296632 | 355 | Minnesota | 191433 | 26747 | 94 |
Mississippi | 671114 | 156392 | 185 | Mississippi | 134654 | 22403 | 66 |
Missouri | 1300357 | 281199 | 351 | Missouri | 153014 | 21355 | 76 |
Montana | 860457 | 180023 | 249 | Montana | 130533 | 24636 | 68 |
Nebraska | 663577 | 187192 | 171 | Nebraska | 47586 | 7036 | 23 |
Nevada | 463933 | 70773 | 107 | Nevada | 28404 | 4568 | 13 |
NewHampshire | 243910 | 38689 | 68 | NewHampshire | 21741 | 2122 | 10 |
NewJersey | 942622 | 98892 | 210 | NewJersey | 65934 | 5681 | 31 |
NewMexico | 680561 | 127638 | 168 | NewMexico | 60150 | 10509 | 29 |
NewYork | 1584215 | 251629 | 404 | NewYork | 177720 | 18849 | 87 |
NorthCarolina | 1769687 | 288948 | 461 | NorthCarolina | 230270 | 28538 | 113 |
NorthDakota | 581985 | 164082 | 154 | NorthDakota | 33947 | 4780 | 16 |
Ohio | 1795292 | 277164 | 420 | Ohio | 193883 | 21635 | 94 |
Oklahoma | 1014493 | 239617 | 250 | Oklahoma | 129390 | 21496 | 63 |
Oregon | 1111082 | 173778 | 302 | Oregon | 129581 | 20241 | 67 |
Pennsylvania | 1922526 | 286625 | 492 | Pennsylvania | 223364 | 27456 | 111 |
RhodeIsland | 126379 | 12978 | 28 | RhodeIsland | 7863 | 610 | 3 |
SouthCarolina | 884137 | 153987 | 228 | SouthCarolina | 102010 | 13842 | 50 |
SouthDakota | 530158 | 151645 | 140 | SouthDakota | 69851 | 11593 | 34 |
Tennessee | 1231633 | 212032 | 333 | Tennessee | 240815 | 34606 | 122 |
Texas | 4901657 | 831644 | 1146 | Texas | 521434 | 88391 | 253 |
Utah | 549837 | 86304 | 133 | Utah | 45061 | 7241 | 22 |
Vermont | 164249 | 32170 | 50 | Vermont | 30386 | 3527 | 15 |
Virginia | 1466999 | 208827 | 378 | Virginia | 108239 | 11788 | 54 |
Washington | 1565086 | 209696 | 390 | Washington | 158898 | 21397 | 80 |
WestVirginia | 446393 | 85916 | 140 | WestVirginia | 102378 | 15400 | 53 |
Wisconsin | 1231694 | 241375 | 309 | Wisconsin | 172143 | 20430 | 83 |
Wyoming | 371246 | 88897 | 109 | Wyoming | 65564 | 13543 | 33 |
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