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cwerner
/
deadtrees
connected to https://github.com/cwerner/deadtrees.git

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   createtiles@2019:
    4. 

  4.     cmd: mkdir -p data/processed.images.2019; gdal_retile.py  -csv locations.csv  -v
    5. 

  5.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    6. 

  6.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2019  data/raw/ortho_ms_2019_EPSG3044.tif
    7. 

  7.     deps:
    8. 

  8.     - path: data/raw/ortho_ms_2019_EPSG3044.tif
    9. 

  9.       md5: 28a27ea53b559c3cec937a3687407557
    10. 

  10.       size: 144920418531
    11. 

  11.     params:
    12. 

  12.       params.yaml:
    13. 

  13.         source_dim: 2048
    14. 

  14.     outs:
    15. 

  15.     - path: data/processed.images.2019
    16. 

  16.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    17. 

  17.       size: 144926802415
    18. 

  18.       nfiles: 30489
    19. 

  19.   createmasks:
    20. 

  20.     cmd: python scripts/createmasks.py  data/processed.images.2019  data/processed.masks.2019  data/raw/shapefiles/deadtrees_2019/deadtrees_2019.shp
    21. 

  21.       --negativesample data/raw/shapefiles/deadtrees_notdead/deadtrees_notdead.shp
    22. 

  22.     deps:
    23. 

  23.     - path: data/processed.images.2019
    24. 

  24.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    25. 

  25.       size: 144926802415
    26. 

  26.       nfiles: 30489
    27. 

  27.     - path: data/raw/shapefiles/deadtrees_2019
    28. 

  28.       md5: 206432f450611c44ceb32c49e50aa8c9.dir
    29. 

  29.       size: 3996517
    30. 

  30.       nfiles: 5
    31. 

  31.     - path: data/raw/shapefiles/deadtrees_notdead
    32. 

  32.       md5: 99b6d382aebfc46e79c9d044e95add4e.dir
    33. 

  33.       size: 31722
    34. 

  34.       nfiles: 5
    35. 

  35.     outs:
    36. 

  36.     - path: data/processed.masks.2019
    37. 

  37.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    38. 

  38.       size: 4103103156
    39. 

  39.       nfiles: 978
    40. 

  40.     - path: data/processed.masks.2019.neg_sample
    41. 

  41.       md5: e39614f069603f65a333d0a8713072a9.dir
    42. 

  42.       size: 335615520
    43. 

  43.       nfiles: 80
    44. 

  44.   createdataset:
    45. 

  45.     cmd: python scripts/createdataset.py  data/processed.images.2019  data/processed.masks.2019  data/dataset  --source_dim
    46. 

  46.       2048 --tile_size 256 --format TIFF
    47. 

  47.     deps:
    48. 

  48.     - path: data/processed.images.2019
    49. 

  49.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    50. 

  50.       size: 144926802415
    51. 

  51.       nfiles: 30489
    52. 

  52.     - path: data/processed.masks.2019
    53. 

  53.       md5: 86dfe9fff1765527e6ab7bdbd50e873d.dir
    54. 

  54.       size: 4103103156
    55. 

  55.       nfiles: 978
    56. 

  56.     - path: data/processed.masks.2019.neg_sample
    57. 

  57.       md5: e39614f069603f65a333d0a8713072a9.dir
    58. 

  58.       size: 335615520
    59. 

  59.       nfiles: 80
    60. 

  60.     params:
    61. 

  61.       params.yaml:
    62. 

  62.         createdataset.tile_size: 256
    63. 

  63.         file_type: TIFF
    64. 

  64.         source_dim: 2048
    65. 

  65.     outs:
    66. 

  66.     - path: data/dataset/stats.csv
    67. 

  67.       md5: 5d5e6ab2648bdb6b7846d4c8655a72a8
    68. 

  68.       size: 2539255
    69. 

  69.     - path: data/dataset/train
    70. 

  70.       md5: 09daf34f435e8a47ce8879a6f7a9899e.dir
    71. 

  71.       size: 10832783360
    72. 

  72.       nfiles: 208
    73. 

  73.   createbalanced:
    74. 

