def gather_data():
cloud_classifier = get_s2_pixel_cloud_detector(average_over=2, dilation_size=1, all_bands=False)
add_clm = AddCloudMaskTask(cloud_classifier, 'BANDS-S2CLOUDLESS', cm_size_y='80m', cm_size_x='80m',
cmask_feature='CLM', # cloud mask name
cprobs_feature='CLP' # cloud prob. map name
)
ndvi = NormalizedDifferenceIndex('NDVI', 'BANDS/3', 'BANDS/2')
add_sh_valmask = AddValidDataMaskTask(SentinelHubValidData(),
'IS_VALID')
layer = 'BANDS-S2-L1C'
custom_script = 'return [B02, B03];'
input_task = S2L1CWCSInput(layer=layer,
feature=(FeatureType.DATA, 'BANDS'),
custom_url_params={CustomUrlParam.EVALSCRIPT: custom_script},
resx='10m', resy='10m',
maxcc=.8)
add_ndvi = S2L1CWCSInput(layer='NDVI')
save = SaveToDisk('io_example', overwrite_permission=2)#compress_level=1
workflow = LinearWorkflow(
input_task,
add_clm,
add_ndvi,
add_sh_valmask,
)
time_interval = ('2017-01-01', '2017-12-31')
result = workflow.execute({input_task: {'bbox': roi_bbox, 'time_interval': time_interval},
save: {'eopatch_folder': 'eopatch'}})
return list(result.values())[0].data['NDVI'], list(result.values())[-1].mask['IS_VALID'], np.array(list(result.values())[0].timestamp)
def _get_eopatch(self, bbox, time_interval):
# request L2A images
add_l2a = S2L2AWCSInput(self.config.l2a_field,
resx=self.config.resx,
resy=self.config.resy,
maxcc=self.config.maxcc,
time_difference=datetime.timedelta(hours=2))
# filter frames with valid data = 1
filter_task = SimpleFilterTask(
(FeatureType.MASK, 'IS_DATA'), ValidDataFractionPredicate(1),
(FeatureType.DATA, self.config.l2a_field))
# randomly select one temporal frame
sel_task = SelectRandomFrameTask([(FeatureType.DATA,
self.config.l2a_field),
(FeatureType.MASK, 'IS_DATA')])
# request corresponding L1C
add_l1c = S2L1CWCSInput(self.config.l1c_field,
resx=self.config.resx,
resy=self.config.resy,
maxcc=self.config.maxcc,
time_difference=datetime.timedelta(hours=2))
# execute workflow
try:
eop = add_l1c.execute(
sel_task.execute(
filter_task.execute(
add_l2a.execute(time_interval=time_interval,
bbox=bbox))))
executed = True
except (HTTPError, IndexError, DownloadFailedException, RuntimeError,
ValueError, TypeError, NameError):
logging.error("Exception thrown in obtaining EOPatch")
eop = None
executed = False
return eop, executed
def get_elevation(self, bounds):
INSTANCE_ID = '4aaea2ec-3a2c-4e1c-8a51-851e220d0273'
roi = BBox(bbox=bounds, crs=CRS.WGS84)
layer = 'MAPZEN_DEM'
time_interval = ('2019-01-01', '2019-06-01')
add_dem = DEMWCSInput(layer=layer, instance_id=INSTANCE_ID)
input_task = S2L1CWCSInput(layer=layer,
resx='30m',
resy='30m',
instance_id=INSTANCE_ID)
workflow = LinearWorkflow(input_task, add_dem)
result = workflow.execute(
{input_task: {
'bbox': roi,
'time_interval': time_interval
}})
eopatch = list(result.values())[0]
return eopatch
def download_patches(path, shp, bbox_list, indexes):
add_data = S2L1CWCSInput(
layer='BANDS-S2-L1C',
feature=(FeatureType.DATA, 'BANDS'), # save under name 'BANDS'
resx='10m', # resolution x
resy='10m', # resolution y
maxcc=0.8, # maximum allowed cloud cover of original ESA tiles
)
path_out = path + '/Slovenia/'
if not os.path.isdir(path_out):
os.makedirs(path_out)
save = SaveTask(path_out,
overwrite_permission=OverwritePermission.OVERWRITE_PATCH)
workflow = LinearWorkflow(add_data, save)
time_interval = ['2017-01-01',
'2017-12-31'] # time interval for the SH request
execution_args = []
