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Error code: DatasetGenerationCastError Exception: DatasetGenerationCastError Message: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 2 new columns ({'PM25', 'PM10'}) and 7 missing columns ({'CLOUD_BASE_PRESSURE', 'HCHO', 'CLOUD_TOP_PRESSURE', 'CH4', 'AER_AI_340_380', 'CO', 'AER_AI_354_388'}). This happened while the csv dataset builder was generating data using hf://datasets/links-ads/mil-qualair/stations.csv (at revision 287b8743d9c12d30faead4922738c2d0e6064ea0) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations) Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single writer.write_table(table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table pa_table = table_cast(pa_table, self._schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast date: string station_id: int64 city: string year: int64 NO2: double O3: double PM10: double PM25: double SO2: double -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1238 to {'date': Value(dtype='string', id=None), 'station_id': Value(dtype='int64', id=None), 'city': Value(dtype='string', id=None), 'year': Value(dtype='int64', id=None), 'CH4': Value(dtype='float64', id=None), 'CO': Value(dtype='float64', id=None), 'HCHO': Value(dtype='float64', id=None), 'NO2': Value(dtype='float64', id=None), 'O3': Value(dtype='float64', id=None), 'SO2': Value(dtype='float64', id=None), 'CLOUD_TOP_PRESSURE': Value(dtype='float64', id=None), 'CLOUD_BASE_PRESSURE': Value(dtype='float64', id=None), 'AER_AI_340_380': Value(dtype='float64', id=None), 'AER_AI_354_388': Value(dtype='float64', id=None)} because column names don't match During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1324, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 938, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single raise DatasetGenerationCastError.from_cast_error( datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset All the data files must have the same columns, but at some point there are 2 new columns ({'PM25', 'PM10'}) and 7 missing columns ({'CLOUD_BASE_PRESSURE', 'HCHO', 'CLOUD_TOP_PRESSURE', 'CH4', 'AER_AI_340_380', 'CO', 'AER_AI_354_388'}). This happened while the csv dataset builder was generating data using hf://datasets/links-ads/mil-qualair/stations.csv (at revision 287b8743d9c12d30faead4922738c2d0e6064ea0) Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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date
string | station_id
int64 | city
string | year
int64 | CH4
null | CO
float64 | HCHO
float64 | NO2
null | O3
float64 | SO2
float64 | CLOUD_TOP_PRESSURE
float64 | CLOUD_BASE_PRESSURE
float64 | AER_AI_340_380
null | AER_AI_354_388
null |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2018-04-30T10-27-52 | 1 | milan | 2,018 | null | 0.037974 | 0.000395 | null | 0.159405 | 0.000412 | 58,506.097227 | 66,746.066641 | null | null |
2018-04-30T10-27-52 | 2 | milan | 2,018 | null | 0.038527 | 0.000494 | null | 0.159214 | 0.000884 | 62,029.962928 | 70,656.242112 | null | null |
2018-04-30T10-27-52 | 3 | milan | 2,018 | null | 0.038249 | 0.000402 | null | 0.159061 | 0.000274 | 59,537.164234 | 67,887.026614 | null | null |
2018-04-30T10-27-52 | 4 | milan | 2,018 | null | 0.038918 | 0.000493 | null | 0.158836 | 0.000583 | 64,873.006641 | 73,806.437734 | null | null |
2018-04-30T10-27-52 | 5 | milan | 2,018 | null | 0.038647 | 0.000499 | null | 0.158831 | 0.000636 | 65,132.211719 | 74,086.594922 | null | null |
2018-04-30T10-27-52 | 6 | milan | 2,018 | null | 0.038648 | 0.000475 | null | 0.159099 | 0.000677 | 61,772.416284 | 70,370.849842 | null | null |
2018-04-30T10-27-52 | 7 | milan | 2,018 | null | 0.03856 | 0.000465 | null | 0.159135 | 0.00065 | 61,021.365859 | 69,537.679922 | null | null |
2018-04-30T10-27-52 | 8 | milan | 2,018 | null | 0.038665 | 0.00045 | null | 0.15775 | null | 63,381.629648 | 72,140.891484 | null | null |
2018-04-30T10-27-52 | 9 | milan | 2,018 | null | 0.038594 | 0.000491 | null | 0.15923 | 0.00085 | 61,906.576328 | 70,520.701172 | null | null |