  74.     cmd: python scripts/createbalanced.py  data/dataset/stats.csv data/dataset/train  data/dataset/train_balanced  data/dataset/train_balanced_short
    75. 

  75.       --format TIFF --tmp-dir ./tmp
    76. 

  76.     deps:
    77. 

  77.     - path: data/dataset/stats.csv
    78. 

  78.       md5: 8f24c9c9fcbcab4f9388a4b2b26ed58c
    79. 

  79.       size: 2415683
    80. 

  80.     - path: data/dataset/train
    81. 

  81.       md5: a5a9bd6897f3c8b0143e3a378928cc54.dir
    82. 

  82.       size: 20627302400
    83. 

  83.       nfiles: 121
    84. 

  84.     params:
    85. 

  85.       params.yaml:
    86. 

  86.         file_type: TIFF
    87. 

  87.     outs:
    88. 

  88.     - path: data/dataset/train_balanced
    89. 

  89.       md5: 00c0e3636b55b92b63add488a2e62a12.dir
    90. 

  90.       size: 20596592640
    91. 

  91.       nfiles: 241
    92. 

  92.     - path: data/dataset/train_balanced_short
    93. 

  93.       md5: 94e764aa3326c386524910bfbac0454d.dir
    94. 

  94.       size: 2072975360
    95. 

  95.       nfiles: 97
    96. 

  96.   createtiles@2017:
    97. 

  97.     cmd: mkdir -p data/processed.images.2017; gdal_retile.py  -csv locations.csv  -v
    98. 

  98.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    99. 

  99.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2017  data/raw/ortho_ms_2017_EPSG3044.tif
    100. 

  100.     deps:
    101. 

  101.     - path: data/raw/ortho_ms_2017_EPSG3044.tif
    102. 

  102.       md5: 2389bdb4952d2c869c52e87abce30d4f
    103. 

  103.       size: 109896357004
    104. 

  104.     params:
    105. 

  105.       params.yaml:
    106. 

  106.         source_dim: 2048
    107. 

  107.     outs:
    108. 

  108.     - path: data/processed.images.2017
    109. 

  109.       md5: 66de96d201af5a47a09997691b992370.dir
    110. 

  110.       size: 109902740888
    111. 

  111.       nfiles: 30489
    112. 

  112.   inference@2017:
    113. 

  113.     cmd: mkdir -p data/predicted.2017; stdbuf -i0 -o0 -e0 python scripts/inference.py
    114. 

  114.       --all --nopreview -o data/predicted.2017 data/processed.images.2017; gdal_merge.py  -co
    115. 

  115.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    116. 

  116.       -o data/predicted_mosaic_2017.tif  data/predicted.2017/ortho_ms_2017_EPSG3044_*
    117. 

  117.     deps:
    118. 

  118.     - path: checkpoints/bestmodel.ckpt
    119. 

  119.       md5: fa7e507a107381e5dd9c2ebc9ddef09f
    120. 

  120.       size: 378649412
    121. 

  121.     - path: data/processed.images.2017
    122. 

  122.       md5: 66de96d201af5a47a09997691b992370.dir
    123. 

  123.       size: 109902740888
    124. 

  124.       nfiles: 30489
    125. 

  125.     outs:
    126. 

  126.     - path: data/predicted.2017
    127. 

  127.       md5: 6991e5e7cf8144ff46445ef25214fce6.dir
    128. 

  128.       size: 601515230
    129. 

  129.       nfiles: 20827
    130. 

  130.     - path: data/predicted_mosaic_2017.tif
    131. 

  131.       md5: 7cef75e791ea66524e8afdac1419e7e5
    132. 

  132.       size: 839252195
    133. 

  133.   inference@2019:
    134. 

  134.     cmd: mkdir -p data/predicted.2019; stdbuf -i0 -o0 -e0 python scripts/inference.py
    135. 

  135.       --all --nopreview -o data/predicted.2019 data/processed.images.2019; gdal_merge.py  -co
    136. 

  136.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    137. 

  137.       -o data/predicted_mosaic_2019.tif  data/predicted.2019/ortho_ms_2019_EPSG3044_*
    138. 

