Spacenet buses download#

regular) plantation, cropland, orchards, vineyards, nurseries, and ornamental horticultural areas confined feeding operations. Agriculture land: 255,255,0 - Farms, any planned (i.e.Urban land: 0,255,255 - Man-made, built up areas with human artifacts (can ignore roads for now which is hard to label).

The mask is a RGB image with 7 classes of labels, using color-coding (R, G, B) as follows. Testing satellite images will be will be uploaded later.Įach satellite image is paired with a mask image for land cover annotation.

Spacenet buses download#

You can download the training data in the download page with filetype of “Starting Kit”.The imagery has 50cm pixel resolution, collected by DigitalGlobe’s satellite.The training data for Land Cover Challenge contains 803 satellite imagery in RGB, size 2448x2448.MultipolygonWKT - the labeled area, which is multipolygon geometry represented in WKT format.Sample_submission.csv - a sample submission file in the correct format Vehicle Small - small vehicle (car, van), motorbike.Crops - contour ploughing/cropland, grain (wheat) crops, row (potatoes, turnips) crops.Trees - woodland, hedgerows, groups of trees, standalone trees.Track - poor/dirt/cart track, footpath/trail.Buildings - large building, residential, non-residential, fuel storage facility, fortified building.Panchromatic and multispectral : 11-bits per pixel.8 Multispectral: (red, red edge, coastal, blue, green, yellow, near-IR1 and near-IR2) 400 nm - 1040 nm.│ └── summaryData # Contains CSV with pixel based labels for each building in the Tile Set.ģ Dstl Satellite Imagery Feature Detection │ ├── RGB-PanSharpen # Contains Tiles of RGB raster data from Worldview-3 │ ├── PAN # Contains Tiles of Panchromatic raster data from Worldview-3 │ ├── MUL-PanSharpen # Contains Tiles of 8-Band Multi-Spectral raster data pansharpened to 0.3m │ ├── MUL # Contains Tiles of 8-Band Multi-Spectral raster data from WorldView-3 │ │ └── buildings # Contains GeoJson labels of buildings for each tile 一个图像分割算法库： segmentation_models 2 AOI_4_Shanghai_Train 5.2 2D Semantic Labeling - Vaihingen data.5.1 2D Semantic Labeling Contest - Potsdam.3 Dstl Satellite Imagery Feature Detection.