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links-ads
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mil-qualair

Tasks:

Tabular Regression

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1K<n<10K

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The dataset generation failed because of a cast error

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)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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

End of preview.

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

Visual crossing 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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