  138.     deps:
    139. 

  139.     - path: checkpoints/bestmodel.ckpt
    140. 

  140.       md5: fa7e507a107381e5dd9c2ebc9ddef09f
    141. 

  141.       size: 378649412
    142. 

  142.     - path: data/processed.images.2019
    143. 

  143.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    144. 

  144.       size: 144926802415
    145. 

  145.       nfiles: 30489
    146. 

  146.     outs:
    147. 

  147.     - path: data/predicted.2019
    148. 

  148.       md5: 88d9fe4afe7054d15e5014b9743d87c9.dir
    149. 

  149.       size: 484982007
    150. 

  150.       nfiles: 16227
    151. 

  151.     - path: data/predicted_mosaic_2019.tif
    152. 

  152.       md5: fd472cc45d61c7223cbd09ab2872a30a
    153. 

  153.       size: 811774816
    154. 

  154.   computestats:
    155. 

  155.     cmd: 'python scripts/computestats.py --frac 0.1 data/processed.images.2017   data/processed.images.2018   data/processed.images.2019  data/processed.images.2020 '
    156. 

  156.     deps:
    157. 

  157.     - path: data/processed.images.2017
    158. 

  158.       md5: 66de96d201af5a47a09997691b992370.dir
    159. 

  159.       size: 109902740888
    160. 

  160.       nfiles: 30489
    161. 

  161.     - path: data/processed.images.2018
    162. 

  162.       md5: cfa0adee6401f838f162a0510085becf.dir
    163. 

  163.       size: 144861203951
    164. 

  164.       nfiles: 30489
    165. 

  165.     - path: data/processed.images.2019
    166. 

  166.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    167. 

  167.       size: 144926802415
    168. 

  168.       nfiles: 30489
    169. 

  169.     - path: data/processed.images.2020
    170. 

  170.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    171. 

  171.       size: 163333629599
    172. 

  172.       nfiles: 30489
    173. 

  173.     outs:
    174. 

  174.     - path: data/processed.images.stats.json
    175. 

  175.       md5: 1ed04cbf29c00ed439bfa15bc170c5cb
    176. 

  176.   computestatsinference:
    177. 

  177.     cmd: 'python scripts/computestats_inference.py data/predicted.2017   data/predicted.2018   data/predicted.2019  data/predicted.2020  '
    178. 

  178.     deps:
    179. 

  179.     - path: data/predicted.2017
    180. 

  180.       md5: 6991e5e7cf8144ff46445ef25214fce6.dir
    181. 

  181.       size: 601515230
    182. 

  182.       nfiles: 20827
    183. 

  183.     - path: data/predicted.2018
    184. 

  184.       md5: 6dc4d6140a683a68243cd01d5d733001.dir
    185. 

  185.       size: 551422735
    186. 

  186.       nfiles: 19182
    187. 

  187.     - path: data/predicted.2019
    188. 

  188.       md5: 88d9fe4afe7054d15e5014b9743d87c9.dir
    189. 

  189.       size: 484982007
    190. 

  190.       nfiles: 16227
    191. 

  191.     - path: data/predicted.2020
    192. 

  192.       md5: ca8bf8b4c611989f5c962b07dee85344.dir
    193. 

  193.       size: 489635809
    194. 

  194.       nfiles: 16125
    195. 

  195.     outs:
    196. 

  196.     - path: data/predicted.stats.csv
    197. 

  197.       md5: 1b921ebb714f7234afd0d8b63f59a8d7
    198. 

  198.       size: 1916604
    199. 

  199.   createtiles@2018:
    200. 

  200.     cmd: mkdir -p data/processed.images.2018; gdal_retile.py  -csv locations.csv  -v
    201. 

  201.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    202. 

  202.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2018  data/raw/ortho_ms_2018_EPSG3044.tif
    203. 

  203.     deps:
    204. 

  204.     - path: data/raw/ortho_ms_2018_EPSG3044.tif
    205. 

  205.       md5: 59fc13ae47105e690ecc396202c2bc30
    206. 

  206.       size: 144854820067
    207. 

