Update README.md

README.md

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----
-license: mit
-task_categories:
-- time-series-forecasting
-- tabular-regression
-language:
-- en
-- el
-- es
-- it
-tags:
-- air
-- quality
-- forecasting
-- imputation
-- analysis
-- pollution
-- weather
-pretty_name: RegionalAQDatasets
-size_categories:
-- 1M<n<10M
----
+---
+license: mit
+task_categories:
+- time-series-forecasting
+- tabular-regression
+language:
+- en
+- el
+- es
+- it
+tags:
+- air
+- quality
+- forecasting
+- imputation
+- analysis
+- pollution
+- weather
+pretty_name: RegionalAQDatasets
+size_categories:
+- 1M<n<10M
+---
+
+
+# Regional Datasets for Air Quality Monitoring in European Cities
+
+by
+_Georgios-Fotios Angelis,
+Alexandros Emvoliadis,
+Traianos-Ioannis Theodorou,
+Alexandros Zamichos,
+Anastasios Drosou
+and Dimitrios Tzovaras_
+
+
+Accepted as an oral presentation paper in the **IGARSS 2024**.
+
+This repository contains the data and source code used to produce the results of the paper
+
+
+## Overview
+![model architecture_process.png](assets/all_stations.png)
+
+
+### Abstract
+
+The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and
+well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover
+indices is appropriate. To this end, this paper introduces three
+European cities’ spatiotemporal datasets, customized for air
+pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use
+information. The duration is approximately from 2020 to 2023
+with an hourly temporal resolution and a spatial resolution of
+0.005◦. The temporal and spatiotemporal datasets are publicly
+released aiming to provide a solid foundation for researchers,
+analysts, and practitioners to conduct in-depth analyses of air
+pollution dynamics.
+
+### Motivation
+Detailed monitoring of pollutant variables provides valuable insights into human activities impacting air quality and pollution sources. Advancements in sensor technologies, satellites, UAVs, and data analysis tools have enhanced the quality and quantity of air quality data, enabling better decision-making and analysis. However, creating air quality datasets is time-consuming, requiring significant effort. To address these challenges, we aggregate data from diverse sources to provide high-quality air pollution data enriched with contextual information.
+
+
+### Contributions
+In summary we make the following contributions:
+- We have collected and aligned diverse air quality data from three
+European cities, offering high temporal accuracy (onehour resolution) for detailed analysis and low spatial
+resolution for regional monitoring.
+- We have enhanced data through anomaly detection and imputation, producing three ready-to-use datasets for air quality
+modeling.
+- We have integrated land usage features, revealing insights into
+the link between urban development, land use, and air
+quality.
+
+
+
+[//]: # (### Usage)
+
+## License
+
+All Python source code (including `.py` and `.ipynb` files) is made available
+under the MIT license. You can freely use and modify the code, without
+warranty, so long as you provide attribution to the authors. See
+`LICENSE-MIT.txt` for the full license text.
+
+[cc-by]: https://creativecommons.org/licenses/by/4.0/
+
+
+
+
+## BibTeX Citation
+
+If you use our datasets in a scientific publication, we would appreciate using the following citations:
+
+```
+@INPROCEEDINGS{10640879,
+author={Angelis, G.- F. and Emvoliadis, A. and Theodorou, T.-I. and Zamichos, A. and Drosou, A. and Tzovaras, D.},
+booktitle={IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
+title={Regional Datasets for Air Quality Monitoring in European Cities},
+year={2024},
+volume={},
+number={},
+pages={6875-6880},
+keywords={Urban areas;Europe;Land surface;Air pollution;Solids;Spatiotemporal phenomena;Pollution measurement;Air pollution;datasets;temporal;spatiotemporal},
+doi={10.1109/IGARSS53475.2024.10640879}
+
+
+```
+
+## Funding
+This research was supported by grants from Horizon 2020, the European Union’s Programme for Research and Innovation under Grant Agreement No. 101037648 - SOCIO-BEE.
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