Datasets#
aitlas.datasets.aid module#
- class AIDDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://1drv.ms/u/s!AthY3vMZmuxChNR0Co7QHpJ56M-SvQ'#
- labels = ['Airport', 'BareLand', 'BaseballField', 'Beach', 'Bridge', 'Center', 'Church', 'Commercial', 'DenseResidential', 'Desert', 'Farmland', 'Forest', 'Industrial', 'Meadow', 'MediumResidential', 'Mountain', 'Park', 'Parking', 'Playground', 'Pond', 'Port', 'RailwayStation', 'Resort', 'River', 'School', 'SparseResidential', 'Square', 'Stadium', 'StorageTanks', 'Viaduct']#
- name = 'AID dataset'#
aitlas.datasets.aid_multilabel module#
- class AIDMultiLabelDataset(config)[source]#
Bases:
MultiLabelClassificationDataset
- url = 'https://github.com/Hua-YS/AID-Multilabel-Dataset'#
- labels = ['airplane', 'bare-soil', 'buildings', 'cars', 'chaparral', 'court', 'dock', 'field', 'grass', 'mobile-home', 'pavement', 'sand', 'sea', 'ship', 'tanks', 'trees', 'water']#
- name = 'AID multilabel dataset'#
aitlas.datasets.airs module#
aitlas.datasets.amazon_rainforest module#
aitlas.datasets.big_earth_net module#
- loads_pickle(buf)[source]#
- Parameters:
buf (bytes-like object) – the output of dumps
- Returns:
object
- dumps_pickle(obj)[source]#
Serialize an object. :param obj: object to be serialized :type obj: bytes-like object :return: Implementation-dependent bytes-like object
- class BigEarthNetDataset(config)[source]#
Bases:
BaseDataset
BigEarthNet dataset adaptation
- schema#
alias of
BigEarthNetSchema
- name = 'Big Earth Net'#
aitlas.datasets.brazilian_coffee_scenes module#
aitlas.datasets.breizhcrops module#
BreizhCrops - a crop type classification dataset
Note
Adapted from: dl4sits/BreizhCrops ; Original implementation of BreizhCrops dataset: dl4sits/BreizhCrops
- class DownloadProgressBar(*_, **__)[source]#
Bases:
tqdm
- Parameters:
iterable (iterable, optional) – Iterable to decorate with a progressbar. Leave blank to manually manage the updates.
desc (str, optional) – Prefix for the progressbar.
total (int or float, optional) – The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.
leave (bool, optional) – If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.
file (io.TextIOWrapper or io.StringIO, optional) – Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).
mininterval (float, optional) – Minimum progress display update interval [default: 0.1] seconds.
maxinterval (float, optional) – Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.
miniters (int or float, optional) – Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.
ascii (bool or str, optional) – If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.
disable (bool, optional) – Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.
unit (str, optional) – String that will be used to define the unit of each iteration [default: it].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.
dynamic_ncols (bool, optional) – If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].
smoothing (float, optional) – Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘
’{rate_fmt}{postfix}]’
- Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.
Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
initial (int or float, optional) – The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.
position (int, optional) – Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).
postfix (dict or *, optional) – Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
write_bytes (bool, optional) – Whether to write bytes. If (default: False) will write unicode.
lock_args (tuple, optional) – Passed to refresh for intermediate output (initialisation, iterating, and updating).
nrows (int, optional) – The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).
delay (float, optional) – Don’t display until [default: 0] seconds have elapsed.
gui (bool, optional) – WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].
- Returns:
out
- Return type:
decorated iterator.
- class BreizhCropsDataset(config)[source]#
Bases:
CropsDataset
- schema#
alias of
BreizhCropsSchema
- build_folder_structure(root, year, level, region)[source]#
Folder structure:
<root> codes.csv classmapping.csv <year> <region>.shp <level> <region>.csv <region>.h5 <region> <csv> 123123.csv 123125.csv ...