  207.     params:
    208. 

  208.       params.yaml:
    209. 

  209.         source_dim: 2048
    210. 

  210.     outs:
    211. 

  211.     - path: data/processed.images.2018
    212. 

  212.       md5: cfa0adee6401f838f162a0510085becf.dir
    213. 

  213.       size: 144861203951
    214. 

  214.       nfiles: 30489
    215. 

  215.   createtiles@2020:
    216. 

  216.     cmd: mkdir -p data/processed.images.2020; gdal_retile.py  -csv locations.csv  -v
    217. 

  217.       -ps 2048 2048 -co "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "ALPHA=NO"
    218. 

  218.       -co "NUM_THREADS=ALL_CPUS" -targetDir data/processed.images.2020  data/raw/ortho_ms_2020_EPSG3044.tif
    219. 

  219.     deps:
    220. 

  220.     - path: data/raw/ortho_ms_2020_EPSG3044.tif
    221. 

  221.       md5: 0ecdb70decb68b2d37446b21977d09f9
    222. 

  222.       size: 163327245715
    223. 

  223.     params:
    224. 

  224.       params.yaml:
    225. 

  225.         source_dim: 2048
    226. 

  226.     outs:
    227. 

  227.     - path: data/processed.images.2020
    228. 

  228.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    229. 

  229.       size: 163333629599
    230. 

  230.       nfiles: 30489
    231. 

  231.   inference@2018:
    232. 

  232.     cmd: mkdir -p data/predicted.2018; stdbuf -i0 -o0 -e0 python scripts/inference.py
    233. 

  233.       --all --nopreview -o data/predicted.2018 data/processed.images.2018; gdal_merge.py  -co
    234. 

  234.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    235. 

  235.       -o data/predicted_mosaic_2018.tif  data/predicted.2018/ortho_ms_2018_EPSG3044_*
    236. 

  236.     deps:
    237. 

  237.     - path: checkpoints/bestmodel.ckpt
    238. 

  238.       md5: fa7e507a107381e5dd9c2ebc9ddef09f
    239. 

  239.       size: 378649412
    240. 

  240.     - path: data/processed.images.2018
    241. 

  241.       md5: cfa0adee6401f838f162a0510085becf.dir
    242. 

  242.       size: 144861203951
    243. 

  243.       nfiles: 30489
    244. 

  244.     outs:
    245. 

  245.     - path: data/predicted.2018
    246. 

  246.       md5: 6dc4d6140a683a68243cd01d5d733001.dir
    247. 

  247.       size: 551422735
    248. 

  248.       nfiles: 19182
    249. 

  249.     - path: data/predicted_mosaic_2018.tif
    250. 

  250.       md5: f8ae9346bf27514a30592e745ab87bc1
    251. 

  251.       size: 830985617
    252. 

  252.   inference@2020:
    253. 

  253.     cmd: mkdir -p data/predicted.2020; stdbuf -i0 -o0 -e0 python scripts/inference.py
    254. 

  254.       --all --nopreview -o data/predicted.2020 data/processed.images.2020; gdal_merge.py  -co
    255. 

  255.       "TILED=YES" -co "COMPRESS=LZW" -co "PREDICTOR=2" -co "NUM_THREADS=ALL_CPUS"
    256. 

  256.       -o data/predicted_mosaic_2020.tif  data/predicted.2020/ortho_ms_2020_EPSG3044_*
    257. 

  257.     deps:
    258. 

  258.     - path: checkpoints/bestmodel.ckpt
    259. 

  259.       md5: fa7e507a107381e5dd9c2ebc9ddef09f
    260. 

  260.       size: 378649412
    261. 

  261.     - path: data/processed.images.2020
    262. 

  262.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    263. 

  263.       size: 163333629599
    264. 

  264.       nfiles: 30489
    265. 

  265.     outs:
    266. 

  266.     - path: data/predicted.2020
    267. 

  267.       md5: ca8bf8b4c611989f5c962b07dee85344.dir
    268. 

  268.       size: 489635809
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    271. 

  271.       md5: 5e3c82736e20844bb9b1fc6f4d6cbbb6
    272. 

