├── 1. Building Footprint Segmentation Pipeline.ipynb ├── 2. Inference on Test Images.ipynb ├── 3. Vectorization and Postprocessing.ipynb ├── 4. Model Evaluation.ipynb ├── LICENSE └── README.md /3. Vectorization and Postprocessing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import numpy as np\n", 10 | "import rasterio\n", 11 | "from rasterio.windows import Window\n", 12 | "\n", 13 | "import cv2 \n", 14 | "import geopandas as gpd\n", 15 | "\n", 16 | "from shapely.geometry import MultiPolygon, Polygon\n", 17 | "from shapely.ops import cascaded_union\n", 18 | "from collections import defaultdict\n", 19 | "\n", 20 | "from pathlib import Path\n", 21 | "\n", 22 | "import matplotlib.pyplot as plt\n", 23 | "%matplotlib inline\n", 24 | "\n", 25 | "from tqdm import tqdm" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# creating polygons from pixel masks\n", 35 | "# from https://www.kaggle.com/lopuhin/full-pipeline-demo-poly-pixels-ml-poly\n", 36 | "\n", 37 | "def mask_to_polygons(mask, epsilon=10, min_area=50.):\n", 38 | " # first, find contours with cv2: it's much faster than shapely\n", 39 | " image, contours, hierarchy = cv2.findContours(\n", 40 | " ((mask == 1) * 255).astype(np.uint8),\n", 41 | " cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_KCOS)\n", 42 | " # create approximate contours to have reasonable submission size\n", 43 | "# epsi = lambda x: epsilon*cv2.arcLength(x,True)\n", 44 | " approx_contours = [cv2.approxPolyDP(cnt, epsilon, True)\n", 45 | " for cnt in contours]\n", 46 | " if not contours:\n", 47 | " return MultiPolygon()\n", 48 | " # now messy stuff to associate parent and child contours\n", 49 | " cnt_children = defaultdict(list)\n", 50 | " child_contours = set()\n", 51 | " assert hierarchy.shape[0] == 1\n", 52 | " # http://docs.opencv.org/3.1.0/d9/d8b/tutorial_py_contours_hierarchy.html\n", 53 | " for idx, (_, _, _, parent_idx) in enumerate(hierarchy[0]):\n", 54 | " if parent_idx != -1:\n", 55 | " child_contours.add(idx)\n", 56 | " cnt_children[parent_idx].append(approx_contours[idx])\n", 57 | " # create actual polygons filtering by area (removes artifacts)\n", 58 | " all_polygons = []\n", 59 | " for idx, cnt in enumerate(approx_contours):\n", 60 | " if idx not in child_contours and cv2.contourArea(cnt) >= min_area:\n", 61 | " assert cnt.shape[1] == 1\n", 62 | " poly = Polygon(\n", 63 | " shell=cnt[:, 0, :],\n", 64 | " holes=[c[:, 0, :] for c in cnt_children.get(idx, [])\n", 65 | " if cv2.contourArea(c) >= min_area])\n", 66 | " all_polygons.append(poly)\n", 67 | " # approximating polygons might have created invalid ones, fix them\n", 68 | " all_polygons = MultiPolygon(all_polygons)\n", 69 | " if not all_polygons.is_valid:\n", 70 | " all_polygons = all_polygons.buffer(0)\n", 71 | " # Sometimes buffer() converts a simple Multipolygon to just a Polygon,\n", 72 | " # need to keep it a Multi throughout\n", 73 | " if all_polygons.type == 'Polygon':\n", 74 | " all_polygons = MultiPolygon([all_polygons])\n", 75 | " return all_polygons" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 3, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "INPUT = Path('/media/splash/00F2D259F2D25310/Data/final/outputs')\n", 85 | "COG_URL = Path('/media/splash/00F2D259F2D25310/Data/final/arid.tif')" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": {}, 91 | "source": [ 92 | "## If needed, reproject to EPSG:4326" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": 4, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "src_tif = 'arid1x_merged.tif'\n", 102 | "dst_tif = 'arid1x_merged_reproj.tif'" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 5, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "data": { 112 | "text/plain": [ 113 | "{'driver': 'GTiff',\n", 114 | " 'dtype': 'uint8',\n", 115 | " 'nodata': None,\n", 116 | " 'width': 6431,\n", 117 | " 'height': 6503,\n", 118 | " 'count': 3,\n", 119 | " 'crs': CRS.from_epsg(3785),\n", 120 | " 'transform': Affine(0.14931428515517545, 0.0, 8135024.072190155,\n", 121 | " 0.0, -0.14931402813080957, 3982066.4113659593)}" 122 | ] 123 | }, 124 | "execution_count": 5, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "raster = rasterio.open(INPUT/src_tif,'r')\n", 131 | "raster.meta" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 6, 137 | "metadata": {}, 138 | "outputs": [], 139 | "source": [ 140 | "# https://rasterio.readthedocs.io/en/latest/topics/reproject.html\n", 141 | "\n", 142 | "from rasterio.warp import calculate_default_transform, reproject, Resampling\n", 143 | "\n", 144 | "dst_crs = 'EPSG:4326'\n", 145 | "\n", 146 | "with rasterio.open(INPUT/src_tif) as src:\n", 147 | " transform, width, height = calculate_default_transform(\n", 148 | " src.crs, dst_crs, src.width, src.height, *src.bounds)\n", 149 | " kwargs = src.meta.copy()\n", 150 | " kwargs.update({\n", 151 | " 'crs': dst_crs,\n", 152 | " 'transform': transform,\n", 153 | " 'width': width,\n", 154 | " 'height': height,\n", 155 | " 'compress': 'JPEG',\n", 156 | " 'tiled': True, \n", 157 | " 'blockxsize': 256, \n", 158 | " 'blockysize': 256\n", 159 | " })\n", 160 | "\n", 161 | " with rasterio.open(INPUT/dst_tif, 'w', **kwargs) as dst:\n", 162 | " for i in range(1, src.count + 1):\n", 163 | " reproject(\n", 164 | " source=rasterio.band(src, i),\n", 165 | " destination=rasterio.band(dst, i),\n", 166 | " src_transform=src.transform,\n", 167 | " src_crs=src.crs,\n", 168 | " dst_transform=transform,\n", 169 | " dst_crs=dst_crs,\n", 170 | " resampling=Resampling.nearest,\n", 171 | " )" 172 | ] 173 | }, 174 | { 175 | "cell_type": "markdown", 176 | "metadata": {}, 177 | "source": [ 178 | "or with gdalwarp on CLI:" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": null, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [ 187 | "!gdalwarp -co compress=JPEG -co PHOTOMETRIC=YCBCR -co TILED=YES -co \"BLOCKXSIZE=256\" -co \"BLOCKYSIZE=256\" -s_srs EPSG:32737 -t_srs EPSG:4326 {INPUT}/{src_tif} {INPUT}/{dst_tif} " 