- get_classes_to_ind(classmapping)[source]#
keep for now, could be needed to make it compatible with GenericMulticlass
aitlas.datasets.camvid module#
- class CamVidDataset(config)[source]#
Bases:
SemanticSegmentationDataset
- url = 'https://github.com/alexgkendall/SegNet-Tutorial'#
- labels = ['sky', 'building', 'column_pole', 'road', 'sidewalk', 'tree', 'sign', 'fence', 'car', 'pedestrian', 'byciclist', 'void']#
- color_mapping = [[255, 127, 127], [255, 191, 127], [255, 255, 127], [191, 255, 127], [127, 255, 127], [127, 255, 191], [127, 255, 255], [127, 191, 255], [127, 127, 255], [191, 127, 255], [255, 127, 255], [255, 127, 191]]#
- name = 'CamVid'#
aitlas.datasets.chactun module#
aitlas.datasets.clrs module#
- class CLRSDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://github.com/lehaifeng/CLRS'#
- labels = ['airport', 'bare-land', 'beach', 'bridge', 'commercial', 'desert', 'farmland', 'forest', 'golf-course', 'highway', 'industrial', 'meadow', 'mountain', 'overpass', 'park', 'parking', 'playground', 'port', 'railway', 'railway-station', 'residential', 'river', 'runway', 'stadium', 'storage-tank']#
- name = 'CLRS dataset'#
aitlas.datasets.crops_classification module#
- class CropsDataset(config)[source]#
Bases:
BaseDataset
CropsDataset - a crop type classification dataset
- schema#
alias of
CropsDatasetSchema
aitlas.datasets.dfc15_multilabel module#
aitlas.datasets.eopatch_crops module#
- class DownloadProgressBar(*_, **__)[source]#
Bases:
tqdm
- Parameters:
iterable (iterable, optional) – Iterable to decorate with a progressbar. Leave blank to manually manage the updates.
desc (str, optional) – Prefix for the progressbar.
total (int or float, optional) – The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.
leave (bool, optional) – If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.
file (io.TextIOWrapper or io.StringIO, optional) – Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).
mininterval (float, optional) – Minimum progress display update interval [default: 0.1] seconds.
maxinterval (float, optional) – Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.
miniters (int or float, optional) – Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.
ascii (bool or str, optional) – If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.
disable (bool, optional) – Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.
unit (str, optional) – String that will be used to define the unit of each iteration [default: it].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.
dynamic_ncols (bool, optional) – If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].
smoothing (float, optional) – Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘
’{rate_fmt}{postfix}]’
- Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.
Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
initial (int or float, optional) – The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.
position (int, optional) – Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).
postfix (dict or *, optional) – Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
write_bytes (bool, optional) – Whether to write bytes. If (default: False) will write unicode.
lock_args (tuple, optional) – Passed to refresh for intermediate output (initialisation, iterating, and updating).
nrows (int, optional) – The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).
delay (float, optional) – Don’t display until [default: 0] seconds have elapsed.
gui (bool, optional) – WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].
- Returns:
out
- Return type:
decorated iterator.
aitlas.datasets.eurosat module#
aitlas.datasets.inria module#
aitlas.datasets.landcover_ai module#
- class LandCoverAiDataset(config)[source]#
Bases:
SemanticSegmentationDataset
- url = 'https://landcover.ai.linuxpolska.com/'#
- labels = ['Background', 'Buildings', 'Woodlands', 'Water', 'Road']#
- color_mapping = [[255, 255, 0], [0, 0, 0], [0, 255, 0], [0, 0, 255], [200, 200, 200]]#
- name = 'Landcover AI'#
aitlas.datasets.massachusetts_buildings module#
aitlas.datasets.massachusetts_roads module#
aitlas.datasets.mlrs_net module#
- class MLRSNetMultiLabelDataset(config)[source]#
Bases:
MultiLabelClassificationDataset
- url = 'https://data.mendeley.com/datasets/7j9bv9vwsx/2'#
- labels = ['airplane', 'airport', 'bare soil', 'baseball diamond', 'basketball court', 'beach', 'bridge', 'buildings', 'cars', 'cloud', 'containers', 'crosswalk', 'dense residential area', 'desert', 'dock', 'factory', 'field', 'football field', 'forest', 'freeway', 'golf course', 'grass', 'greenhouse', 'gully', 'habor', 'intersection', 'island', 'lake', 'mobile home', 'mountain', 'overpass', 'park', 'parking lot', 'parkway', 'pavement', 'railway', 'railway station', 'river', 'road', 'roundabout', 'runway', 'sand', 'sea', 'ships', 'snow', 'snowberg', 'sparse residential area', 'stadium', 'swimming pool', 'tanks', 'tennis court', 'terrace', 'track', 'trail', 'transmission tower', 'trees', 'water', 'chaparral', 'wetland', 'wind turbine']#
- name = 'MLRSNet dataset'#
aitlas.datasets.multiclass_classification module#
- class MultiClassClassificationDataset(config)[source]#
Bases:
BaseDataset
- schema#
alias of
ClassificationDatasetSchema
aitlas.datasets.multilabel_classification module#
- class MultiLabelClassificationDataset(config)[source]#
Bases:
BaseDataset
- schema#
alias of
ClassificationDatasetSchema
aitlas.datasets.npz module#
- class NpzDataset(config)[source]#
Bases:
BaseDataset
- schema#
alias of
NPZDatasetSchema
- labels = None#
aitlas.datasets.object_detection module#
- class BaseObjectDetectionDataset(config)[source]#
Bases:
BaseDataset
Base object detection dataset class
BaseDataset constructor
- Parameters:
config (Config, contains information for the batch size, number of workers, list of labels, list of transformations) – Configuration object which specifies the details of the dataset.