2018-05-01T10-08-54 | 1 | milan | 2,018 | null | null | null | null | 0.157039 | 0.00331 | 26,242.688086 | 30,677.560254 | null | null |
2018-05-01T10-08-54 | 2 | milan | 2,018 | null | null | null | null | 0.156873 | 0.003836 | 26,885.609582 | 31,400.039682 | null | null |
2018-05-01T10-08-54 | 3 | milan | 2,018 | null | null | null | null | 0.157045 | 0.003486 | 26,052.428743 | 30,462.55604 | null | null |
2018-05-01T10-08-54 | 4 | milan | 2,018 | null | null | null | null | 0.157489 | 0.002346 | 25,834.269063 | 30,210.478867 | null | null |
2018-05-01T10-08-54 | 5 | milan | 2,018 | null | null | null | null | 0.156578 | 0.003775 | 27,522.670332 | 32,115.134258 | null | null |
2018-05-01T10-08-54 | 6 | milan | 2,018 | null | null | null | null | 0.157332 | 0.003213 | 25,936.108981 | 30,331.256648 | null | null |
2018-05-01T10-08-54 | 7 | milan | 2,018 | null | null | null | null | 0.157326 | 0.003266 | 25,895.014609 | 30,285.905625 | null | null |
2018-05-01T10-08-54 | 8 | milan | 2,018 | null | null | null | null | 0.157423 | 0.003173 | 25,871.085977 | 30,250.42752 | null | null |
2018-05-01T10-08-54 | 9 | milan | 2,018 | null | null | null | null | 0.157065 | 0.003593 | 26,498.551387 | 30,964.767793 | null | null |
2018-05-02T11-31-27 | 1 | milan | 2,018 | null | null | null | null | 0.161591 | null | 60,772.72957 | 69,104.997031 | null | null |
2018-05-02T11-31-27 | 2 | milan | 2,018 | null | 0.031778 | null | null | 0.161097 | null | 67,406.710562 | 76,496.369742 | null | null |
2018-05-02T11-31-27 | 3 | milan | 2,018 | null | null | null | null | 0.161496 | null | 60,700.264595 | 69,022.490642 | null | null |
2018-05-02T11-31-27 | 4 | milan | 2,018 | null | null | null | null | 0.160897 | null | 65,467.06957 | 74,330.578828 | null | null |
2018-05-02T11-31-27 | 5 | milan | 2,018 | null | 0.031778 | null | null | 0.16048 | null | 69,180.746563 | 78,464.550703 | null | null |
2018-05-02T11-31-27 | 6 | milan | 2,018 | null | null | null | null | 0.161288 | null | 64,724.399083 | 73,506.598767 | null | null |
2018-05-02T11-31-27 | 7 | milan | 2,018 | null | null | null | null | 0.161385 | null | 63,947.500703 | 72,641.109609 | null | null |
2018-05-02T11-31-27 | 8 | milan | 2,018 | null | null | null | null | 0.161464 | null | 59,299.773945 | 67,459.585 | null | null |
2018-05-02T11-31-27 | 9 | milan | 2,018 | null | null | null | null | 0.161159 | null | 66,527.915625 | 75,518.666094 | null | null |
2018-05-03T11-12-29 | 1 | milan | 2,018 | null | null | null | null | 0.154993 | null | 61,657.761836 | 70,038.321719 | null | null |
2018-05-03T11-12-29 | 2 | milan | 2,018 | null | null | null | null | 0.155263 | null | 59,066.889461 | 67,167.53418 | null | null |
2018-05-03T11-12-29 | 3 | milan | 2,018 | null | null | null | null | 0.155567 | null | 61,802.361822 | 70,197.632212 | null | null |
2018-05-03T11-12-29 | 4 | milan | 2,018 | null | null | null | null | 0.155516 | null | 61,448.609805 | 69,810.382266 | null | null |
2018-05-03T11-12-29 | 5 | milan | 2,018 | null | null | null | null | 0.155283 | null | 58,933.514063 | 67,020.440234 | null | null |
2018-05-03T11-12-29 | 6 | milan | 2,018 | null | null | null | null | 0.155746 | null | 60,801.614321 | 69,091.030605 | null | null |
2018-05-03T11-12-29 | 7 | milan | 2,018 | null | null | null | null | 0.155752 | null | 61,030.062227 | 69,344.023906 | null | null |
2018-05-03T11-12-29 | 8 | milan | 2,018 | null | null | null | null | 0.155975 | null | 62,799.95957 | 71,302.505156 | null | null |
2018-05-03T11-12-29 | 9 | milan | 2,018 | null | null | null | null | 0.155368 | null | 59,472.31793 | 67,617.091172 | null | null |
2018-05-04T10-54-02 | 1 | milan | 2,018 | null | null | 0.000489 | null | 0.156905 | 0.000175 | 42,391.40793 | 49,264.014688 | null | null |
2018-05-04T10-54-02 | 2 | milan | 2,018 | null | null | 0.000555 | null | 0.155792 | null | 45,524.439228 | 52,105.205829 | null | null |
