Google Earth Engine (GEE)——张量流水灾模型数据集（Sentinel-1）

Mayer, T., Poortinga, A., Bhandari, B., Nicolau, A.P., Markert, K., Thwal, N.S., Markert, A., Haag, A., Kilbride, J., Chishtie, F. and Wadhwa, A.,
2021. Deep Learning approach for Sentinel-1 Surface Water Mapping leveraging Google Earth Engine. ISPRS Open Journal of Photogrammetry and Remote
Sensing, p.100005.

var HydraFloods_Deep_Learning_Output = ee.Image("users/tjm0042/Hydrafloods_Outputs/TensorFlow_Surface_Water_Model_Mosaic")
Map.setCenter(104.9614, 12.2642,9);
var palettes = require('users/gena/packages:palettes');
Map.addLayer(HydraFloods_Deep_Learning_Output,{min: 0,max: 1, palette: palettes.cmocean.Tempo[7]},
"HYDRAFloods TensorFlow Model Approach: Joint Research Centre Adjusted Learning Rate Binary Cross Entropy Dice")
//Add a grayscale map to help higlight the feature
var SubtleGrayscale
 = 
[
  {
    "featureType": "administrative",
    "elementType": "all",
    "stylers": [
      {
        "saturation": "-100"
      }
    ]
  },
  {
    "featureType": "administrative.province",
    "elementType": "all",
    "stylers": [
      {
        "visibility": "off"
      }
    ]
  },
  {
    "featureType": "landscape",
    "elementType": "all",
    "stylers": [
      {
        "saturation": -100
      },
      {
        "lightness": 65
      },
      {
        "visibility": "on"
      }
    ]
  },
  {
    "featureType": "poi",
    "elementType": "all",
    "stylers": [
      {
        "saturation": -100
      },
      {
        "lightness": "50"
      },
      {
        "visibility": "simplified"
      }
    ]
  },
  {
    "featureType": "road",
    "elementType": "all",
    "stylers": [
      {
        "saturation": "-100"
      }
    ]
  },
  {
    "featureType": "road.highway",
    "elementType": "all",
    "stylers": [
      {
        "visibility": "simplified"
      }
    ]
  },
  {
    "featureType": "road.arterial",
    "elementType": "all",
    "stylers": [
      {
        "lightness": "30"
      }
    ]
  },
  {
    "featureType": "road.local",
    "elementType": "all",
    "stylers": [
      {
        "lightness": "40"
      }
    ]
  },
  {
    "featureType": "transit",
    "elementType": "all",
    "stylers": [
      {
        "saturation": -100
      },
      {
        "visibility": "simplified"
      }
    ]
  },
  {
    "featureType": "water",
    "elementType": "geometry",
    "stylers": [
      {
        "hue": "#ffff00"
      },
      {
        "lightness": -25
      },
      {
        "saturation": -97
      }
    ]
  },
  {
    "featureType": "water",
    "elementType": "labels",
    "stylers": [
      {
        "lightness": -25
      },
      {
        "saturation": -100
      }
    ]
  }
]
Map.setOptions('SubtleGrayscale', {SubtleGrayscale: SubtleGrayscale})

# content of example.py Python file
# import the hydrafloods and ee package
import hydrafloods as hf
import ee
ee.Initialize()
# specify start and end time as well as geographic region to process
start_time = "2019-10-05"
end_time =  "2019-10-06"
region = ee.Geometry.Rectangle([104, 11.5, 106, 12.5 ])
# get the Sentinel-1 collection
# the hf.dataset classes performs the spatial-temporal filtering for you
s1 = hf.datasets.Sentinel1(region, start_time, end_time)
# apply a water mapping function to the S1 dataset
# this applies the "Edge Otsu" algorithm from https://doi.org/10.3390/rs12152469
water_imgs = s1.apply_func(
    hf.thresholding.edge_otsu,
    initial_threshold=-14,
    edge_buffer=300
)
# take the mode from multiple images
# since this is just imagery from one day, it will simply mosaic the images
water_map = ee.Image(water_imgs.collection.mode())
# export the water map
hf.geeutils.export_image(
    water_map,
    region,
    "users/<YOUR_USERNAME>/water_map_example",
    scale=30,
)