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mapbiomas-classification-mode-rf.py
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import ee
ASSETS = {
'classification': 'projects/imazon-simex/LULC/classification',
'n_observations': 'projects/imazon-simex/LULC/quality',
'tiles': 'projects/mapbiomas-workspace/AUXILIAR/landsat-mask',
'samples_folder': 'projects/imazon-simex/LULC/SAMPLES/COLLECTION6/INTEGRATE',
'output': 'projects/imazon-simex/LULC/classification-itg'
}
INPUT_VERSION = '2'
OUTPUT_VERSION = '7'
CLASS_IDS = [
3, # forest
4, # savanna
12, # grassland
15, # pasture
19, # agriculture
33 # water
]
# N Samples padrao
N_SAMPLES = [
{'class_id': 3, 'n_samples': 4000},
{'class_id': 4, 'n_samples': 1000},
{'class_id': 12, 'n_samples': 2000},
{'class_id': 15, 'n_samples': 6000},
{'class_id': 19, 'n_samples': 2000},
{'class_id': 33, 'n_samples': 2000},
]
YEARS = [
1985, 1986, 1987, 1988,
1989, 1990, 1991, 1992,
1993, 1994, 1995, 1996,
1997, 1998, 1999, 2000,
2001, 2002, 2003, 2004,
2005, 2006, 2007, 2008,
2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016,
2017, 2018, 2019, 2020
]
SAMPLES_LIST = [
"samples-amazon-collection-6-2018-2", "samples-amazon-collection-6-2017-2",
"samples-amazon-collection-6-2016-2", "samples-amazon-collection-6-2015-2",
"samples-amazon-collection-6-2014-2", "samples-amazon-collection-6-2013-2",
"samples-amazon-collection-6-2012-2", "samples-amazon-collection-6-2011-2",
"samples-amazon-collection-6-2010-2", "samples-amazon-collection-6-2009-2",
"samples-amazon-collection-6-2008-2", "samples-amazon-collection-6-2007-2",
"samples-amazon-collection-6-2006-2", "samples-amazon-collection-6-2005-2",
"samples-amazon-collection-6-2004-2", "samples-amazon-collection-6-2003-2",
"samples-amazon-collection-6-2002-2", "samples-amazon-collection-6-2001-2",
"samples-amazon-collection-6-2000-2", "samples-amazon-collection-6-1999-2",
"samples-amazon-collection-6-1998-2", "samples-amazon-collection-6-1997-2",
"samples-amazon-collection-6-1996-2", "samples-amazon-collection-6-1995-2",
"samples-amazon-collection-6-1994-2", "samples-amazon-collection-6-1993-2",
"samples-amazon-collection-6-1992-2", "samples-amazon-collection-6-1991-2",
"samples-amazon-collection-6-1990-2", "samples-amazon-collection-6-1989-2",
"samples-amazon-collection-6-1988-2", "samples-amazon-collection-6-1987-2",
"samples-amazon-collection-6-1986-2",
]
# landsat tiles
TILE_LIST = [
"001057", "001058", "001059", "001061",
"001062", "001063", "001064", "001065",
"001066", "001067", "001068", "002057",
"002059", "002060", "002061", "002062",
"002063", "002064", "002065", "002066",
"002067", "002068", "221064", "222061",
"222062", "222063", "222067", "223061",
"223062", "223063", "223068", "224063",
"224064", "224070", "225061", "225062",
"225063", "225064", "225070", "226059",
"226062", "226063", "226064", "226065",
"226068", "227058", "227059", "227060",
"227063", "227064", "227065", "227067",
"228058", "228060", "228063", "228064",
"228067", "228068", "228072", "229060",
"229061", "229063", "229064", "229066",
"229067", "229068", "230058", "230059",
"230060", "230061", "230063", "230064",
"230065", "230066", "230067", "230070",
"231060", "231061", "231063", "231065",
"231067", "232062", "232064", "232067",
"233058", "233062", "233063", "233064",
"233065", "233067", "003058", "003059",
"003060", "003061", "003062", "003063",
"003064", "003065", "003066", "003067",
"003068", "004058", "004059", "004060",
"004061", "004062", "004063", "004064",
"004065", "004066", "004067", "005059",
"005060", "005063", "005064", "005065",
"005066", "005067", "006063", "006064",
"006065",
"006066"
]
TRASH = [
"LT05_226062_20010715",
"LT05_226062_20010731",
"LE07_233058_20200322",