  272.       size: 812671767
    273. 

  273.   createmasks@2017:
    274. 

  274.     cmd: python scripts/createmasks.py  data/processed.images.2017  data/processed.masks.2017  data/raw/shapefiles/deadtrees_2017/deadtrees_2017.shp
    275. 

  275.     deps:
    276. 

  276.     - path: data/processed.images.2017
    277. 

  277.       md5: 66de96d201af5a47a09997691b992370.dir
    278. 

  278.       size: 109902740888
    279. 

  279.       nfiles: 30489
    280. 

  280.     - path: data/raw/shapefiles/deadtrees_2017
    281. 

  281.       md5: 8d9432d5827254797ae04f89ec30aae1.dir
    282. 

  282.       size: 811993
    283. 

  283.       nfiles: 6
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  284.     outs:
    285. 

  285.     - path: data/processed.masks.2017
    286. 

  286.       md5: ed46f628af2e934ce245fef7bb707d02.dir
    287. 

  287.       size: 3633218132
    288. 

  288.       nfiles: 866
    289. 

  289.   createdataset@2017:
    290. 

  290.     cmd: python scripts/createdataset.py  data/processed.images.2017  data/processed.masks.2017  data/processed.lus.2017  data/dataset  --subdir
    291. 

  291.       train_2017 --source_dim 2048 --tile_size 256 --format TIFF --stats stats_2017.csv
    292. 

  292.     deps:
    293. 

  293.     - path: data/processed.images.2017
    294. 

  294.       md5: 66de96d201af5a47a09997691b992370.dir
    295. 

  295.       size: 109902740888
    296. 

  296.       nfiles: 30489
    297. 

  297.     - path: data/processed.lus.2017
    298. 

  298.       md5: cd93e1d1eb4f857a9dab0cdc9e61c476.dir
    299. 

  299.       size: 54193516092
    300. 

  300.       nfiles: 12918
    301. 

  301.     - path: data/processed.masks.2017
    302. 

  302.       md5: ed46f628af2e934ce245fef7bb707d02.dir
    303. 

  303.       size: 3633218132
    304. 

  304.       nfiles: 866
    305. 

  305.     params:
    306. 

  306.       params.yaml:
    307. 

  307.         createdataset.tile_size: 256
    308. 

  308.         file_type: TIFF
    309. 

  309.         source_dim: 2048
    310. 

  310.     outs:
    311. 

  311.     - path: data/dataset/stats_2017.csv
    312. 

  312.       md5: b29cb5609b2c95ab0e819619279d4c1c
    313. 

  313.       size: 2258658
    314. 

  314.     - path: data/dataset/train_2017
    315. 

  315.       md5: 325e1c100e27b7ba458f7a49ad45b311.dir
    316. 

  316.       size: 738652160
    317. 

  317.       nfiles: 29
    318. 

  318.   createdataset@2019:
    319. 

  319.     cmd: python scripts/createdataset.py  data/processed.images.2019  data/processed.masks.2019  data/processed.lus.2019  data/dataset  --subdir
    320. 

  320.       train_2019 --source_dim 2048 --tile_size 256 --format TIFF --stats stats_2019.csv
    321. 

  321.     deps:
    322. 

  322.     - path: data/processed.images.2019
    323. 

  323.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    324. 

  324.       size: 144926802415
    325. 

  325.       nfiles: 30489
    326. 

  326.     - path: data/processed.lus.2019
    327. 

  327.       md5: e7700c6cafd6d3140afc916637ef54c6.dir
    328. 

  328.       size: 54193516092
    329. 

  329.       nfiles: 12918
    330. 

  330.     - path: data/processed.masks.2019
    331. 

  331.       md5: 83ce7ca88e1ed7fbf401d0d185ec261d.dir
    332. 

  332.       size: 4103103156
    333. 

  333.       nfiles: 978
    334. 

  334.     params:
    335. 

  335.       params.yaml:
    336. 

  336.         createdataset.tile_size: 256
    337. 

  337.         file_type: TIFF
    338. 

  338.         source_dim: 2048
    339. 

