Rio de Janeiro

Rio de Janeiro

SpaceNet AOI 1 – Rio de Janeiro

Catalog ID: ??
Image Time: ??

Download Instructions

To view the contents of the dataset

aws s3 ls s3://spacenet-dataset/AOIs/AOI_1_Rio/

SpaceNet Simple Storage Service (S3) Directory Structure (AOI 1)

-- AOI_1_Rio
    |-- processedData
    |   -- processedBuildingLabels.tar.gz  # Compressed 3band and 8band 200m x 200m tiles with associated building foot print labels                                 # This dataset is the Training Dataset for the first Top Coder Competition
    `-- srcData
        |-- rasterData
        |   |-- 3-Band.tar.gz # 3band (RGB) Raster Mosaic for Rio De Jenairo area (2784 sq KM) collected by WorldView-2
        |    -- 8-Band.tar.gz # 8band Raster Mosaic for Rio De Jenairo area (2784 sq KM) collected by WorldView-2
         -- vectorData
            |-- Rio_BuildingLabels.tar.gz # Source Dataset that contains Building the building foot prints traced from the Mosaic
            |-- Rio_HGIS_Metro.gdb.tar.gz  # Source Point of Interest Dataset in GeoDatabase Format.  Best if Used with ESRI
             -- Rio_HGIS_Metro_extract.tar # Source Point of Interest Dataset in GeoJSON with associated .jpg.  Easy to Use without ESRI toolset

Rio de Janeiro // Dataset Resources

To download processed 200mx200m tiles of AOI 1 (3.4 GB) with associated building footprints do the following:

aws s3 cp s3://spacenet-dataset/AOIs/AOI_1_Rio/processedData/processedBuildingLabels.tar.gz .

To download the Source Imagery Mosaic (3-band = 2.3 GB and 8-band = 6.5 GB):

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_3band.tar.gz .
aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_8band.tar.gz .

To download the Source Vector Data (0.18 GB):

aws s3 cp s3://spacenet-dataset/spacenet/SN1_buildings/tarballs/SN1_buildings_train_AOI_1_Rio_geojson_buildings.tar.gz .

Point of Interest Dataset in ESRI GeoDatabase Form (31 GB)

Point of Interest Dataset Extracted into GeoJSONs with associated .jpg (29 GB)