Datasets:

iwcyou
/

Shenzhen_SDF

license: cc-by-nc-sa-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: mask
      dtype: image
    - name: sdf_mask
      dtype:
        array2_d:
          shape:
            - 1024
            - 1024
          dtype: float32
    - name: road_network
      dtype: image
    - name: gps
      sequence:
        - name: id
          dtype: string
        - name: longitude
          dtype: float32
        - name: latitude
          dtype: float32
        - name: timestamp
          dtype: string
        - name: speed
          dtype: float32
        - name: direction
          dtype: float32
    - name: filename
      dtype: string
  splits:
    - name: train
      num_bytes: 5327869524
      num_examples: 614
    - name: test
      num_bytes: 1221907723
      num_examples: 152
  download_size: 3125871408
  dataset_size: 6549777247
tags:
  - Satellite_iamge
  - Remote_sensing
  - GPS_data
  - Multi-modal

SDF-Guided Multi-modal Big Data Road Extraction

The source code about special session of PAKDD 2025 paper "SDF-Guided Multi-modal Big Data Road Extraction"

Usage

pip install -r requirement.txt # install the dependency library

source tr_sz_sdf.sh # run code

Shenzhen dataset

All of our Shenzhen dataset is based on the web Mercator projection in the GCJ-02 coordinate system.

Dataset description

train_val/
- image/: contains 614 satellite images (x_y_sat.png )
- mask/: contains 614 binary mask images (x_y_mask.png )
- mask_sdf_T/: contains 614 SDF mask images (x_y_mask.npy )
- road_network/ :contains 614 road network images (x_y_mask.png )
test/
- image/: contains 152 satellite images (x_y_sat.png )
- mask/: contains 152 mask images (x_y_mask.png )
- mask_sdf_T/: contains 152 SDF mask images (x_y_mask.npy )
- road_network/ :contains 152 road network images (x_y_mask.png )
GPS/
- taxi/: contains 766 GPS patch files (x_y_gps.pkl). Each stores the GPS records located in the area of input image x_y_sat.png
coordinates/: contains x_y_gps.txt (web Mercator GCJ-02 format) files, (left up corner, right down corner) <- format

Each input image image/x_y_sat.png is a RGB satellite image of 1024 $\times$ 1024 pixel size. Its corresponding GPS data is stored in file /GPS/patch/x_y_gps.pkl, and corresponding mask image is mask/x_y_mask.png.

GPS Data

To save the loading time, we publish the dataset in Python's Pickle format, which can be directly loaded like:

import pandas
import pickle
gps_data = pickle.load(open('dataset_sz_sdf/GPS/taxi/0_6_gps.pkl', 'rb'))

Definition of columns:

id: Vehicle ID (integer)
time: Timestamp (UNIX format, in second)
lat: Latitude (in pixel coordinate)
lon: Longitude (in pixel coordinate)
direction: Heading (in degree, 0 means the vehicle is heading north or isn't moving)
speed: Speed (in meter per minute)
time: The time stamp.

The lat/lon are in the gcj02 System.

Range of sampling

Coordinate Range of satellite images in Nanshan district

wgs84 format：
Top left corner：113.77477269727868, 22.658708423462986
Lower right corner：114.01655951201688, 22.401131313831055

web Mercator on GCJ-02 format：
TLC：12665921.334966816,2590450.8885846175
LRC：12692827.1689232 ,2559417.4551008344

Coordinate Range of road networks in Nanshan district

wgs84 format：
TLC：113.72531536623958, 22.676333371889751640059225977739
LRC：114.07037282840729, 22.352754460489630359940774022261

web Mercator on GCJ-02 format：
TLC：12660417.89499784 , 2592578.6326045664
LRC：12698827.572095804, 2553607.944832368

Coordinate range of train(satellite) ：

wgs84 format:
TLC：113.77477269727868, 22.658708423462986
LRC：114.01655951201688, 22.52994959856712

web Mercator on GCJ-02 format：
TLC：12665921.334966816, 2590450.8885846175
LRC：12692827.1689232 , 2574934.17184272595

Coordinate range of test(satellite) ：

wgs84 format：
TLC：113.77477269727868, 22.52994959856712
LRC：114.01655951201688, 22.465558186041523

web Mercator on GCJ-02 format：
TLC：12665921.334966816, 2574934.17184272595
LRC：12692827.1689232 , 2567175.813471780175

Coordinate range of Nanshan road network (overbold version)：

web Mercator on GCJ-02 format：
TLC：12660417.89499784 , 2592493.9833760057
LRC：12698658.366469823, 2553607.944832368