  339.     outs:
    340. 

  340.     - path: data/dataset/stats_2019.csv
    341. 

  341.       md5: c4bdc60a6b48ccbf31bba9c7504c19fe
    342. 

  342.       size: 2513851
    343. 

  343.     - path: data/dataset/train_2019
    344. 

  344.       md5: c39665bff457346b2537a4a3a511b50b.dir
    345. 

  345.       size: 1519052800
    346. 

  346.       nfiles: 60
    347. 

  347.   createmasks@2019:
    348. 

  348.     cmd: python scripts/createmasks.py  data/processed.images.2019  data/processed.masks.2019  data/raw/shapefiles/deadtrees_2019/deadtrees_2019.shp
    349. 

  349.     deps:
    350. 

  350.     - path: data/processed.images.2019
    351. 

  351.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    352. 

  352.       size: 144926802415
    353. 

  353.       nfiles: 30489
    354. 

  354.     - path: data/raw/shapefiles/deadtrees_2019
    355. 

  355.       md5: 206432f450611c44ceb32c49e50aa8c9.dir
    356. 

  356.       size: 3996517
    357. 

  357.       nfiles: 5
    358. 

  358.     outs:
    359. 

  359.     - path: data/processed.masks.2019
    360. 

  360.       md5: 83ce7ca88e1ed7fbf401d0d185ec261d.dir
    361. 

  361.       size: 4103103156
    362. 

  362.       nfiles: 978
    363. 

  363.   mergedatasets:
    364. 

  364.     cmd: python scripts/mergedatasets.py data/dataset/train_2017 data/dataset/train_2019
    365. 

  365.     deps:
    366. 

  366.     - path: data/dataset/train_2017
    367. 

  367.       md5: 325e1c100e27b7ba458f7a49ad45b311.dir
    368. 

  368.       size: 738652160
    369. 

  369.       nfiles: 29
    370. 

  370.     - path: data/dataset/train_2019
    371. 

  371.       md5: c39665bff457346b2537a4a3a511b50b.dir
    372. 

  372.       size: 1519052800
    373. 

  373.       nfiles: 60
    374. 

  374.     outs:
    375. 

  375.     - path: data/dataset/test
    376. 

  376.       md5: 3ed97313db477e2d5f59e46b3d217014.dir
    377. 

  377.       size: 202332160
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  378.       nfiles: 9
    379. 

  379.     - path: data/dataset/train
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    381. 

  381.       size: 1592913920
    382. 

  382.       nfiles: 62
    383. 

  383.     - path: data/dataset/val
    384. 

  384.       md5: 14cbc9d627fef88c676cfbee18530b3f.dir
    385. 

  385.       size: 462458880
    386. 

  386.       nfiles: 18
    387. 

  387.   createforestmasks@2019:
    388. 

  388.     cmd: python scripts/createmasks.py  data/processed.images.2019  data/processed.lus.2019  data/raw/shapefiles/forestmask/CORINE_forest.shp
    389. 

  389.       --simple
    390. 

  390.     deps:
    391. 

  391.     - path: data/processed.images.2019
    392. 

  392.       md5: 35c31b781cb0bdb19650329132d83d05.dir
    393. 

  393.       size: 144926802415
    394. 

  394.       nfiles: 30489
    395. 

  395.     - path: data/raw/shapefiles/forestmask/CORINE_forest.shp
    396. 

  396.       md5: 63947721b118c62a091559483db5d8f6
    397. 

  397.       size: 43334576
    398. 

  398.     outs:
    399. 

  399.     - path: data/processed.lus.2019
    400. 

  400.       md5: e7700c6cafd6d3140afc916637ef54c6.dir
    401. 

  401.       size: 54193516092
    402. 

  402.       nfiles: 12918
    403. 

  403.   createforestmasks@2017:
    404. 

  404.     cmd: python scripts/createmasks.py  data/processed.images.2017  data/processed.lus.2017  data/raw/shapefiles/forestmask/CORINE_forest.shp
    405. 

  405.       --simple
    406. 

  406.     deps:
    407. 

  407.     - path: data/processed.images.2017
    408. 