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "## Open and Read Windows" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 7, 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [ 203 | "dst_tif = 'arid1x_merged_reproj.tif'" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": 8, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "raster = rasterio.open(INPUT/dst_tif,'r')\n", 213 | "data = raster.read()" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 9, 219 | "metadata": {}, 220 | "outputs": [ 221 | { 222 | "data": { 223 | "text/plain": [ 224 | "{'driver': 'GTiff',\n", 225 | " 'dtype': 'uint8',\n", 226 | " 'nodata': None,\n", 227 | " 'width': 6997,\n", 228 | " 'height': 5890,\n", 229 | " 'count': 3,\n", 230 | " 'crs': CRS.from_epsg(4326),\n", 231 | " 'transform': Affine(1.2328306174800615e-06, 0.0, 73.07816460728642,\n", 232 | " 0.0, -1.2328306174800615e-06, 33.651230626615664)}" 233 | ] 234 | }, 235 | "execution_count": 9, 236 | "metadata": {}, 237 | "output_type": "execute_result" 238 | } 239 | ], 240 | "source": [ 241 | "raster.meta" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": 10, 247 | "metadata": {}, 248 | "outputs": [ 249 | { 250 | "data": { 251 | "text/plain": [ 252 | "[(256, 256), (256, 256), (256, 256)]" 253 | ] 254 | }, 255 | "execution_count": 10, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "raster.block_shapes" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 11, 267 | "metadata": {}, 268 | "outputs": [], 269 | "source": [ 270 | "def pad_window(window, pad):\n", 271 | " col_off, row_off, width, height = window.flatten()\n", 272 | " return Window(col_off-pad//2, row_off-pad//2,width+pad,height+pad)" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": 12, 278 | "metadata": {}, 279 | "outputs": [], 280 | "source": [ 281 | "# padded windowed reads with blocks and rasterio: https://rasterio.readthedocs.io/en/latest/topics/windowed-rw.html\n", 282 | "\n", 283 | "pad = 64\n", 284 | "windows = []\n", 285 | "for ji, window in raster.block_windows(1):\n", 286 | " assert len(set(raster.block_shapes)) == 1\n", 287 | " window_padded = pad_window(window, pad)\n", 288 | " windows.append((ji, window_padded))" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": 13, 294 | "metadata": {}, 295 | "outputs": [ 296 | { 297 | "data": { 298 | "text/plain": [ 299 | "672" 300 | ] 301 | }, 302 | "execution_count": 13, 303 | "metadata": {}, 304 | "output_type": "execute_result" 305 | } 306 | ], 307 | "source": [ 308 | "len(windows)" 309 | ] 310 | }, 311 | { 312 | "cell_type": "code", 313 | "execution_count": 14, 314 | "metadata": { 315 | "scrolled": true 316 | }, 317 | "outputs": [ 318 | { 319 | "data": { 320 | "text/plain": [ 321 | "[((5, 10), Window(col_off=2528, row_off=1248, width=320, height=320)),\n", 322 | " ((5, 11), Window(col_off=2784, row_off=1248, width=320, height=320)),\n", 323 | " ((5, 12), Window(col_off=3040, row_off=1248, width=320, height=320)),\n", 324 | " ((5, 13), Window(col_off=3296, row_off=1248, width=320, height=320)),\n", 325 | " ((5, 14), Window(col_off=3552, row_off=1248, width=320, height=320)),\n", 326 | " ((5, 15), Window(col_off=3808, row_off=1248, width=320, height=320)),\n", 327 | " ((5, 16), Window(col_off=4064, row_off=1248, width=320, height=320)),\n", 328 | " ((5, 17), Window(col_off=4320, row_off=1248, width=320, height=320)),\n", 329 | " ((5, 18), Window(col_off=4576, row_off=1248, width=320, height=320)),\n", 330 | " ((5, 19), Window(col_off=4832, row_off=1248, width=320, height=320))]" 331 | ] 332 | }, 333 | "execution_count": 14, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "windows[150:160]" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": 15, 345 | "metadata": {}, 346 | "outputs": [], 347 | "source": [ 348 | "def poly2coords(poly_mask, transform):\n", 349 | " geo_coords = [(coord[0],coord[1])*transform for coord in poly[0].exterior.coords for poly in poly_mask]\n", 350 | " return Polygon(geo_coords)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 16, 356 | "metadata": { 357 | "scrolled": true 358 | }, 359 | "outputs": [ 360 | { 361 | "name": "stderr", 362 | "output_type": "stream", 363 | "text": [ 364 | "100%|██████████| 672/672 [00:01<00:00, 662.94it/s]\n" 365 | ] 366 | } 367 | ], 368 | "source": [ 369 | "mask_thres = 0.7\n", 370 | "epsilon = 10\n", 371 | "min_area = 100.\n", 372 | "erode = 5\n", 373 | "dilate = 7\n", 374 | "\n", 375 | "polys = []\n", 376 | "\n", 377 | "for window in tqdm(windows):\n", 378 | " win_tnfm = rasterio.windows.transform(window[1], raster.meta['transform'])\n", 379 | " win_img = raster.read(window=window[1])[0,:,:]\n", 380 | "\n", 381 | " mask = win_img > (mask_thres*255)\n", 382 | " # erode and dilate\n", 383 | " mask = cv2.erode(mask.astype('uint8'), np.ones((erode,erode),np.uint8), iterations=1)\n", 384 | " mask = cv2.dilate(mask.astype('uint8'), np.ones((dilate,dilate),np.uint8), iterations=1)\n", 385 | "\n", 386 | " # label via connected components\n", 387 | " _, instances = cv2.connectedComponents(mask.astype('uint8'))\n", 388 | "\n", 389 | " # make polys from instances\n", 390 | " uniques = list(np.unique(instances))\n", 391 | " for b in uniques[1:]:\n", 392 | " poly = mask_to_polygons(instances==b,epsilon, min_area)\n", 393 | " try: \n", 394 | " if poly.type == 'MultiPolygon': \n", 395 | " geo_coords = poly2coords(poly, win_tnfm)\n", 396 | " polys.append(geo_coords)\n", 397 | " #else: print('not a MultiPolygon')\n", 398 | " except Exception as exc: print(f\"{exc}: {window}\")" 399 | ] 400 | }, 401 | { 402 | "cell_type": "code", 403 | "execution_count": 17, 404 | "metadata": {}, 405 | "outputs": [ 406 | { 407 | "data": { 408 | "image/svg+xml": [ 409 | "" 410 | ], 411 | "text/plain": [ 412 | "" 413 | ] 414 | }, 415 | "execution_count": 17, 416 | "metadata": {}, 417 | "output_type": "execute_result" 418 | } 419 | ], 420 | "source": [ 421 | "polys[5]" 422 | ] 423 | }, 424 | { 425 | "cell_type": "code", 426 | "execution_count": 18, 427 | "metadata": {}, 428 | "outputs": [ 429 | { 430 | "data": { 431 | "text/plain": [ 432 | "357" 433 | ] 434 | }, 435 | "execution_count": 18, 436 | "metadata": {}, 437 | "output_type": "execute_result" 438 | } 439 | ], 440 | "source": [ 441 | "len(polys)" 442 | ] 443 | }, 444 | { 445 | "cell_type": "code", 446 | "execution_count": 19, 447 | "metadata": {}, 448 | "outputs": [], 449 | "source": [ 450 | "# dedupe windowed polys\n", 451 | "merged_poly = gpd.GeoDataFrame()\n", 452 | "merged_poly['geometry'] = gpd.GeoSeries(cascaded_union(polys))" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 20, 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "text/plain": [ 463 | "" 464 | ] 465 | }, 466 | "execution_count": 20, 467 | "metadata": {}, 468 | "output_type": "execute_result" 469 | }, 470 | { 471 | "data": { 472 | "image/png": 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" 475 | ] 476 | }, 477 | "metadata": { 478 | "needs_background": "light" 479 | }, 480 | "output_type": "display_data" 481 | } 482 | ], 483 | "source": [ 484 | "merged_poly['geometry'].plot(figsize=(10,10))" 485 | ] 486 | }, 487 | { 488 | "cell_type": "code", 489 | "execution_count": 21, 490 | "metadata": {}, 491 | "outputs": [], 492 | "source": [ 493 | "# https://gis.stackexchange.com/questions/271733/geopandas-dissolve-overlapping-polygons\n", 494 | "# https://nbviewer.jupyter.org/gist/rutgerhofste/6e7c6569616c2550568b9ce9cb4716a3\n", 495 | "\n", 496 | "def explode(gdf):\n", 497 | " \"\"\" \n", 498 | " Will explode the geodataframe's muti-part geometries into single \n", 499 | " geometries. Each row containing a multi-part geometry will be split into\n", 500 | " multiple rows with single geometries, thereby increasing the vertical size\n", 501 | " of the geodataframe. The index of the input geodataframe is no longer\n", 502 | " unique and is replaced with a multi-index. \n", 503 | "\n", 504 | " The output geodataframe has an index based on two columns (multi-index) \n", 505 | " i.e. 'level_0' (index of input geodataframe) and 'level_1' which is a new\n", 506 | " zero-based index for each single part geometry per multi-part geometry\n", 507 | " \n", 508 | " Args:\n", 509 | " gdf (gpd.GeoDataFrame) : input geodataframe with multi-geometries\n", 510 | " \n", 511 | " Returns:\n", 512 | " gdf (gpd.GeoDataFrame) : exploded geodataframe with each single \n", 513 | " geometry as a separate entry in the \n", 514 | " geodataframe. The GeoDataFrame has a multi-\n", 515 | " index set to columns level_0 and level_1\n", 516 | " \n", 517 | " \"\"\"\n", 518 | " gs = gdf.explode()\n", 519 | " gdf2 = gs.reset_index().rename(columns={0: 'geometry'})\n", 520 | " gdf_out = gdf2.merge(gdf.drop('geometry', axis=1), left_on='level_0', right_index=True)\n", 521 | " gdf_out = gdf_out.set_index(['level_0', 'level_1']).set_geometry('geometry')\n", 522 | " gdf_out.crs = gdf.crs\n", 523 | " return gdf_out" 524 | ] 525 | }, 526 | { 527 | "cell_type": "code", 528 | "execution_count": 22, 529 | "metadata": {}, 530 | "outputs": [], 531 | "source": [ 532 | "gdf_out = explode(merged_poly)\n", 533 | "gdf_out = gdf_out.reset_index()" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 23, 539 | "metadata": {}, 540 | "outputs": [ 541 | { 542 | "data": { 543 | "text/html": [ 544 | "
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" 968 | ] 969 | }, 970 | "execution_count": 33, 971 | "metadata": {}, 972 | "output_type": "execute_result" 973 | } 974 | ], 975 | "source": [ 976 | "gdf_out.head()" 977 | ] 978 | }, 979 | { 980 | "cell_type": "code", 981 | "execution_count": 34, 982 | "metadata": {}, 983 | "outputs": [], 984 | "source": [ 985 | "gdf_out.geometry = gdf_out.geometry_buffered" 986 | ] 987 | }, 988 | { 989 | "cell_type": "code", 990 | "execution_count": 35, 991 | "metadata": {}, 992 | "outputs": [], 993 | "source": [ 994 | "gdf_out.geometry.to_file(f'/media/splash/00F2D259F2D25310/Data/final/outputs/arid.geojson', driver='GeoJSON')" 995 | ] 996 | }, 997 | { 998 | "cell_type": "markdown", 999 | "metadata": {}, 1000 | "source": [ 1001 | "# Crop detected building images for further classifier" 1002 | ] 1003 | }, 1004 | { 1005 | "cell_type": "code", 1006 | "execution_count": null, 1007 | "metadata": {}, 1008 | "outputs": [], 1009 | "source": [ 1010 | "cog_rst = rasterio.open(COG_URL,'r')" 1011 | ] 1012 | }, 1013 | { 1014 | "cell_type": "code", 1015 | "execution_count": null, 1016 | "metadata": {}, 1017 | "outputs": [], 1018 | "source": [ 1019 | "cog_rst.meta" 1020 | ] 1021 | }, 1022 | { 1023 | "cell_type": "code", 1024 | "execution_count": null, 1025 | "metadata": {}, 1026 | "outputs": [], 1027 | "source": [ 1028 | "df = gpd.read_file(f'/media/splash/00F2D259F2D25310/Data/final/outputs/arid.geojson')\n", 1029 | "df.tail()" 1030 | ] 1031 | }, 1032 | { 1033 | "cell_type": "code", 1034 | "execution_count": null, 1035 | "metadata": {}, 1036 | "outputs": [], 1037 | "source": [ 1038 | "cog_rst.meta['crs']['init']" 1039 | ] 1040 | }, 1041 | { 1042 | "cell_type": "code", 1043 | "execution_count": null, 1044 | "metadata": {}, 1045 | "outputs": [], 1046 | "source": [ 1047 | "from pyproj import Proj, transform\n", 1048 | "\n", 1049 | "condition = 'test'\n", 1050 | "grid_num = '042'\n", 1051 | "CLASSIFY = Path('/media/splash/00F2D259F2D25310/Data/final')\n", 1052 | "CLASSIFY.mkdir(exist_ok=True)\n", 1053 | "(CLASSIFY/f'{condition}_{grid_num}').mkdir(exist_ok=True)\n", 1054 | "\n", 1055 | "for i,row in tqdm(df[(df['geometry'].type=='Polygon')].iterrows()):\n", 1056 | " \n", 1057 | " poly = row['geometry'].buffer(0.00001) # padding around detection to crop\n", 1058 | "# print(poly.bounds)\n", 1059 | "\n", 1060 | " inProj = Proj(init='epsg:4326') \n", 1061 | " outProj = Proj(init=cog_rst.meta['crs']['init']) # convert to cog crs\n", 1062 | " \n", 1063 | " # convert from geocoords to display window\n", 1064 | " minx, miny = transform(inProj,outProj,*poly.bounds[:2])\n", 1065 | " maxx, maxy = transform(inProj,outProj,*poly.bounds[2:])\n", 1066 | " ul = cog_rst.index(minx, miny)\n", 1067 | " lr = cog_rst.index(maxx, maxy)\n", 1068 | " disp_minx, disp_maxx, disp_miny, disp_maxy = lr[0], (max(ul[0],0)+1), max(ul[1],0), (lr[1]+1)\n", 1069 | "\n", 1070 | " if disp_maxx-disp_minx <= 150: disp_maxx += 25; disp_minx-=25; \n", 1071 | " if disp_maxy-disp_miny <= 150: disp_maxy += 25; disp_miny-=25;\n", 1072 | "\n", 1073 | " window = (max(disp_minx,0), disp_maxx), (max(disp_miny,0), disp_maxy)\n", 1074 | " data = cog_rst.read(window=window)\n", 1075 | " \n", 1076 | " pk = str(row.id).zfill(5)\n", 1077 | " tile_bgr = cv2.cvtColor(np.rollaxis(data,0,3), cv2.COLOR_RGB2BGR)\n", 1078 | " cv2.imwrite(f\"{str(CLASSIFY)}/{condition}_{grid_num}/{grid_num}_{pk}_{condition}.jpg\", tile_bgr)\n" 1079 | ] 1080 | }, 1081 | { 1082 | "cell_type": "code", 1083 | "execution_count": null, 1084 | "metadata": {}, 1085 | "outputs": [], 1086 | "source": [] 1087 | } 1088 | ], 1089 | "metadata": { 1090 | "kernelspec": { 1091 | "display_name": "Python [conda env:fastai1] *", 1092 | "language": "python", 1093 | "name": "conda-env-fastai1-py" 1094 | }, 1095 | "language_info": { 1096 | "codemirror_mode": { 1097 | "name": "ipython", 1098 | "version": 3 1099 | }, 1100 | "file_extension": ".py", 1101 | "mimetype": "text/x-python", 1102 | "name": "python", 1103 | "nbconvert_exporter": "python", 1104 | "pygments_lexer": "ipython3", 1105 | "version": "3.7.1" 1106 | } 1107 | }, 1108 | "nbformat": 4, 1109 | "nbformat_minor": 2 1110 | } 1111 | -------------------------------------------------------------------------------- /4. Model Evaluation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Eval scripts adapted from https://github.com/SpaceNetChallenge/utilities/tree/master/python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": null, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import numpy as np\n", 17 | "import geopandas as gpd\n", 18 | "import rtree\n", 19 | "\n", 20 | "from pathlib import Path\n", 21 | "\n", 22 | "import matplotlib.pyplot as plt\n", 23 | "import matplotlib as mpl\n", 24 | "%matplotlib inline\n", 25 | "\n", 26 | "from tqdm import tqdm" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": null, 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "def create_rtree_from_poly(poly_list):\n", 36 | " # create index\n", 37 | " index = rtree.index.Index(interleaved=False)\n", 38 | " for idx, building in enumerate(poly_list):\n", 39 | " minx, miny, maxx, maxy = building.bounds\n", 40 | " envelope = (minx, maxx, miny, maxy)\n", 41 | " index.insert(idx, envelope)\n", 42 | "\n", 43 | " return index\n", 44 | "\n", 45 | "def search_rtree(test_building, index):\n", 46 | " # input test poly ogr.Geometry and rtree index\n", 47 | " if test_building.type == 'Polygon' or \\\n", 48 | " test_building.type == 'MultiPolygon':\n", 49 | " minx, miny, maxx, maxy = test_building.bounds\n", 50 | " envelope = (minx, maxx, miny, maxy) \n", 51 | " fidlist = index.intersection(envelope)\n", 52 | " else:\n", 53 | " fidlist = []\n", 54 | "\n", 55 | " return fidlist\n" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "def iou(test_poly, truth_polys, truth_index=[]):\n", 65 | " fidlistArray = []\n", 66 | " iou_list = []\n", 67 | " \n", 68 | " if truth_index:\n", 69 | " fidlist = search_rtree(test_poly, truth_index)\n", 70 | "\n", 71 | " for fid in fidlist:\n", 72 | " if not test_poly.is_valid:\n", 73 | " test_poly = test_poly.buffer(0.0)\n", 74 | "\n", 75 | " intersection_result = test_poly.intersection(truth_polys[fid].buffer(0.0))\n", 76 | " fidlistArray.append(fid)\n", 77 | "\n", 78 | " if intersection_result.type == 'Polygon' or \\\n", 79 | " intersection_result.type == 'MultiPolygon':\n", 80 | " intersection_area = intersection_result.area\n", 81 | " union_area = test_poly.union(truth_polys[fid].buffer(0.0)).area\n", 82 | " iou_list.append(intersection_area / union_area)\n", 83 | "\n", 84 | " else:\n", 85 | " iou_list.append(0)\n", 86 | "\n", 87 | " else:\n", 88 | " for idx, truth_poly in enumerate(truth_polys):\n", 89 | " if not test_poly.is_valid or not truth_poly.is_valid:\n", 90 | " test_poly = test_poly.buffer(0.0)\n", 91 | " truth_poly = truth_poly.buffer(0.0)\n", 92 | "# print(f'fixed geom error at {idx}')\n", 93 | "\n", 94 | " intersection_result = test_poly.intersection(truth_poly)\n", 95 | " #print(idx, intersection_result.type)\n", 96 | "\n", 97 | " if intersection_result.type == 'Polygon' or \\\n", 98 | " intersection_result.type == 'MultiPolygon':\n", 99 | " intersection_area = intersection_result.area\n", 100 | " union_area = test_poly.union(truth_poly).area\n", 101 | " iou_list.append(intersection_area / union_area)\n", 102 | " # print(f'found intersect at test_poly {i} with truth poly {idx}')\n", 103 | " # print(intersection_area/union_area)\n", 104 | " else: \n", 105 | " iou_list.append(0)\n", 106 | " \n", 107 | " return iou_list, fidlistArray" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "def score(test_polys, truth_polys, threshold=0.5, truth_index=[],\n", 117 | " resultGeoJsonName = [],\n", 118 | " imageId = []):\n", 119 | "\n", 120 | " # Define internal functions\n", 121 | "\n", 122 | " # Find detections using threshold/argmax/IoU for test polygons\n", 123 | " true_pos_count = 0\n", 124 | " false_pos_count = 0\n", 125 | " truth_poly_count = len(truth_polys)\n", 126 | " \n", 127 | " true_ids = []\n", 128 | " false_ids = []\n", 129 | "\n", 130 | " for idx, test_poly in tqdm(enumerate(test_polys)):\n", 131 | " if truth_polys:\n", 132 | " iou_list, fidlist = iou(test_poly, truth_polys, truth_index)\n", 133 | " if not iou_list:\n", 134 | " maxiou = 0\n", 135 | " else:\n", 136 | " maxiou = np.max(iou_list)\n", 137 | "\n", 138 | "# print(maxiou, iou_list, fidlist)\n", 139 | " if maxiou >= threshold:\n", 140 | " true_pos_count += 1\n", 141 | " true_ids.append(idx)\n", 142 | " minx, miny, maxx, maxy = truth_polys[fidlist[np.argmax(iou_list)]].bounds\n", 143 | " envelope = (minx, maxx, miny, maxy) \n", 144 | " truth_index.delete(fidlist[np.argmax(iou_list)], envelope)\n", 145 | " #del truth_polys[fidlist[np.argmax(iou_list)]]\n", 146 | " else:\n", 147 | " false_pos_count += 1\n", 148 | " false_ids.append(idx)\n", 149 | " else:\n", 150 | " false_pos_count += 1\n", 151 | " false_ids.append(idx)\n", 152 | "\n", 153 | " false_neg_count = truth_poly_count - true_pos_count\n", 154 | "\n", 155 | " return true_pos_count, false_pos_count, false_neg_count, true_ids, false_ids" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": null, 161 | "metadata": {}, 162 | "outputs": [], 163 | "source": [ 164 | "def evalfunction(image_id, test_polys, truth_polys, truth_index=[], resultGeoJsonName=[], threshold = 0.5):\n", 165 | "\n", 166 | " if len(truth_polys)==0:\n", 167 | " true_pos_count = 0\n", 168 | " false_pos_count = len(test_polys)\n", 169 | " false_neg_count = 0\n", 170 | " else:\n", 171 | " true_pos_count, false_pos_count, false_neg_count, true_ids, false_ids = score(test_polys, truth_polys,\n", 172 | " truth_index=truth_index,\n", 173 | " resultGeoJsonName=resultGeoJsonName,\n", 174 | " imageId=image_id,\n", 175 | " threshold=threshold\n", 176 | " )\n", 177 | "\n", 178 | "\n", 179 | " if (true_pos_count > 0):\n", 180 | "\n", 181 | " precision = float(true_pos_count) / (float(true_pos_count) + float(false_pos_count))\n", 182 | " recall = float(true_pos_count) / (float(true_pos_count) + float(false_neg_count))\n", 183 | " F1score = 2.0 * precision * recall / (precision + recall)\n", 184 | " else:\n", 185 | " F1score = 0\n", 186 | " return ((F1score, true_pos_count, false_pos_count, false_neg_count), true_ids, false_ids, image_id)" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": null, 192 | "metadata": {}, 193 | "outputs": [], 194 | "source": [ 195 | "def precision_recall(true_pos_count, false_pos_count, false_neg_count):\n", 196 | " precision = float(true_pos_count) / (float(true_pos_count) + float(false_pos_count))\n", 197 | " recall = float(true_pos_count) / (float(true_pos_count) + float(false_neg_count))\n", 198 | " return (precision, recall)" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": null, 204 | "metadata": {}, 205 | "outputs": [], 206 | "source": [ 207 | "TRUTH = Path('znz-input')\n", 208 | "TEST = Path('znz-20190118')" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": null, 214 | "metadata": {}, 215 | "outputs": [], 216 | "source": [ 217 | "df_truth = gpd.read_file(f'{str(TRUTH)}/grid_042.geojson')\n", 218 | "df_test = gpd.read_file(f'{str(TEST)}/grid_042_20190118_07_classes.geojson')" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": null, 224 | "metadata": {}, 225 | "outputs": [], 226 | "source": [ 227 | "df_truth.head()" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": null, 233 | "metadata": {}, 234 | "outputs": [], 235 | "source": [ 236 | "df_test.head()" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "metadata": {}, 243 | "outputs": [], 244 | "source": [ 245 | "df_truth.geometry.plot(figsize=(10,10))" 246 | ] 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": null, 251 | "metadata": {}, 252 | "outputs": [], 253 | "source": [ 254 | "df_test.geometry.plot(figsize=(10,10))" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": null, 260 | "metadata": {}, 261 | "outputs": [], 262 | "source": [ 263 | "df_test['cat'].value_counts()" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": null, 269 | "metadata": {}, 270 | "outputs": [], 271 | "source": [ 272 | "df_truth['condition'].value_counts()" 273 | ] 274 | }, 275 | { 276 | "cell_type": "code", 277 | "execution_count": null, 278 | "metadata": {}, 279 | "outputs": [], 280 | "source": [ 281 | "cats = [('conf_foundation','Foundation'),('conf_unfinished','Incomplete'),('conf_completed','Complete')]" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": null, 287 | "metadata": {}, 288 | "outputs": [], 289 | "source": [ 290 | "for (test_cat, truth_cat) in cats:\n", 291 | " test_polys = [geom for geom in df_test[df_test['cat'] == test_cat].geometry]\n", 292 | " truth_polys = [geom for geom in df_truth[df_truth['condition'] == truth_cat].geometry]\n", 293 | " truth_index = create_rtree_from_poly(truth_polys)\n", 294 | " scores = evalfunction(grid_num,test_polys, truth_polys, truth_index=truth_index)\n", 295 | " print(truth_cat)\n", 296 | " print(scores[0],precision_recall(*scores[0][1:]))" 297 | ] 298 | }, 299 | { 300 | "cell_type": "code", 301 | "execution_count": null, 302 | "metadata": {}, 303 | "outputs": [], 304 | "source": [ 305 | "test_polys = [geom for geom in df_test.geometry]\n", 306 | "truth_polys = [geom for geom in df_truth.geometry]\n", 307 | "truth_index = create_rtree_from_poly(truth_polys)" 308 | ] 309 | }, 310 | { 311 | "cell_type": "code", 312 | "execution_count": null, 313 | "metadata": {}, 314 | "outputs": [], 315 | "source": [ 316 | "scores = evalfunction(grid_num,test_polys, truth_polys, truth_index=truth_index)\n", 317 | "scores[0],precision_recall(*scores[0][1:])" 318 | ] 319 | } 320 | ], 321 | "metadata": { 322 | "kernelspec": { 323 | "display_name": "Python [conda env:fastai1] *", 324 | "language": "python", 325 | "name": "conda-env-fastai1-py" 326 | }, 327 | "language_info": { 328 | "codemirror_mode": { 329 | "name": "ipython", 330 | "version": 3 331 | }, 332 | "file_extension": ".py", 333 | "mimetype": "text/x-python", 334 | "name": "python", 335 | "nbconvert_exporter": "python", 336 | "pygments_lexer": "ipython3", 337 | "version": "3.7.1" 338 | } 339 | }, 340 | "nbformat": 4, 341 | "nbformat_minor": 2 342 | } 343 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Mozilla Public License Version 2.0 2 | ================================== 3 | 4 | 1. Definitions 5 | -------------- 6 | 7 | 1.1. "Contributor" 8 | means each individual or legal entity that creates, contributes to 9 | the creation of, or owns Covered Software. 