for idx, bbox in zip(indexes, bbox_list[indexes]):
execution_args.append({
add_data: {
'bbox': bbox,
'time_interval': time_interval
},
save: {
'eopatch_folder': 'eopatch_{}'.format(idx)
}
})
start_time = time.time()
executor = EOExecutor(workflow, execution_args, save_logs=True)
executor.run(workers=1, multiprocess=False)
file = open('timing.txt', 'a')
running = str(
dt.datetime.now()) + ' Running time: {}\n'.format(time.time() -
start_time)
print(running)
file.write(running)
file.close()
lulc_cmap = mpl.colors.ListedColormap([entry.color for entry in LULC])
lulc_norm = mpl.colors.BoundaryNorm(np.arange(-0.5, 6, 1), lulc_cmap.N)
#%%
# TASK FOR BAND DATA
# add a request for B(B02), G(B03), R(B04), NIR (B08), SWIR1(B11), SWIR2(B12)
# from default layer 'ALL_BANDS' at 10m resolution
# Here we also do a simple filter of cloudy scenes. A detailed cloud cover
# detection is performed in the next step
custom_script = 'return [B02, B03, B04, B08, B11, B12];'
add_data = S2L1CWCSInput(
layer='BANDS-S2-L1C',
feature=(FeatureType.DATA, 'BANDS'), # save under name 'BANDS'
custom_url_params={CustomUrlParam.EVALSCRIPT:
custom_script}, # custom url for 6 specific bands
resx='10m', # resolution x
resy='10m', # resolution y
maxcc=0.8, # maximum allowed cloud cover of original ESA tiles
)
# TASK FOR CLOUD INFO
# cloud detection is performed at 80m resolution
# and the resulting cloud probability map and mask
# are scaled to EOPatch's resolution
cloud_classifier = get_s2_pixel_cloud_detector(average_over=2,
dilation_size=1,
all_bands=False)
add_clm = AddCloudMaskTask(
cloud_classifier,
'BANDS-S2CLOUDLESS',
# inflate the BBOX
inflate_bbox = 5
minx, miny, maxx, maxy = dam_nominal.bounds
delx = maxx - minx
dely = maxy - miny
minx = minx - delx * inflate_bbox
maxx = maxx + delx * inflate_bbox
miny = miny - dely * inflate_bbox
maxy = maxy + dely * inflate_bbox
dam_bbox = BBox(bbox=[minx, miny, maxx, maxy], crs=CRS.WGS84)
input_task = S2L1CWCSInput('BANDS-S2-L1C',
resx='20m',
resy='20m',
maxcc=1.,
time_difference=datetime.timedelta(hours=2),
instance_id=WMS_INSTANCE)
add_ndwi = S2L1CWCSInput('NDWI', instance_id=WMS_INSTANCE)
gdf = gpd.GeoDataFrame(crs={'init': 'epsg:4326'}, geometry=[dam_nominal])
gdf.plot()
add_nominal_water = VectorToRaster(
(FeatureType.MASK_TIMELESS, 'NOMINAL_WATER'), gdf, 1,
(FeatureType.MASK, 'IS_DATA'), np.uint8)
cloud_classifier = get_s2_pixel_cloud_detector(average_over=2,
dilation_size=1,
all_bands=False)
cloud_det = AddCloudMaskTask(cloud_classifier,
'BANDS-S2CLOUDLESS',
def download_data(path_save,
coords_top,
coords_bot,
patch_n,
s_date,
e_date,
debug=False):
# before moving onto actual tasks, check setup
check_sentinel_cfg()
[lat_left_top, lon_left_top] = coords_top
[lat_right_bot, lon_right_bot] = coords_bot
# TASK FOR BAND DATA
# add a request for B(B02), G(B03), R(B04), NIR (B08), SWIR1(B11), SWIR2(B12)
# from default layer 'ALL_BANDS' at 10m resolution
# Here we also do a simple filter of cloudy scenes. A detailed cloud cover
# detection is performed in the next step
custom_script = "return [B02, B03, B04, B08, B11, B12];"
add_data = S2L1CWCSInput(
layer="BANDS-S2-L1C",
feature=(FeatureType.DATA, "BANDS"), # save under name 'BANDS'
# custom url for 6 specific bands
custom_url_params={CustomUrlParam.EVALSCRIPT: custom_script},
resx="10m", # resolution x
resy="10m", # resolution y
maxcc=0.1, # maximum allowed cloud cover of original ESA tiles
)