2018-05-04T10-54-02 | 3 | milan | 2,018 | null | null | null | null | 0.156078 | 0.000333 | 43,970.224974 | 50,878.904447 | null | null |
2018-05-04T10-54-02 | 4 | milan | 2,018 | null | 0.03348 | 0.000531 | null | 0.154277 | null | 52,116.419727 | 59,434.62918 | null | null |
2018-05-04T10-54-02 | 5 | milan | 2,018 | null | 0.032877 | 0.000613 | null | 0.155474 | 0.000149 | 49,715.229727 | 56,785.027891 | null | null |
2018-05-04T10-54-02 | 6 | milan | 2,018 | null | null | 0.000484 | null | 0.154846 | null | 47,066.007526 | 53,834.320348 | null | null |
2018-05-04T10-54-02 | 7 | milan | 2,018 | null | null | 0.000469 | null | 0.155024 | null | 46,397.208984 | 53,124.92918 | null | null |
2018-05-04T10-54-02 | 8 | milan | 2,018 | null | null | null | null | 0.154477 | 0.000616 | 49,622.235195 | 56,662.204023 | null | null |
2018-05-04T10-54-02 | 9 | milan | 2,018 | null | null | 0.000514 | null | 0.155296 | null | 46,412.563828 | 53,091.659766 | null | null |
2018-05-05T10-35-08 | 1 | milan | 2,018 | null | 0.032965 | 0.000609 | null | 0.149966 | null | null | null | null | null |
2018-05-05T10-35-08 | 2 | milan | 2,018 | null | 0.033603 | 0.000543 | null | 0.148985 | null | 36,416.625 | 41,997.00323 | null | null |
2018-05-05T10-35-08 | 3 | milan | 2,018 | null | 0.033276 | 0.000647 | null | 0.149776 | null | null | null | null | null |
2018-05-05T10-35-08 | 4 | milan | 2,018 | null | 0.03389 | 0.00051 | null | 0.148933 | 0.000052 | null | null | null | null |
2018-05-05T10-35-08 | 5 | milan | 2,018 | null | 0.033714 | 0.000485 | null | 0.148456 | null | 35,776.137383 | 41,279.320078 | null | null |
2018-05-05T10-35-08 | 6 | milan | 2,018 | null | 0.033681 | 0.000607 | null | 0.149277 | null | null | null | null | null |
2018-05-05T10-35-08 | 7 | milan | 2,018 | null | 0.03357 | 0.000625 | null | 0.149405 | null | null | null | null | null |
2018-05-05T10-35-08 | 8 | milan | 2,018 | null | 0.03444 | 0.000551 | null | 0.149538 | 0.000034 | null | null | null | null |
2018-05-05T10-35-08 | 9 | milan | 2,018 | null | 0.033666 | 0.000566 | null | 0.149077 | null | 36,451.720165 | 42,035.575408 | null | null |
2018-05-06T10-16-16 | 1 | milan | 2,018 | null | null | 0.000477 | null | 0.148044 | 0.000282 | null | null | null | null |
2018-05-06T10-16-16 | 2 | milan | 2,018 | null | null | 0.00051 | null | 0.148424 | 0.000322 | null | null | null | null |
2018-05-06T10-16-16 | 3 | milan | 2,018 | null | null | 0.000489 | null | 0.148263 | 0.000373 | null | null | null | null |
2018-05-06T10-16-16 | 4 | milan | 2,018 | null | null | 0.000549 | null | 0.148809 | 0.00045 | null | null | null | null |
2018-05-06T10-16-16 | 5 | milan | 2,018 | null | null | 0.000544 | null | 0.148699 | 0.000164 | null | null | null | null |
2018-05-06T10-16-16 | 6 | milan | 2,018 | null | null | 0.000502 | null | 0.148448 | 0.00049 | null | null | null | null |
2018-05-06T10-16-16 | 7 | milan | 2,018 | null | null | 0.000493 | null | 0.148351 | 0.000477 | null | null | null | null |
2018-05-06T10-16-16 | 8 | milan | 2,018 | null | null | 0.000567 | null | 0.14887 | 0.000438 | null | null | null | null |
2018-05-06T10-16-16 | 9 | milan | 2,018 | null | null | 0.000504 | null | 0.148412 | 0.000393 | null | null | null | null |
2018-05-07T11-38-08 | 1 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 2 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 3 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 4 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 5 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 6 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 7 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 8 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-07T11-38-08 | 9 | milan | 2,018 | null | null | null | null | null | null | null | null | null | null |
2018-05-08T11-19-10 | 1 | milan | 2,018 | null | 0.034598 | 0.000596 | null | 0.153171 | 0.000128 | 38,607.106563 | 44,450.194844 | null | null |
2018-05-08T11-19-10 | 2 | milan | 2,018 | null | 0.033026 | 0.000518 | null | 0.152575 | 0.000204 | 37,037.99046 | 42,844.011756 | null | null |