10 | 11 | 1.2. "Contributor Version" 12 | means the combination of the Contributions of others (if any) used 13 | by a Contributor and that particular Contributor's Contribution. 14 | 15 | 1.3. "Contribution" 16 | means Covered Software of a particular Contributor. 17 | 18 | 1.4. "Covered Software" 19 | means Source Code Form to which the initial Contributor has attached 20 | the notice in Exhibit A, the Executable Form of such Source Code 21 | Form, and Modifications of such Source Code Form, in each case 22 | including portions thereof. 23 | 24 | 1.5. "Incompatible With Secondary Licenses" 25 | means 26 | 27 | (a) that the initial Contributor has attached the notice described 28 | in Exhibit B to the Covered Software; or 29 | 30 | (b) that the Covered Software was made available under the terms of 31 | version 1.1 or earlier of the License, but not also under the 32 | terms of a Secondary License. 33 | 34 | 1.6. "Executable Form" 35 | means any form of the work other than Source Code Form. 36 | 37 | 1.7. "Larger Work" 38 | means a work that combines Covered Software with other material, in 39 | a separate file or files, that is not Covered Software. 40 | 41 | 1.8. "License" 42 | means this document. 43 | 44 | 1.9. "Licensable" 45 | means having the right to grant, to the maximum extent possible, 46 | whether at the time of the initial grant or subsequently, any and 47 | all of the rights conveyed by this License. 48 | 49 | 1.10. "Modifications" 50 | means any of the following: 51 | 52 | (a) any file in Source Code Form that results from an addition to, 53 | deletion from, or modification of the contents of Covered 54 | Software; or 55 | 56 | (b) any new file in Source Code Form that contains any Covered 57 | Software. 58 | 59 | 1.11. "Patent Claims" of a Contributor 60 | means any patent claim(s), including without limitation, method, 61 | process, and apparatus claims, in any patent Licensable by such 62 | Contributor that would be infringed, but for the grant of the 63 | License, by the making, using, selling, offering for sale, having 64 | made, import, or transfer of either its Contributions or its 65 | Contributor Version. 66 | 67 | 1.12. "Secondary License" 68 | means either the GNU General Public License, Version 2.0, the GNU 69 | Lesser General Public License, Version 2.1, the GNU Affero General 70 | Public License, Version 3.0, or any later versions of those 71 | licenses. 72 | 73 | 1.13. "Source Code Form" 74 | means the form of the work preferred for making modifications. 75 | 76 | 1.14. "You" (or "Your") 77 | means an individual or a legal entity exercising rights under this 78 | License. For legal entities, "You" includes any entity that 79 | controls, is controlled by, or is under common control with You. For 80 | purposes of this definition, "control" means (a) the power, direct 81 | or indirect, to cause the direction or management of such entity, 82 | whether by contract or otherwise, or (b) ownership of more than 83 | fifty percent (50%) of the outstanding shares or beneficial 84 | ownership of such entity. 85 | 86 | 2. License Grants and Conditions 87 | -------------------------------- 88 | 89 | 2.1. Grants 90 | 91 | Each Contributor hereby grants You a world-wide, royalty-free, 92 | non-exclusive license: 93 | 94 | (a) under intellectual property rights (other than patent or trademark) 95 | Licensable by such Contributor to use, reproduce, make available, 96 | modify, display, perform, distribute, and otherwise exploit its 97 | Contributions, either on an unmodified basis, with Modifications, or 98 | as part of a Larger Work; and 99 | 100 | (b) under Patent Claims of such Contributor to make, use, sell, offer 101 | for sale, have made, import, and otherwise transfer either its 102 | Contributions or its Contributor Version. 103 | 104 | 2.2. Effective Date 105 | 106 | The licenses granted in Section 2.1 with respect to any Contribution 107 | become effective for each Contribution on the date the Contributor first 108 | distributes such Contribution. 109 | 110 | 2.3. Limitations on Grant Scope 111 | 112 | The licenses granted in this Section 2 are the only rights granted under 113 | this License. No additional rights or licenses will be implied from the 114 | distribution or licensing of Covered Software under this License. 115 | Notwithstanding Section 2.1(b) above, no patent license is granted by a 116 | Contributor: 117 | 118 | (a) for any code that a Contributor has removed from Covered Software; 119 | or 120 | 121 | (b) for infringements caused by: (i) Your and any other third party's 122 | modifications of Covered Software, or (ii) the combination of its 123 | Contributions with other software (except as part of its Contributor 124 | Version); or 125 | 126 | (c) under Patent Claims infringed by Covered Software in the absence of 127 | its Contributions. 128 | 129 | This License does not grant any rights in the trademarks, service marks, 130 | or logos of any Contributor (except as may be necessary to comply with 131 | the notice requirements in Section 3.4). 132 | 133 | 2.4. Subsequent Licenses 134 | 135 | No Contributor makes additional grants as a result of Your choice to 136 | distribute the Covered Software under a subsequent version of this 137 | License (see Section 10.2) or under the terms of a Secondary License (if 138 | permitted under the terms of Section 3.3). 139 | 140 | 2.5. Representation 141 | 142 | Each Contributor represents that the Contributor believes its 143 | Contributions are its original creation(s) or it has sufficient rights 144 | to grant the rights to its Contributions conveyed by this License. 145 | 146 | 2.6. Fair Use 147 | 148 | This License is not intended to limit any rights You have under 149 | applicable copyright doctrines of fair use, fair dealing, or other 150 | equivalents. 