1 dfc2019 track1
-  - Ground
-  - High Vegetation / Trees
-  - Building Roof
-  - Water
-  - Elevated Road / Bridge
-  - Unlabeled
SpaceNet 数据集 https://spacenetchallenge.github.io/
│ ├── geojson
│ │ └── buildings # Contains GeoJson labels of buildings for each tile
│ ├── MUL # Contains Tiles of 8-Band Multi-Spectral raster data from WorldView-3
│ ├── MUL-PanSharpen # Contains Tiles of 8-Band Multi-Spectral raster data pansharpened to 0.3m
│ ├── PAN # Contains Tiles of Panchromatic raster data from Worldview-3
│ ├── RGB-PanSharpen # Contains Tiles of RGB raster data from Worldview-3
│ └── summaryData # Contains CSV with pixel based labels for each building in the Tile Set.
3 Dstl Satellite Imagery Feature Detection
- Buildings - large building, residential, non-residential, fuel storage facility, fortified building
- Misc. Manmade structures
- Track - poor/dirt/cart track, footpath/trail
- Trees - woodland, hedgerows, groups of trees, standalone trees
- Crops - contour ploughing/cropland, grain (wheat) crops, row (potatoes, turnips) crops
- Standing water
- Vehicle Large - large vehicle (e.g. lorry, truck,bus), logistics vehicle
- Vehicle Small - small vehicle (car, van), motorbike
数据集：RGB 语义分割 6+1种类别
- The training data for Land Cover Challenge contains 803 satellite imagery in RGB, size 2448x2448.
- The imagery has 50cm pixel resolution, collected by DigitalGlobe’s satellite.
- You can download the training data in the download page with filetype of “Starting Kit”. Testing satellite images will be will be uploaded later.
Each satellite image is paired with a mask image for land cover annotation. The mask is a RGB image with 7 classes of labels, using color-coding (R, G, B) as follows.
- Urban land: 0,255,255 - Man-made, built up areas with human artifacts (can ignore roads for now which is hard to label)
- Agriculture land: 255,255,0 - Farms, any planned (i.e. regular) plantation, cropland, orchards, vineyards, nurseries, and ornamental horticultural areas; confined feeding operations.
- Rangeland: 255,0,255 - Any non-forest, non-farm, green land, grass
- Forest land: 0,255,0 - Any land with x% tree crown density plus clearcuts.
- Water: 0,0,255 - Rivers, oceans, lakes, wetland, ponds.
- Barren land: 255,255,255 - Mountain, land, rock, dessert, beach, no vegetation
- Unknown: 0,0,0 - Clouds and others
File names for satellite images and the corresponding mask image are _sat.jpg and _mask.png. is a randomized integer.
5.1 2D Semantic Labeling Contest - Potsdam
The ground sampling distance of both, the TOP and the DSM, is 5 cm. The DSM was generated via dense image matching with Trimble INPHO 5.6 software and Trimble INPHO OrthoVista was used to generate the TOP mosaic. In order to avoid areas without data (“holes”) in the TOP and the DSM, the patches were selected from the central part of the TOP mosaic and none at the boundaries. Remaining (very small) holes in the TOP and the DSM were interpolated.
The TOP come as TIFF files in different channel composistions, where each channel has a spectral resolution of 8bit:
- IRRG: 3 channels (IR-R-G)
- RGB: 3 channels (R-G-B)
- RGBIR: 4 channels (R-G-B-IR)
In this way participants can pick the data needed conveniently.
The DSM are TIFF files with one band; the grey levels (corresponding to the DSM heights) are encoded as 32 bit float values. The TOP and the DSM are defined on the same grid (UTM WGS84). Each tile comes with an affine transformation file (tiff world file) in order to enable a re-composition of images to larger mosaics if desired.
5.2 2D Semantic Labeling - Vaihingen data
The data set contains 33 patches (of different sizes), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, see Figure below and a DSM. For further information about the original input data, please refer to the data description of the object detection and 3d reconstruction benchmark.
SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion：
7 TNO Image Fusion Dataset