Building Inspector - Shape + Address consensus

Transcript

1. mauricio giraldo arteaga @mgiraldo NYPL Labs

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3. bon jour

4. my name is mauricio

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6. research and circulating library system spanning the Bronx, Staten Island

   and Manhattan boroughs in NYC
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8. NYPL Labs

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10. i’m going to talk about maps

11. The Great Map Data Extraction

12. an adventure in three acts and a prologue and an

    epilogue

13. prologue

14. The Lionel Pincus and Princess Firyal Map Division

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21. 500,000+ maps 20,000+ books & atlases

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27. year

28. street names year

29. use type street names year

30. use type street names name year

31. material use type street names name year

32. material use type street names name class year

33. material use type street names address name class year

34. material use type street names address floors name class year

35. material use type street names address floors name class year

    skylights

36. material use type street names address floors name class year

    skylights backyards

37. material use type street names address floors name class geo

    location year skylights backyards

38. footprint material use type street names address floors name class

    geo location year skylights backyards

39. footprint material use type street names address floors name class

    geo location year skylights backyards

40. we got these for several decades since the 1800s and

    by 1950 every town in the US with a population of 2,000 had been mapped

41. data trapped in a legacy format

42. we want all the data!

43. f**k yeah historical data!

44. citysdk.waag.org/buildings

45. citysdk.waag.org/buildings

46. NYU Stern / Imaginaria3D

47. NYU Stern / Imaginaria3D

48. maps.google.com

49. maps.google.com

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51. data

52. it all starts with a photograph

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54. but it is “just a photo” but it is only

    a few clicks away
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56. maps.nypl.org/warper

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59. geo-rectification or: “make it match Open Street Map”

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62. *this is a simulation. actual process is intensive. consult your

    mathematician before trying
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65. vectorization or: “draw the building shapes”

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67. results from maps.nypl.org/warper

68. hand-crafted, artisanal, locally-sourced data

69. 500,000+ maps 20,000+ books & atlases

70. 500,000+ maps 20,000+ books & atlases* *imagine how many pages

    an atlas has

71. in the order of dozens of millions building footprints if

    counting NYC only
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73. ~120k footprints produced in three years
    by staff and volunteers

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75. this will take us a few millenia* *actual number taken

    out from a hat

76. there has to be a better way

77. act i: will there be polygons?

78. requests to geo companies went unanswered

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80. can we automate this?

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82. ¿¡quoi!? @mgiraldo

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87. what is a building?

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89. completely enclosed by black lines

90. completely enclosed by black lines dashed lines are not walls

91. completely enclosed by black lines dashed lines are not walls

    > 20m2 (~180ft2)

92. completely enclosed by black lines dashed lines are not walls

    > 20m2 (~180ft2) < 3,000m2 (~27,000ft2)

93. completely enclosed by black lines dashed lines are not walls

    > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored

94. process

95. github.com/NYPL/map-vectorizer

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100. completely enclosed by black lines dashed lines are not walls

     > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored

101. completely enclosed by black lines dashed lines are not walls

     > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored

102. provide the best (possible) input image

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107. differences in resampling cubic nearest neighbor

108. differences in resampling cubic nearest neighbor

109. make the image a binary bitmap or: “black and white”

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112. polygonize or: “convert contiguous pixels to a single line shape”

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114. ! gdal_polygonize.py test.tif -f "ESRI Shapefile" test.shp test

115. ! gdal_polygonize.py test.tif -f "ESRI Shapefile" test.shp test

116. ! gdal_polygonize.py test.tif -f "ESRI Shapefile" test.shp test

117. ! gdal_polygonize.py test.tif -f "ESRI Shapefile" test.shp test

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119. no no no no no

120. no no no no no yes yes

121. simplify* *for those polygons that we care about

122. completely enclosed by black lines dashed lines are not walls

     > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored ✔ ✔
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125. alpha shape *code basically stolen wholesale from rpubs.com/geospacedman/alphasimple

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129. we need a set of points

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131. pts = spsample(polygon, n=1000, type="hexagonal")

132. pts = spsample(polygon, n=1000, type="regular")

133. pts = spsample(polygon, n=1000, type="random")

134. now we alpha shaping

135. x.as = ashape(pts@coords, alpha=2.0)

136. x.as = ashape(pts@coords, alpha=2.0)