- name = 'Object Detection Dataset'#
- class ObjectDetectionPascalDataset(config)[source]#
Bases:
BaseObjectDetectionDataset
- schema#
alias of
ObjectDetectionPascalDatasetSchema
- labels = [None]#
- class ObjectDetectionCocoDataset(config)[source]#
Bases:
BaseObjectDetectionDataset
This is a skeleton object detection dataset following the Coco format
- schema#
alias of
ObjectDetectionCocoDatasetSchema
aitlas.datasets.optimal_31 module#
- class Optimal31Dataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://drive.google.com/file/d/1Fk9a0DW8UyyQsR8dP2Qdakmr69NVBhq9/view'#
- labels = ['airplane', 'airport', 'baseball_diamond', 'basketball_court', 'beach', 'bridge', 'chaparral', 'church', 'circular_farmland', 'commercial_area', 'dense_residential', 'desert', 'forest', 'freeway', 'golf_course', 'ground_track_field', 'harbor', 'industrial_area', 'intersection', 'island', 'lake', 'meadow', 'medium_residential', 'mobile_home_park', 'mountain', 'overpass', 'parking_lot', 'railway', 'rectangular_farmland', 'roundabout', 'runway']#
- name = 'Optimal31 dataset'#
aitlas.datasets.pattern_net module#
- class PatternNetDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://arxiv.org/abs/1706.03424'#
- labels = ['airplane', 'baseball_field', 'basketball_court', 'beach', 'bridge', 'cemetery', 'chaparral', 'christmas_tree_farm', 'closed_road', 'coastal_mansion', 'crosswalk', 'dense_residential', 'ferry_terminal', 'football_field', 'forest', 'freeway', 'golf_course', 'harbor', 'intersection', 'mobile_home_park', 'nursing_home', 'oil_gas_field', 'oil_well', 'overpass', 'parking_lot', 'parking_space', 'railway', 'river', 'runway', 'runway_marking', 'shipping_yard', 'solar_panel', 'sparse_residential', 'storage_tank', 'swimming_pool', 'tennis_court', 'transformer_station', 'wastewater_treatment_plant']#
- name = 'PatternNet dataset'#
aitlas.datasets.planet_uas module#
- class PlanetUASMultiLabelDataset(config)[source]#
Bases:
MultiLabelClassificationDataset
- url = 'https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/overview'#
- labels = ['haze', 'primary', 'agriculture', 'clear', 'water', 'habitation', 'road', 'cultivation', 'slash_burn', 'cloudy', 'partly_cloudy', 'conventional_mine', 'bare_ground', 'artisinal_mine', 'blooming', 'selective_logging', 'blow_down']#
- name = 'Planet UAS multilabel dataset'#
aitlas.datasets.resisc45 module#
- class Resisc45Dataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://www.tensorflow.org/datasets/catalog/resisc45'#
- labels = ['airplane', 'airport', 'baseball_diamond', 'basketball_court', 'beach', 'bridge', 'chaparral', 'church', 'circular_farmland', 'cloud', 'commercial_area', 'dense_residential', 'desert', 'forest', 'freeway', 'golf_course', 'ground_track_field', 'harbor', 'industrial_area', 'intersection', 'island', 'lake', 'meadow', 'medium_residential', 'mobile_home_park', 'mountain', 'overpass', 'palace', 'parking_lot', 'railway', 'railway_station', 'rectangular_farmland', 'river', 'roundabout', 'runway', 'sea_ice', 'ship', 'snowberg', 'sparse_residential', 'stadium', 'storage_tank', 'tennis_court', 'terrace', 'thermal_power_station', 'wetland']#
- name = 'RESISC45 dataset'#
aitlas.datasets.rsd46_whu module#
- class RSD46WHUDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU'#