2018-05-08T11-19-10 | 3 | milan | 2,018 | null | 0.034964 | 0.000587 | null | 0.153554 | 0.000054 | 41,340.989741 | 47,481.591732 | null | null |
2018-05-08T11-19-10 | 4 | milan | 2,018 | null | 0.033625 | 0.000506 | null | 0.15322 | 0.000057 | 35,389.066445 | 41,127.467109 | null | null |
2018-05-08T11-19-10 | 5 | milan | 2,018 | null | 0.03348 | 0.000421 | null | 0.153647 | null | 48,204.223867 | 55,175.315078 | null | null |
2018-05-08T11-19-10 | 6 | milan | 2,018 | null | 0.033916 | 0.000458 | null | 0.152981 | 0.000243 | 33,919.877903 | 39,390.539242 | null | null |
2018-05-08T11-19-10 | 7 | milan | 2,018 | null | 0.034113 | 0.000465 | null | 0.153049 | 0.000246 | 34,229.614336 | 39,660.064297 | null | null |
2018-05-08T11-19-10 | 8 | milan | 2,018 | null | 0.034122 | 0.000448 | null | 0.154022 | 0.000143 | 53,390.441758 | 60,847.502109 | null | null |
2018-05-08T11-19-10 | 9 | milan | 2,018 | null | 0.033326 | 0.000461 | null | 0.152534 | 0.000296 | 35,127.47207 | 40,786.252773 | null | null |
2018-05-09T11-00-15 | 1 | milan | 2,018 | null | 0.032984 | 0.000352 | null | 0.164005 | null | 85,489.192266 | 97,183.035625 | null | null |
2018-05-09T11-00-15 | 2 | milan | 2,018 | null | 0.034201 | 0.000333 | null | 0.165235 | null | 94,380.430439 | 98,194.761719 | null | null |
2018-05-09T11-00-15 | 3 | milan | 2,018 | null | 0.033003 | 0.000335 | null | 0.163878 | null | 79,981.748455 | 92,067.37603 | null | null |
2018-05-09T11-00-15 | 4 | milan | 2,018 | null | 0.033841 | 0.00029 | null | 0.165015 | null | 78,176.716563 | 88,266.397969 | null | null |
2018-05-09T11-00-15 | 5 | milan | 2,018 | null | 0.034628 | 0.000284 | null | 0.165788 | null | 86,358.334609 | 94,117.516719 | null | null |
2018-05-09T11-00-15 | 6 | milan | 2,018 | null | 0.033907 | 0.0003 | null | 0.164851 | null | 82,757.367689 | 91,990.410479 | null | null |
2018-05-09T11-00-15 | 7 | milan | 2,018 | null | 0.03382 | 0.000306 | null | 0.164763 | null | 82,974.089609 | 92,715.061797 | null | null |
2018-05-09T11-00-15 | 8 | milan | 2,018 | null | 0.033165 | 0.000335 | null | 0.164307 | 0.000224 | 71,732.254609 | 81,860.563984 | null | null |
2018-05-09T11-00-15 | 9 | milan | 2,018 | null | 0.034127 | 0.000322 | null | 0.164959 | null | 91,321.986406 | 97,038.013516 | null | null |
2018-05-10T10-41-22 | 1 | milan | 2,018 | null | 0.037586 | 0.000229 | null | 0.15758 | 0.000042 | 84,813.825469 | 91,321.397188 | null | null |
2018-05-10T10-41-22 | 2 | milan | 2,018 | null | 0.037553 | 0.000155 | null | 0.156905 | null | 79,924.521785 | 90,990.432843 | null | null |
2018-05-10T10-41-22 | 3 | milan | 2,018 | null | 0.037783 | 0.000233 | null | 0.1573 | 0.000097 | 81,222.136676 | 89,052.671446 | null | null |
2018-05-10T10-41-22 | 4 | milan | 2,018 | null | 0.038174 | 0.00017 | null | 0.157596 | 0.000258 | 74,172.889609 | 85,134.159453 | null | null |
2018-05-10T10-41-22 | 5 | milan | 2,018 | null | 0.037556 | 0.000136 | null | 0.156775 | null | 82,433.806016 | 93,540.481094 | null | null |
2018-05-10T10-41-22 | 6 | milan | 2,018 | null | 0.037813 | 0.000186 | null | 0.157115 | 0.00016 | 74,869.594037 | 85,331.790209 | null | null |
2018-05-10T10-41-22 | 7 | milan | 2,018 | null | 0.03778 | 0.000194 | null | 0.157158 | 0.000154 | 75,610.915 | 85,516.935938 | null | null |
2018-05-10T10-41-22 | 8 | milan | 2,018 | null | 0.038637 | 0.000209 | null | 0.157484 | 0.000056 | 77,567.636484 | 86,788.556875 | null | null |
2018-05-10T10-41-22 | 9 | milan | 2,018 | null | 0.037646 | 0.000165 | null | 0.156956 | 0.000065 | 77,485.845156 | 88,375.432266 | null | null |
2018-05-11T10-22-28 | 1 | milan | 2,018 | null | 0.035108 | 0.00022 | null | 0.15232 | null | null | null | null | null |
Dataset Card for MIL-QUALAIR
The dataset has been constructed for urban air pollution forecasting in task the Milan metropolitan area and includes Sentinel-5P satellite observations, meteorological conditions, topographical features, and ground monitoring station measurements.