"LC08_233058_20200227",
"LC08_233058_20200126",
"LE07_233058_20200219",
"LC08_233058_20200211",
"LE07_233058_20190320",
"LC08_233058_20190328",
"LE07_233058_20190405",
"LC08_233058_20190429",
"LC08_233058_20190819",
"LE07_233058_20191014",
"LC08_233058_20191107",
"LC08_232059_20190305",
"LC08_232059_20190321",
"LC08_232059_20190406",
"LE07_232059_20190430",
"LE07_232059_20190820",
"LC08_232059_20190828",
"LE07_232059_20190905",
"LC08_232059_20190913",
"LE07_232059_20190921",
"LC08_232059_20190929",
"LE07_232059_20191007",
"LE07_232059_20191124",
"LC08_232059_20191202",
"LE07_232059_20191210",
"LE07_232059_20191226",
"LC08_232059_20180926",
"LC08_232059_20180825"
]
FEATURE_SPACE = [
'mode',
'mode_secondary',
'transitions_total',
'distinct_total',
'transitions_year',
'distinct_year',
'occurrence_forest_year',
'occurrence_savanna_year',
'occurrence_grassland_year',
'occurrence_pasture_year',
'occurrence_agriculture_year',
'occurrence_water_year',
'occurrence_savanna_total',
'occurrence_grassland_total',
'occurrence_agriculture_total',
'occurrence_water_total',
]
ee.Initialize()
def shuffle(collection, seed=1):
"""
Adds a column of deterministic pseudorandom numbers to a collection.
The range 0 (inclusive) to 1000000000 (exclusive).
"""
collection = collection.randomColumn('random', seed)\
.sort('random', True)\
.map(
lambda feature: feature.set(
'new_id',
ee.Number(feature.get('random'))
.multiply(1000000000)
.round()
)
)
# list of random ids
randomIdList = ee.List(
collection.reduceColumns(ee.Reducer.toList(), ['new_id'])
.get('list'))
# list of sequential ids
sequentialIdList = ee.List.sequence(1, collection.size())
# set new ids
shuffled = collection.remap(randomIdList, sequentialIdList, 'new_id')
return shuffled
#
# collection of all classified images
#
classification = ee.ImageCollection(ASSETS['classification'])
TRASH = list(map(lambda imageid: imageid + '-' + INPUT_VERSION, TRASH))
classification = classification \
.filter(ee.Filter.inList('system:index', TRASH).Not());
#
#
#
n_observations = ee.ImageCollection(ASSETS['n_observations'])
#
#
# samples
samples = map(
lambda sampleName:
ee.FeatureCollection(ASSETS['samples_folder'] + "/" + sampleName),
SAMPLES_LIST
)
samples = ee.FeatureCollection(list(samples)).flatten()
samples = shuffle(samples, seed=1)
shuffledSamples = map(
lambda obj: samples.filterMetadata(
'class', 'equals', obj['class_id']).limit(obj['n_samples']),
N_SAMPLES
)
samples = ee.FeatureCollection(list(shuffledSamples)).flatten()
#
#
# iterate
for tile in TILE_LIST:
try:
integrated = ee.Image().select()
tile_mask = ee.Image('{}/{}-2'.format(ASSETS['tiles'], int(tile)))
centroid = tile_mask.geometry().centroid()
#
#
#
classification_tile = classification.filterBounds(centroid)
n_observations_total = n_observations\
.filterBounds(centroid)\
.sum()\
.rename('observations_total')
transitions_total = classification_tile\
.reduce(ee.Reducer.countRuns())\
.divide(n_observations_total)\
.rename('transitions_total')
distinct_total = classification_tile\
.reduce(ee.Reducer.countDistinctNonNull())\
.rename('distinct_total')
#
# occurrence total
#
forest_total = classification_tile\
.map(lambda image: image.eq(3))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_forest_total')
savanna_total = classification_tile\
.map(lambda image: image.eq(4))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_savanna_total')
grassland_total = classification_tile\
.map(lambda image: image.eq(12))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_grassland_total')
pasture_total = classification_tile\
.map(lambda image: image.eq(15))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_pasture_total')
agriculture_total = classification_tile\