  408.       md5: 66de96d201af5a47a09997691b992370.dir
    409. 

  409.       size: 109902740888
    410. 

  410.       nfiles: 30489
    411. 

  411.     - path: data/raw/shapefiles/forestmask/CORINE_forest.shp
    412. 

  412.       md5: 63947721b118c62a091559483db5d8f6
    413. 

  413.       size: 43334576
    414. 

  414.     outs:
    415. 

  415.     - path: data/processed.lus.2017
    416. 

  416.       md5: cd93e1d1eb4f857a9dab0cdc9e61c476.dir
    417. 

  417.       size: 54193516092
    418. 

  418.       nfiles: 12918
    419. 

  419.   createmasks@2020:
    420. 

  420.     cmd: python scripts/createmasks.py  data/processed.images.2020  data/processed.masks.2020  data/raw/shapefiles/deadtrees_2020_test/deadtrees_2020_test.shp
    421. 

  421.     deps:
    422. 

  422.     - path: data/processed.images.2020
    423. 

  423.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    424. 

  424.       size: 163333629599
    425. 

  425.       nfiles: 30489
    426. 

  426.     - path: data/raw/shapefiles/deadtrees_2020_test
    427. 

  427.       md5: 2a046c036168c2c799ec3e7ff2ac4bc4.dir
    428. 

  428.       size: 747779
    429. 

  429.       nfiles: 5
    430. 

  430.     outs:
    431. 

  431.     - path: data/processed.masks.2020
    432. 

  432.       md5: 64ab160672cf600aafc0292164150af2.dir
    433. 

  433.       size: 247528718
    434. 

  434.       nfiles: 59
    435. 

  435.   createmasks@2018:
    436. 

  436.     cmd: python scripts/createmasks.py  data/processed.images.2018  data/processed.masks.2018  data/raw/shapefiles/deadtrees_2018_test/deadtrees_2018_test.shp
    437. 

  437.     deps:
    438. 

  438.     - path: data/processed.images.2018
    439. 

  439.       md5: cfa0adee6401f838f162a0510085becf.dir
    440. 

  440.       size: 144861203951
    441. 

  441.       nfiles: 30489
    442. 

  442.     - path: data/raw/shapefiles/deadtrees_2018_test
    443. 

  443.       md5: d0f0aad50514c79c8939acc9c29683bf.dir
    444. 

  444.       size: 65551
    445. 

  445.       nfiles: 5
    446. 

  446.     outs:
    447. 

  447.     - path: data/processed.masks.2018
    448. 

  448.       md5: df9b55bc5bfd32cfedaafe633d4963ec.dir
    449. 

  449.       size: 121666658
    450. 

  450.       nfiles: 29
    451. 

  451.   createforestmasks@2018:
    452. 

  452.     cmd: python scripts/createmasks.py  data/processed.images.2018  data/processed.lus.2018  data/raw/shapefiles/forestmask/CORINE_forest.shp
    453. 

  453.       --simple
    454. 

  454.     deps:
    455. 

  455.     - path: data/processed.images.2018
    456. 

  456.       md5: cfa0adee6401f838f162a0510085becf.dir
    457. 

  457.       size: 144861203951
    458. 

  458.       nfiles: 30489
    459. 

  459.     - path: data/raw/shapefiles/forestmask/CORINE_forest.shp
    460. 

  460.       md5: 63947721b118c62a091559483db5d8f6
    461. 

  461.       size: 43334576
    462. 

  462.     outs:
    463. 

  463.     - path: data/processed.lus.2018
    464. 

  464.       md5: 692742b22e690cfbbef15101c24078c5.dir
    465. 

  465.       size: 54193516092
    466. 

  466.       nfiles: 12918
    467. 

  467.   createforestmasks@2020:
    468. 

  468.     cmd: python scripts/createmasks.py  data/processed.images.2020  data/processed.lus.2020  data/raw/shapefiles/forestmask/CORINE_forest.shp
    469. 

  469.       --simple
    470. 

  470.     deps:
    471. 

  471.     - path: data/processed.images.2020
    472. 

  472.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    473. 