- labels = ['Airplane', 'Airport', 'Artificial dense forest land', 'Artificial sparse forest land', 'Bare land', 'Basketball court', 'Blue structured factory building', 'Building', 'Construction site', 'Cross river bridge', 'Crossroads', 'Dense tall building', 'Dock', 'Fish pond', 'Footbridge', 'Graff', 'Grassland', 'Low scattered building', 'Lrregular farmland', 'Medium density scattered building', 'Medium density structured building', 'Natural dense forest land', 'Natural sparse forest land', 'Oiltank', 'Overpass', 'Parking lot', 'Plasticgreenhouse', 'Playground', 'Railway', 'Red structured factory building', 'Refinery', 'Regular farmland', 'Scattered blue roof factory building', 'Scattered red roof factory building', 'Sewage plant-type-one', 'Sewage plant-type-two', 'Ship', 'Solar power station', 'Sparse residential area', 'Square', 'Steelsmelter', 'Storage land', 'Tennis court', 'Thermal power plant', 'Vegetable plot', 'Water']#
- name = 'RSD46-WHU dataset'#
aitlas.datasets.rsi_cb256 module#
- class RSICB256Dataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://github.com/lehaifeng/RSI-CB'#
- labels = ['airplane', 'airport_runway', 'artificial_grassland', 'avenue', 'bare_land', 'bridge', 'city_building', 'coastline', 'container', 'crossroads', 'dam', 'desert', 'dry_farm', 'forest', 'green_farmland', 'highway', 'hirst', 'lakeshore', 'mangrove', 'marina', 'mountain', 'parkinglot', 'pipeline', 'residents', 'river', 'river_protection_forest', 'sandbeach', 'sapling', 'sea', 'shrubwood', 'snow_mountain', 'sparse_forest', 'storage_room', 'stream', 'town']#
- name = 'RSI-CB256 dataset'#
aitlas.datasets.rsscn7 module#
- class RSSCN7Dataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxxaW56b3VjbnxneDo1MDYzYWMxOWIwMjRiMWFi'#
- labels = ['farm_land', 'forest', 'grass_land', 'industrial_region', 'parking_lot', 'residential_region', 'river_lake']#
- name = 'RSSCN7 dataset'#
aitlas.datasets.sat6 module#
- class SAT6Dataset(config)[source]#
Bases:
BaseDataset
- schema#
alias of
MatDatasetSchema
- url = 'http://csc.lsu.edu/~saikat/deepsat/'#
- labels = ['buildings', 'barren land', 'trees', 'grassland', 'roads', 'water bodies']#
- name = 'SAT-6 dataset'#
aitlas.datasets.schemas module#
- class MatDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring a classification dataset given as mat file.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class NPZDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring a classification dataset given as npz file.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class ClassificationDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring a classification dataset.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class SegmentationDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring a segmentation dataset.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class ObjectDetectionPascalDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring an object detection dataset given in PASCAL VOC format.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class ObjectDetectionCocoDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring an object detection dataset given in COCO format.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class BigEarthNetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring the BigEarthNet dataset.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class SpaceNet6DatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring the SpaceNet6 dataset.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class BreizhCropsSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring the BreizhCrops dataset for crop type prediction.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class CropsDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring dataset for crop type prediction.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
- class So2SatDatasetSchema(*, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)[source]#
Bases:
BaseDatasetSchema
Schema for configuring the So2Sat dataset.