.map(lambda image: image.eq(19))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_agriculture_total')
water_total = classification_tile\
.map(lambda image: image.eq(33))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_total)\
.rename('occurrence_water_total')
for year in YEARS:
#
classification_year = classification\
.filterDate(str(year) + '-01-01', str(year) + '-12-31')\
.filterBounds(centroid)
if classification_year.size().getInfo() > 0:
classification_secondary = classification\
.filterDate(str(year - 1) + '-11-01', str(year) + '-06-30')\
.filterBounds(centroid)
#
n_observations_year = ee.Image(
'{}/{}-{}'.format(ASSETS['n_observations'], int(tile), year))\
.rename('observations_year')
#
transitions_year = classification_year\
.reduce(ee.Reducer.countRuns())\
.divide(n_observations_year)\
.rename('transitions_year')
distinct_year = classification_year\
.reduce(ee.Reducer.countDistinctNonNull())\
.rename('distinct_year')
#
# mode
#
mode_year = classification_year\
.reduce(ee.Reducer.mode())\
.rename('mode')
if classification_secondary.size().getInfo() > 0:
mode_secondary = classification_secondary\
.reduce(ee.Reducer.mode())\
.rename('mode_secondary')
else:
mode_secondary = mode_year.rename('mode_secondary')
#
# occurrence in the year
#
forest_year = classification_year\
.map(lambda image: image.eq(3))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_forest_year')
savanna_year = classification_year\
.map(lambda image: image.eq(4))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_savanna_year')
grassland_year = classification_year\
.map(lambda image: image.eq(12))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_grassland_year')
pasture_year = classification_year\
.map(lambda image: image.eq(15))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_pasture_year')
agriculture_year = classification_year\
.map(lambda image: image.eq(19))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_agriculture_year')
water_year = classification_year\
.map(lambda image: image.eq(33))\
.reduce(ee.Reducer.sum())\
.divide(n_observations_year)\
.rename('occurrence_water_year')
# image feature space
image = mode_year\
.addBands(mode_secondary)\
.addBands(transitions_total)\
.addBands(distinct_total)\
.addBands(transitions_year)\
.addBands(distinct_year)\
.addBands(n_observations_total)\
.addBands(n_observations_year)\
.addBands(forest_year)\
.addBands(savanna_year)\
.addBands(grassland_year)\
.addBands(pasture_year)\
.addBands(agriculture_year)\
.addBands(water_year)\
.addBands(forest_total)\
.addBands(savanna_total)\
.addBands(grassland_total)\
.addBands(pasture_total)\
.addBands(agriculture_total)\
.addBands(water_total)
image = image.select(FEATURE_SPACE)
# traning the classifier
classifier = ee.Classifier.smileRandomForest(
numberOfTrees=50,
variablesPerSplit=4,
minLeafPopulation=25
).train(samples, 'class', FEATURE_SPACE)
integrated_year = image\
.select(FEATURE_SPACE)\
.classify(classifier)
integrated_year = mode_year\
.where(integrated_year.neq(0), integrated_year)\
.rename(['classification_' + str(year)])
integrated = integrated.addBands(integrated_year)
else:
integrated_year = ee.Image(0)\
.rename(['classification_' + str(year)])
integrated = integrated.addBands(integrated_year)
integrated = integrated.mask(tile_mask)
geometry = tile_mask.geometry()
name = '{}-{}'.format(int(tile), OUTPUT_VERSION)
task = ee.batch.Export.image.toAsset(
image=integrated.toByte().set('version', OUTPUT_VERSION).set('tile', tile),
description=name,
assetId='{}/{}'.format(ASSETS['output'], name),
pyramidingPolicy={".default": "mode"},
region=geometry.getInfo()['coordinates'],
scale=30,
)
print('Exporting {}...'.format(name))
task.start()
except Exception as error:
print(error)