Tensor Flow Hydra Flood Models
这个数据集是水文遥感分析洪水（HYDRAFloods）系统的地表水输出图像，利用深度学习TensorFlow方法。具体来说，这个联合研究中心（JRC）调整后的学习率二元交叉熵（BCE）Dice模型和方法在最近的利用谷歌地球引擎的Sentinel-1地表水制图的深度学习方法中得到了详细讨论。
洪水的水文遥感分析（或HYDRAFloods）是一个开源的Python应用程序，用于下载、处理和提供来自遥感数据的地表水地图。该工具背后的基础是提供与传感器无关的方法来制作地表水地图。此外，还有一些工作流程，结合利用多个遥感数据集，为洪水应用提供每日地表水地图。
HYDRAFloods应用程序是使用谷歌地球引擎和谷歌云平台（GCP）建立的，以利用云计算进行大规模计算和处理高数据量输出。该软件包的目标是让用户以最小的努力获得高质量、基于云的地表水绘图算法。为了实现这一目标，hydrafloods在地球引擎Python API的基础上提供了一个高级API，以减少代码的重复，如过滤或携带元数据进行图像处理，并提供复杂的地表水算法。此外，该软件包还提供了一些GCP功能，以读取和传输数据，在地球引擎内使用。
快速启动
为了突出 hydrafloods API 的一个快速例子和制作高质量地表水地图的简单性，我们提供了一个快速的例子，在柬埔寨的湄公河和洞里萨河的汇合处使用哨兵-1绘制地表水，那里经常发生洪水。
文献：
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.
关于HYDRAFloods开源Python应用程序的更多细节，用于下载、处理和提供从遥感数据得到的地表水地图。请参见HYDRAFloods文档。 HYDRAFloods Documentation.
代码：
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})
代码链接：
代码：
# 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, )
License¶
This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Curated by: Tim Mayer, Kel Markert, Biplov Bhandari, Ate Poortinga
Keywords: Surface Water Mapping, Floods, Deep Learning TensorFlow, SERVIR
Last updated: 2021-10-20
