![]() Sample ground truth GeoJSON label, with a mask half-width of 2 meters. # SN3 python /path/to/cresi/cresi/data_prep/create_8bit_images.py \ - indir=/path/to/data/SN3_roads/AOI_4_Shanghai/PS-MS \ - outdir=/path/to/data/cresi_data/8bit/PS-RGB - rescale_type=perc \ - percentiles=2,98 \ - band_order=5,3,2 # SN5 python /path/to/cresi/cresi/data_prep/create_8bit_images.py \ - indir=/path/to/data/SN5_roads/AOI_7_Moscow/PS-MS \ - outdir=/path/to/data/cresi_data/8bit/PS-RGB \ - rescale_type=perc \ - percentiles=2,98 \ - band_order=5,3,2įigure 2. The script below should be run for all 6 training areas of interest (AOIs): AOI_2_Vegas, AOI_3_Paris, AOI_4_Shanghai, AOI_5_Khartoum, AOI_7_Moscow, AOI_8_Mumbai. In this example, we rescale the image to the 2nd and 98th percentile of pixel values when converting to 8-bit. This is accomplished via the create_8bit_images.py script. While we lose a significant amount of information by utilizing only a subset of the multispectral bands, for ease of exploration we extract the 8-bit RGB imagery from the 16-bit multispectral imagery, where RGB corresponds to bands 5, 3, 2, respectively. The pan-sharpened 8-band multispectral images (PS-MS) are prepared the same for both challenges, so we will utilize this data for training and testing. In SpaceNet 5 the RGB 3-band pan-sharpened imagery utilized the Maxar DRA (Dynamic Range Adjusted) product which seeks to equalize color scales, and yields an 8-bit image. The RGB 3-band pan-sharpened imagery (PS-RGB) for SpaceNet 3 was distributed in the native 16-bit data format. SpaceNet 3 and SpaceNet 5 data formats are slightly different, due to extra post-processing performed on the SpaceNet 5 imagery. Attach docker container: docker attach cresi_container 3. Create docker container: nvidia-docker run -it - rm -ti - ipc=host - name cresi_container cresi_imageĭ. Build docker image: cd /path/to/cresi/docker nvidia-docker build - no-cache -t cresi_image. Download: cd /path/to/cresi/ git clone ī. All CRESI commands should be run within this docker container.Ī. To run CRESI, you will need docker (ideally the nvidia-docker version) installed on a GPU-enabled machine. The City-scale Road Extraction from Satellite Imagery ( CRESI) framework was designed to extract roads and speed estimates at large scale, but works equally well on the smaller image chips of the SpaceNet 5 Challenge. aws s3 cp s3://spacenet-dataset/spacenet/SN5_roads/tarballs/SN5_roads_train_AOI_7_ /path/to/data 2. #SPACENET 5 DOWNLOAD#An example download command is shown below (see spacenet.ai for further instructions). To begin with, we’ll download data for both SpaceNet 3 and SpaceNet 5. Data AccessĪccessing SpaceNet data is free, and only requires the creation of an AWS account. Code to reproduce the processes detailed below is available in our CRESI github repository. In support of this rather complex challenge, this post walks readers through the steps necessary to prepare the data for the first step in our baseline: creating training masks for a deep learning segmentation model. There is still plenty of time to get involved with the SpaceNet 5 Challenge that seeks to determine route travel times along roadways directly from satellite imagery. SpaceNet is run in collaboration with CosmiQ Works, Maxar Technologies, Intel AI, Amazon Web Services (AWS), Capella Space, and Topcoder. building footprint
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