- Parameters:
- opts: SchemaOpts = <marshmallow.schema.SchemaOpts object>#
aitlas.datasets.semantic_segmentation module#
- class SemanticSegmentationDataset(config)[source]#
Bases:
BaseDataset
- schema#
alias of
SegmentationDatasetSchema
- labels = None#
- color_mapping = None#
- name = None#
aitlas.datasets.siri_whu module#
- class SiriWhuDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'http://www.lmars.whu.edu.cn/prof_web/zhongyanfei/e-code.html'#
- labels = ['agriculture', 'commercial', 'harbor', 'idle_land', 'industrial', 'meadow', 'overpass', 'park', 'pond', 'residential', 'river', 'water']#
- name = 'SIRI-WHU dataset'#
aitlas.datasets.so2sat module#
- class So2SatDataset(config)[source]#
Bases:
BaseDataset
So2Sat dataset version 2 (contains train, validation and test splits)
So2Sat LCZ42 is a dataset consisting of corresponding synthetic aperture radar and multispectral optical image data acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world, and comes with a split into fully independent, non-overlapping training, validation, and test sets.
- url = 'https://dataserv.ub.tum.de/s/m1483140/download?path=%2F&files=testing.h5'#
- name = 'So2Sat dataset'#
- schema#
alias of
So2SatDatasetSchema
- labels = ['Compact high_rise', 'Compact middle_rise', 'Compact low_rise', 'Open high_rise', 'Open middle_rise', 'Open low_rise', 'Lightweight low_rise', 'Large low_rise', 'Sparsely built', 'Heavy industry', 'Dense trees', 'Scattered trees', 'Bush or scrub', 'Low plants', 'Bare rock or paved', 'Bare soil or sand', 'Water']#
aitlas.datasets.spacenet6 module#
Note
Based on the implementation at: SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions
- process_image(image_path, segmentation_directory, edge_width, contact_width, gt_buildings_csv)[source]#
Creates and saves the target (ground-truth) segmentation mask for the input image.
- Parameters:
- class SpaceNet6Dataset(config)[source]#
Bases:
BaseDataset
SpaceNet6 dataset.
- schema#
alias of
SpaceNet6DatasetSchema
- prepare()[source]#
Prepares the SpaceNet6 data set for model training and validation by:
Creating training segmentation masks from the geojson files
2. Splitting the data set by location, which was shown to be very important for model learning, see: SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions Creates 10 splits of the data set. Each split consists of 10 folds (i.e. further splits) of which 9 are used for training and one for validation/testing (in essence, a cross validation procedure).
aitlas.datasets.uc_merced module#
- class UcMercedDataset(config)[source]#
Bases:
MultiClassClassificationDataset
- labels = ['agricultural', 'airplane', 'baseballdiamond', 'beach', 'buildings', 'chaparral', 'denseresidential', 'forest', 'freeway', 'golfcourse', 'harbor', 'intersection', 'mediumresidential', 'mobilehomepark', 'overpass', 'parkinglot', 'river', 'runway', 'sparseresidential', 'storagetanks', 'tenniscourt']#
- name = 'UC Merced dataset'#
aitlas.datasets.uc_merced_multilabel module#
- class UcMercedMultiLabelDataset(config)[source]#
Bases:
MultiLabelClassificationDataset
- url = 'https://drive.google.com/file/d/1DtKiauowCB0ykjFe8v0OVvT76rEfOk0v/view'#
- labels = ['airplane', 'bare-soil', 'buildings', 'cars', 'chaparral', 'court', 'dock', 'field', 'grass', 'mobile-home', 'pavement', 'sand', 'sea', 'ship', 'tanks', 'trees', 'water']#
- name = 'UC Merced multilabel dataset'#
aitlas.datasets.urls module#
Contains raw urls to download the data for crop type prediction tasks. TODO Refactor raw csv urls to be more general
aitlas.datasets.whu_rs19 module#
- class WHURS19Dataset(config)[source]#
Bases:
MultiClassClassificationDataset
- url = 'https://github.com/CAPTAIN-WHU/BED4RS'#
- labels = ['Airport', 'Beach', 'Bridge', 'Commercial', 'Desert', 'Farmland', 'footballField', 'Forest', 'Industrial', 'Meadow', 'Mountain', 'Park', 'Parking', 'Pond', 'Port', 'railwayStation', 'Residential', 'River', 'Viaduct']#
- name = 'WHU-RS19 dataset'#