151 | 152 | 2.7. Conditions 153 | 154 | Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted 155 | in Section 2.1. 156 | 157 | 3. Responsibilities 158 | ------------------- 159 | 160 | 3.1. Distribution of Source Form 161 | 162 | All distribution of Covered Software in Source Code Form, including any 163 | Modifications that You create or to which You contribute, must be under 164 | the terms of this License. You must inform recipients that the Source 165 | Code Form of the Covered Software is governed by the terms of this 166 | License, and how they can obtain a copy of this License. You may not 167 | attempt to alter or restrict the recipients' rights in the Source Code 168 | Form. 169 | 170 | 3.2. Distribution of Executable Form 171 | 172 | If You distribute Covered Software in Executable Form then: 173 | 174 | (a) such Covered Software must also be made available in Source Code 175 | Form, as described in Section 3.1, and You must inform recipients of 176 | the Executable Form how they can obtain a copy of such Source Code 177 | Form by reasonable means in a timely manner, at a charge no more 178 | than the cost of distribution to the recipient; and 179 | 180 | (b) You may distribute such Executable Form under the terms of this 181 | License, or sublicense it under different terms, provided that the 182 | license for the Executable Form does not attempt to limit or alter 183 | the recipients' rights in the Source Code Form under this License. 184 | 185 | 3.3. Distribution of a Larger Work 186 | 187 | You may create and distribute a Larger Work under terms of Your choice, 188 | provided that You also comply with the requirements of this License for 189 | the Covered Software. If the Larger Work is a combination of Covered 190 | Software with a work governed by one or more Secondary Licenses, and the 191 | Covered Software is not Incompatible With Secondary Licenses, this 192 | License permits You to additionally distribute such Covered Software 193 | under the terms of such Secondary License(s), so that the recipient of 194 | the Larger Work may, at their option, further distribute the Covered 195 | Software under the terms of either this License or such Secondary 196 | License(s). 197 | 198 | 3.4. Notices 199 | 200 | You may not remove or alter the substance of any license notices 201 | (including copyright notices, patent notices, disclaimers of warranty, 202 | or limitations of liability) contained within the Source Code Form of 203 | the Covered Software, except that You may alter any license notices to 204 | the extent required to remedy known factual inaccuracies. 205 | 206 | 3.5. Application of Additional Terms 207 | 208 | You may choose to offer, and to charge a fee for, warranty, support, 209 | indemnity or liability obligations to one or more recipients of Covered 210 | Software. However, You may do so only on Your own behalf, and not on 211 | behalf of any Contributor. You must make it absolutely clear that any 212 | such warranty, support, indemnity, or liability obligation is offered by 213 | You alone, and You hereby agree to indemnify every Contributor for any 214 | liability incurred by such Contributor as a result of warranty, support, 215 | indemnity or liability terms You offer. You may include additional 216 | disclaimers of warranty and limitations of liability specific to any 217 | jurisdiction. 218 | 219 | 4. Inability to Comply Due to Statute or Regulation 220 | --------------------------------------------------- 221 | 222 | If it is impossible for You to comply with any of the terms of this 223 | License with respect to some or all of the Covered Software due to 224 | statute, judicial order, or regulation then You must: (a) comply with 225 | the terms of this License to the maximum extent possible; and (b) 226 | describe the limitations and the code they affect. Such description must 227 | be placed in a text file included with all distributions of the Covered 228 | Software under this License. Except to the extent prohibited by statute 229 | or regulation, such description must be sufficiently detailed for a 230 | recipient of ordinary skill to be able to understand it. 231 | 232 | 5. Termination 233 | -------------- 234 | 235 | 5.1. The rights granted under this License will terminate automatically 236 | if You fail to comply with any of its terms. However, if You become 237 | compliant, then the rights granted under this License from a particular 238 | Contributor are reinstated (a) provisionally, unless and until such 239 | Contributor explicitly and finally terminates Your grants, and (b) on an 240 | ongoing basis, if such Contributor fails to notify You of the 241 | non-compliance by some reasonable means prior to 60 days after You have 242 | come back into compliance. Moreover, Your grants from a particular 243 | Contributor are reinstated on an ongoing basis if such Contributor 244 | notifies You of the non-compliance by some reasonable means, this is the 245 | first time You have received notice of non-compliance with this License 246 | from such Contributor, and You become compliant prior to 30 days after 247 | Your receipt of the notice. 248 | 249 | 5.2. If You initiate litigation against any entity by asserting a patent 250 | infringement claim (excluding declaratory judgment actions, 251 | counter-claims, and cross-claims) alleging that a Contributor Version 252 | directly or indirectly infringes any patent, then the rights granted to 253 | You by any and all Contributors for the Covered Software under Section 254 | 2.1 of this License shall terminate. 255 | 256 | 5.3. In the event of termination under Sections 5.1 or 5.2 above, all 257 | end user license agreements (excluding distributors and resellers) which 258 | have been validly granted by You or Your distributors under this License 259 | prior to termination shall survive termination. 