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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
#==========================================================#
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# Shoreline extraction from satellite images
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#==========================================================#
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# load modules
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import os
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import pickle
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import warnings
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warnings.filterwarnings("ignore")
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import matplotlib.pyplot as plt
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import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools
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# region of interest (longitude, latitude), can also be loaded from a .kml polygon
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polygon = SDS_tools.coords_from_kml('NARRA.kml')
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#polygon = [[[151.301454, -33.700754],
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# [151.311453, -33.702075],
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# [151.307237, -33.739761],
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# [151.294220, -33.736329],
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# [151.301454, -33.700754]]]
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# date range
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dates = ['2017-12-01', '2018-01-01']
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# satellite missions
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sat_list = ['S2']
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# name of the site
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sitename = 'NARRA'
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# put all the inputs into a dictionnary
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inputs = {
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'polygon': polygon,
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'dates': dates,
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'sat_list': sat_list,
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'sitename': sitename
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}
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# retrieve satellite images from GEE
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metadata = SDS_download.retrieve_images(inputs)
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# if you have already downloaded the images, just load the metadata file
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filepath = os.path.join(os.getcwd(), 'data', sitename)
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with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
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metadata = pickle.load(f)
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#%%
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# settings for the shoreline extraction
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settings = {
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# general parameters:
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'cloud_thresh': 0.2, # threshold on maximum cloud cover
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'output_epsg': 28356, # epsg code of spatial reference system desired for the output
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# [ONLY FOR ADVANCED USERS] shoreline detection parameters:
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'min_beach_area': 4500, # minimum area (in metres^2) for an object to be labelled as a beach
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'buffer_size': 150, # radius (in pixels) of disk for buffer around sandy pixels
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'min_length_sl': 200, # minimum length of shoreline perimeter to be kept
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# quality control:
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'check_detection': True, # if True, shows each shoreline detection and lets the user
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# decide which ones are correct and which ones are false due to
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# the presence of clouds
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# add the inputs
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'inputs': inputs
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}
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# [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling)
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SDS_preprocess.save_jpg(metadata, settings)
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# [OPTIONAL] create a reference shoreline (helps to identify outliers and false detections)
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settings['reference_shoreline'] = SDS_preprocess.get_reference_sl_manual(metadata, settings)
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# set the max distance (in meters) allowed from the reference shoreline for a detected shoreline to be valid
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settings['max_dist_ref'] = 100
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# extract shorelines from all images (also saves output.pkl and output.kml)
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output = SDS_shoreline.extract_shorelines(metadata, settings)
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#%%
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# basic figure plotting the mapped shorelines
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plt.figure()
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plt.axis('equal')
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plt.xlabel('Eastings [m]')
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plt.ylabel('Northings [m]')
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for satname in output.keys():
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if satname == 'meta':
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continue
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for i in range(len(output[satname]['shoreline'])):
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sl = output[satname]['shoreline'][i]
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date = output[satname]['timestamp'][i]
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plt.plot(sl[:, 0], sl[:, 1], '.', label=date.strftime('%d-%m-%Y'))
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plt.legend()
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