# TASK FOR CLOUD INFO
# cloud detection is performed at 80m resolution
# and the resulting cloud probability map and mask
# are scaled to EOPatch's resolution
cloud_classifier = get_s2_pixel_cloud_detector(average_over=2,
dilation_size=1,
all_bands=False)
add_clm = AddCloudMaskTask(
cloud_classifier,
"BANDS-S2CLOUDLESS",
cm_size_y="80m",
cm_size_x="80m",
cmask_feature="CLM", # cloud mask name
cprobs_feature="CLP", # cloud prob. map name
)
# TASKS FOR CALCULATING NEW FEATURES
# NDVI: (B08 - B04)/(B08 + B04)
# NDWI: (B03 - B08)/(B03 + B08)
# NORM: sqrt(B02^2 + B03^2 + B04^2 + B08^2 + B11^2 + B12^2)
ndvi = NormalizedDifferenceIndex("NDVI", "BANDS/3", "BANDS/2")
ndwi = NormalizedDifferenceIndex("NDWI", "BANDS/1", "BANDS/3")
norm = EuclideanNorm("NORM", "BANDS")
# TASK FOR VALID MASK
# validate pixels using SentinelHub's cloud detection mask and region of acquisition
add_sh_valmask = AddValidDataMaskTask(
SentinelHubValidData(),
"IS_VALID" # name of output mask
)
# TASK FOR COUNTING VALID PIXELS
# count number of valid observations per pixel using valid data mask
count_val_sh = CountValid(
"IS_VALID",
"VALID_COUNT" # name of existing mask # name of output scalar
)
# TASK FOR SAVING TO OUTPUT (if needed)
path_save = Path(path_save)
path_save.mkdir(exist_ok=True)
# if not os.path.isdir(path_save):
# os.makedirs(path_save)
save = SaveToDisk(path_save,
overwrite_permission=OverwritePermission.OVERWRITE_PATCH)
# Define the workflow
workflow = LinearWorkflow(add_data, add_clm, ndvi, ndwi, norm,
add_sh_valmask, count_val_sh, save)
# Execute the workflow
# time interval for the SH request
# TODO: need to check if specified time interval is valid
time_interval = [s_date, e_date]
# define additional parameters of the workflow
execution_args = []
path_EOPatch = path_save / f"eopatch_{patch_n}"
execution_args.append({
add_data: {
"bbox":
BBox(
((lon_left_top, lat_left_top), (lon_right_bot, lat_right_bot)),
crs=CRS.WGS84,
),
"time_interval":
time_interval,
},
save: {
"eopatch_folder": path_EOPatch.stem
},
})
executor = EOExecutor(workflow, execution_args, save_logs=True)
if debug:
print("Downloading Satellite data ...")
executor.run(workers=2, multiprocess=False)
if executor.get_failed_executions():
raise RuntimeError("EOExecutor failed in finishing tasks!")
if debug:
executor.make_report()
if debug:
print("Satellite data is downloaded")
return path_EOPatch
from eolearn.io import S2L1CWCSInput, SentinelHubInputTask, S2L2AWCSInput
input_task = S2L2AWCSInput('BANDS-S2-L1C', resx='10m', resy='10m', maxcc=0.8)
add_l2a = S2L2AWCSInput(layer='BANDS-S2-L2A')
true_color = S2L1CWCSInput('TRUE-COLOR-S2-L1C')
SENT_SAT_CLASSICATIONS = S2L2AWCSInput("SCENE_CLASSIFICATION")
# dam_bbox = BBox(bbox=[106.898236, 11.040099, 107.319836, 11.316956], crs=CRS.WGS84)
# BBox(((106.95406121147886, 11.05524688042548), (107.32241244845807, 11.297316867404723)), crs=EPSG:4326)
print(dam_bbox)
# ### Step 1: Intialize (and implement workflow specific) EOTasks
# #### Create an EOPatch and add all EO features (satellite imagery data)
# In[5]:
input_task = S2L1CWCSInput('TRUE-COLOR-S2-L1C',
resx='20m',
resy='20m',
maxcc=0.5,
instance_id=None)
add_ndwi = S2L1CWCSInput('NDWI')
# #### Burn in the nominal water extent
#
# The `VectorToRaster` task expects the vectorised dataset in geopandas dataframe.
# In[6]:
gdf = gpd.GeoDataFrame(crs={'init': 'epsg:4326'}, geometry=[dam_nominal])
# In[8]:
add_nominal_water = VectorToRaster(