260 | 261 | ************************************************************************ 262 | * * 263 | * 6. Disclaimer of Warranty * 264 | * ------------------------- * 265 | * * 266 | * Covered Software is provided under this License on an "as is" * 267 | * basis, without warranty of any kind, either expressed, implied, or * 268 | * statutory, including, without limitation, warranties that the * 269 | * Covered Software is free of defects, merchantable, fit for a * 270 | * particular purpose or non-infringing. The entire risk as to the * 271 | * quality and performance of the Covered Software is with You. * 272 | * Should any Covered Software prove defective in any respect, You * 273 | * (not any Contributor) assume the cost of any necessary servicing, * 274 | * repair, or correction. This disclaimer of warranty constitutes an * 275 | * essential part of this License. No use of any Covered Software is * 276 | * authorized under this License except under this disclaimer. * 277 | * * 278 | ************************************************************************ 279 | 280 | ************************************************************************ 281 | * * 282 | * 7. Limitation of Liability * 283 | * -------------------------- * 284 | * * 285 | * Under no circumstances and under no legal theory, whether tort * 286 | * (including negligence), contract, or otherwise, shall any * 287 | * Contributor, or anyone who distributes Covered Software as * 288 | * permitted above, be liable to You for any direct, indirect, * 289 | * special, incidental, or consequential damages of any character * 290 | * including, without limitation, damages for lost profits, loss of * 291 | * goodwill, work stoppage, computer failure or malfunction, or any * 292 | * and all other commercial damages or losses, even if such party * 293 | * shall have been informed of the possibility of such damages. This * 294 | * limitation of liability shall not apply to liability for death or * 295 | * personal injury resulting from such party's negligence to the * 296 | * extent applicable law prohibits such limitation. Some * 297 | * jurisdictions do not allow the exclusion or limitation of * 298 | * incidental or consequential damages, so this exclusion and * 299 | * limitation may not apply to You. * 300 | * * 301 | ************************************************************************ 302 | 303 | 8. Litigation 304 | ------------- 305 | 306 | Any litigation relating to this License may be brought only in the 307 | courts of a jurisdiction where the defendant maintains its principal 308 | place of business and such litigation shall be governed by laws of that 309 | jurisdiction, without reference to its conflict-of-law provisions. 310 | Nothing in this Section shall prevent a party's ability to bring 311 | cross-claims or counter-claims. 312 | 313 | 9. Miscellaneous 314 | ---------------- 315 | 316 | This License represents the complete agreement concerning the subject 317 | matter hereof. If any provision of this License is held to be 318 | unenforceable, such provision shall be reformed only to the extent 319 | necessary to make it enforceable. Any law or regulation which provides 320 | that the language of a contract shall be construed against the drafter 321 | shall not be used to construe this License against a Contributor. 322 | 323 | 10. Versions of the License 324 | --------------------------- 325 | 326 | 10.1. New Versions 327 | 328 | Mozilla Foundation is the license steward. Except as provided in Section 329 | 10.3, no one other than the license steward has the right to modify or 330 | publish new versions of this License. Each version will be given a 331 | distinguishing version number. 332 | 333 | 10.2. Effect of New Versions 334 | 335 | You may distribute the Covered Software under the terms of the version 336 | of the License under which You originally received the Covered Software, 337 | or under the terms of any subsequent version published by the license 338 | steward. 339 | 340 | 10.3. Modified Versions 341 | 342 | If you create software not governed by this License, and you want to 343 | create a new license for such software, you may create and use a 344 | modified version of this License if you rename the license and remove 345 | any references to the name of the license steward (except to note that 346 | such modified license differs from this License). 347 | 348 | 10.4. Distributing Source Code Form that is Incompatible With Secondary 349 | Licenses 350 | 351 | If You choose to distribute Source Code Form that is Incompatible With 352 | Secondary Licenses under the terms of this version of the License, the 353 | notice described in Exhibit B of this License must be attached. 354 | 355 | Exhibit A - Source Code Form License Notice 356 | ------------------------------------------- 357 | 358 | This Source Code Form is subject to the terms of the Mozilla Public 359 | License, v. 2.0. If a copy of the MPL was not distributed with this 360 | file, You can obtain one at http://mozilla.org/MPL/2.0/. 361 | 362 | If it is not possible or desirable to put the notice in a particular 363 | file, then You may include the notice in a location (such as a LICENSE 364 | file in a relevant directory) where a recipient would be likely to look 365 | for such a notice. 366 | 367 | You may add additional accurate notices of copyright ownership. 368 | 369 | Exhibit B - "Incompatible With Secondary Licenses" Notice 370 | --------------------------------------------------------- 371 | 372 | This Source Code Form is "Incompatible With Secondary Licenses", as 373 | defined by the Mozilla Public License, v. 2.0. 374 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Satellite-Image-Building-Segmentation 2 | Building Segmentation from Fused Satellite and Aerial Imagery Datasets using U-Net in FastAI 3 | 4 | Dataset: https://drive.google.com/open?id=1Nn-wFtBpFUahePDghGoe_A8Qtm8MG7aN 5 | --------------------------------------------------------------------------------