137. x.as = ashape(pts@coords, alpha=2.0)

138. there are other point reduction algorithms like Ramer-Douglas-Peucker or Whyatt

     Curve Simplification

139. separate the buildings from the chaff

140. completely enclosed by black lines dashed lines are not walls

     > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored ✔ ✔ ✔ ✔
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143. [218, 211, 209]

144. [218, 211, 209] paper [199, 179, 173], [179, 155, 157],

     [206, 193, 189], [199, 195, 163], [207, 204, 179], [195, 189, 154], [209, 203, 181], [255, 225, 40], [194, 198, 192], [161, 175, 190], [137, 174, 163], [166, 176, 172], [149, 156, 141] [205, 200, 186] not paper
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148. this is good enough for our use case

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152. ✔ ✔ ✔ ✔ ✔ completely enclosed by black lines

     dashed lines are not walls > 20m2 (~180ft2) < 3,000m2 (~27,000ft2) not paper-colored

153. computer-vision for attribute recognition *bonus quest

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157. 66,056 footprints produced in one day for an 1859 atlas

     of Manhattan

158. caveats:
     ! adjacency not enforced
     false positives/negatives
     buildings may also

     overlap

159. act ii: the vectorizer needs to prove itself

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164. multiple inspections for each item and let consensus surface on

     its own

165. footprint validation or: “tell us what the computer got right

     or wrong“

166. are people willing to spend time checking building footprints? insurance

     atlases are not exactly the coolest type of maps
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168. buildinginspector.nypl.org

169. github.com/NYPL/building-inspector

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174. about a month later…

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179. 420k+ flags* 70k+ unique polygons ! consensus: ~84% YES, 7%

     FIX, 9% NO *a “flag” is a YES/NO/FIX by one person for a given polygon

180. seems people are willing after all… we — our contributors

181. seems people are willing after all… we — our contributors

182. act iii: the return of the inspector

183. footprint material use type street names address floors name class

     geo location year skylights backyards

184. divide and conquer

185. footprint material use type street names address floors name class

     geo location year skylights backyards

186. three new tasks for now… we really want it all!

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188. footprint material use type street names address floors name class

     geo location year skylights backyards

189. check

190. check YES

191. check YES address color

192. check YES FIX address color

193. check YES FIX address color fix

194. check YES FIX address color fix

195. check YES FIX address color fix *footprints marked as “NO”

     go to building heaven

196. check YES FIX address color fix *footprints marked as “NO”

     go to building heaven

197. fix

198. fix

199. address

200. address

201. classify color

202. classify color

203. 865k+ flags

204. check YES FIX address color fix

205. check YES FIX address color fix for 80k+ unique polygons

     77k+ 5k+ 42k+ 26k+

206. epilogue

207. address and shape consensus or: how to determine what the

     right building footprint and address looks like?
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210. all points are useful inclusiveness above all

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219. DBSCAN for the win citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1980

220. bit.ly/nypl-consensus

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230. 246 246 246 414 246 414 414 246 414 414

     414 ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ + +

231. 246 246 246 414 246 414 414 246 414 414

     414 ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ ﹡ + +

232. 246 414 + +

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235. DBSCAN for shapes also!

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242. all points are still useful

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251. + + + + + + +

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266. resulting data available via an API

267. resulting data available via an API in 100% recyclable GeoJSON

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269. photographing

270. photographing ↓

271. photographing ↓ geo-rectification

272. photographing ↓ geo-rectification ↓

273. photographing ↓ geo-rectification ↓ vectorization

274. photographing ↓ geo-rectification ↓ vectorization ↓

275. photographing ↓ geo-rectification ↓ vectorization ↓ inspection

276. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓

277. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓ check /

     fix / color / address

278. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓ check /

     fix / color / address ↓

279. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓ check /

     fix / color / address ↓ consensus

280. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓ check /

     fix / color / address ↓ consensus ↓

281. photographing ↓ geo-rectification ↓ vectorization ↓ inspection ↓ check /

     fix / color / address ↓ consensus ↓ data release

282. not the end

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286. ¡merci beaucoup! mauricio giraldo arteaga @mgiraldo NYPL Labs slides at:

     bit.ly/nypl-ehess images from: NYPL digital collections - Wikimedia Commons
 Christopher Cannon - Flickr user wallyg - Giphy