Dataset Details
Dataset Description
The dataset encompasses a compilation of various data sources, including Sentinel-5 satellite observations, Digital Elevation Model (DEM) data, land cover information, meteorological records, and ground-level measurements, spanning the period from 2018 to 2023 within the metropolitan area of Milan. It is curated to support the task of forecasting the concentrations of five major pollutants namely PM10, PM25, NO2, O3, SO2. This dataset has been utilized and introduced in the study Urban Air Pollution Forecasting: A Machine Learning Approach Leveraging Satellite Observations and Meteorological Forecasts.
- Curated by: LINKS Foundation
- Funded by: UP2030 project
- License: MIT License
Dataset Sources [optional]
- Paper:
@misc{https://doi.org/10.48550/arxiv.2405.19901,
doi = {10.48550/ARXIV.2405.19901},
url = {https://arxiv.org/abs/2405.19901},
author = {Blanco, Giacomo and Barco, Luca and Innocenti, Lorenzo and Rossi, Claudio},
keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.m; G.3},
title = {Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts},
publisher = {arXiv},
year = {2024},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
Uses
The dataset is intended to serve as a comprehensive resource for researchers and practitioners interested in studying urban air quality dynamics and developing pollution forecasting models. With its diverse array of environmental data sources, including Sentinel-5 satellite observations, Digital Elevation Model (DEM) data, land cover information, meteorological records, and ground-level measurements, the dataset offers rich insights into the complex interplay of factors influencing air pollution levels in the Milan metropolitan area. Researchers can utilize this dataset to investigate correlations between different environmental variables and pollutant concentrations, identify patterns and trends over time, and develop and validate predictive models for air quality forecasting.
Direct Use
Major dataset use case is for the development of air pollution forecasting models. By combining various data sources within the dataset, users can create a comprehensive feature set for each day. This aggregated feature set provides a robust foundation for predicting the levels of the five supported pollutants with greater accuracy. The repository presents each data source separately, allowing users to follow the aggregation process outlined in the associated paper or develop their own methodology tailored to specific research objectives.
Dataset Structure
Sentinel 5P
- sentinel5.csv : Daily readings of Sentinel5P satellite bands, sampled around each station
DEM
- dem.tiff : 10m-resolution map of Milan metropolitan area with Digital Elevation model measurement
Weather
- weather.csv : Daily measurements of weather variables
Land Cover
- land_cover/land_cover.tiff : 10m-resolution map of Milan metropolitan area land cover classification
- land_cover/land_cover_taxonomy.json : Association between numeric class in tiff file and correspondent label
- land_cover/land_cover_mapping.json : Mapping of land cover classes as proposed in the original work
Ground truth
- stations.csv : Daily readings of station measurements for the five supported pollutants
Dataset Creation
Source Data
Sentinel 5P
- ESA Copernicus Sentinel 5p mission
DEM
- Copernicus
Weather
Land Cover
- Copernicus Land Monitoring Service - Urban Atlas
Ground truth
- Milan open data portal
Dataset Card Authors [optional]
Giacomo Blanco, Luca Barco, Lorenzo Innocenti and Claudio rossi
Dataset Card Contact
giacomo.blanco@linksfoundation.com, luca.barco@linksfoundation.com, lorenzo.innocenti@linksfoundation.com, claudio.rossi@linksfoundation.com
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