  473.       size: 163333629599
    474. 

  474.       nfiles: 30489
    475. 

  475.     - path: data/raw/shapefiles/forestmask/CORINE_forest.shp
    476. 

  476.       md5: 63947721b118c62a091559483db5d8f6
    477. 

  477.       size: 43334576
    478. 

  478.     outs:
    479. 

  479.     - path: data/processed.lus.2020
    480. 

  480.       md5: 10691c48713cdf6555842329a31cf9a2.dir
    481. 

  481.       size: 54193516092
    482. 

  482.       nfiles: 12918
    483. 

  483.   createdataset@2018:
    484. 

  484.     cmd: python scripts/createdataset.py  data/processed.images.2018  data/processed.masks.2018  data/processed.lus.2018  data/dataset  --subdir
    485. 

  485.       train_2018 --source_dim 2048 --tile_size 256 --format TIFF --stats stats_2018.csv
    486. 

  486.     deps:
    487. 

  487.     - path: data/processed.images.2018
    488. 

  488.       md5: cfa0adee6401f838f162a0510085becf.dir
    489. 

  489.       size: 144861203951
    490. 

  490.       nfiles: 30489
    491. 

  491.     - path: data/processed.lus.2018
    492. 

  492.       md5: 692742b22e690cfbbef15101c24078c5.dir
    493. 

  493.       size: 54193516092
    494. 

  494.       nfiles: 12918
    495. 

  495.     - path: data/processed.masks.2018
    496. 

  496.       md5: df9b55bc5bfd32cfedaafe633d4963ec.dir
    497. 

  497.       size: 121666658
    498. 

  498.       nfiles: 29
    499. 

  499.     params:
    500. 

  500.       params.yaml:
    501. 

  501.         createdataset.tile_size: 256
    502. 

  502.         file_type: TIFF
    503. 

  503.         source_dim: 2048
    504. 

  504.     outs:
    505. 

  505.     - path: data/dataset/stats_2018.csv
    506. 

  506.       md5: aea5a89c55e0f0b3ab2a1c1bbccf218d
    507. 

  507.       size: 76225
    508. 

  508.     - path: data/dataset/train_2018
    509. 

  509.       md5: 5a70790f4dc3df08571e3994c5421205.dir
    510. 

  510.       size: 25692160
    511. 

  511.       nfiles: 1
    512. 

  512.   createdataset@2020:
    513. 

  513.     cmd: python scripts/createdataset.py  data/processed.images.2020  data/processed.masks.2020  data/processed.lus.2020  data/dataset  --subdir
    514. 

  514.       train_2020 --source_dim 2048 --tile_size 256 --format TIFF --stats stats_2020.csv
    515. 

  515.     deps:
    516. 

  516.     - path: data/processed.images.2020
    517. 

  517.       md5: 76d4daf0d9e69904a9370a0c74006d17.dir
    518. 

  518.       size: 163333629599
    519. 

  519.       nfiles: 30489
    520. 

  520.     - path: data/processed.lus.2020
    521. 

  521.       md5: 10691c48713cdf6555842329a31cf9a2.dir
    522. 

  522.       size: 54193516092
    523. 

  523.       nfiles: 12918
    524. 

  524.     - path: data/processed.masks.2020
    525. 

  525.       md5: 64ab160672cf600aafc0292164150af2.dir
    526. 

  526.       size: 247528718
    527. 

  527.       nfiles: 59
    528. 

  528.     params:
    529. 

  529.       params.yaml:
    530. 

  530.         createdataset.tile_size: 256
    531. 

  531.         file_type: TIFF
    532. 

  532.         source_dim: 2048
    533. 

  533.     outs:
    534. 

  534.     - path: data/dataset/stats_2020.csv
    535. 

  535.       md5: fbeccb80c0c127aafd2a3009d8a1cf1a
    536. 

  536.       size: 148564
    537. 

  537.     - path: data/dataset/train_2020
    538. 

  538.       md5: a7ef5b8149ea1b419ab2d22ad60165e5.dir
    539. 

  539.       size: 146124800
    540. 

  540.       nfiles: 6
    541.
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