Abstract
In this paper, we present a Climate Change Parameter Dataset (CCPD) intending to achieve state-of-the-art results in parameters which effect climate change, including forest cover, water bodies, agriculture and vegetation, population, temperature, construction, and air index. The dataset can be used by the research community to validate the claims made in relation to the climate change. Research community has been deeply involved in extending the use case of machine learning algorithms to the effects of climate change. However, the non-availability of sufficient data related to climate change parameters has limited the research in this domain. By presenting this dataset, we want to facilitate the researchers. In this dataset, we provide a large variety of statistical and satellite data acquired by various image processing techniques and on-ground data collection. The data is collected in abundance for a specific region, and then various machine learning techniques are used to extract the useful data related to each parameter separately. We call this amalgam of processed data as CCPD dataset. CCPD dataset contains over 6000 data points for all seven parameters and covers the data from 1960 onwards. We hope this dataset will aid the research community in tackling climate change with the help of AI.
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References
Solomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences, 106(6), 1704–1709.
Young, O. R., & Steffen, W. (2009). The earth system: Sustaining planetary life-support systems. In Principles of ecosystem stewardship (pp. 295–315). Springer.
Warner, K., Hamza, M., Oliver-Smith, A., Renaud, F., & Julca, A. (2010). Climate change, environmental degradation and migration. Natural Hazards, 55(3), 689–715.
Ramanathan, V., & Feng, Y. (2009). Air pollution, greenhouse gases and climate change: Global and regional perspectives. Atmospheric Environment, 43(1), 37–50.
Stein, A. L. (2020). Artificial intelligence and climate change. Yale J. on Reg., 37, 890.
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., & Anderson, D. (2019). Viewing forced climate patterns through an AI lens. Geophysical Research Letters, 46(22), 13389–13398.
Wani, A., Gatoo, A., Bhat, A., Murtaza, S., Masoodi, T., Ahmad, S., Bhat, J., & Islam, M. A. (2020). Assessing drivers of deforestation and forest degradation in pirpanjal region of Kashmir Himalayas using geospatial approach. In Indian Forester (Vol. 146, pp. 1104–1114).
Farooq, M., Amin, A., Rashid, H., & Aasim, M. (2014). Geospatial monitoring of forests a case study of Pirpanjal forest division, Jandk. International Journal of Remote Sensing and Geoscience, 3, 16–25.
Ziou, D., Tabbone, S., et al. (1998). Edge detection techniques-an overview. Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 8, 537–559.
Lim, Y. W., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23(9), 935–952.
Wani, A., Joshi, P., Singh, O., Shafi, S. (2016). Multi-temporal forest cover dynamics in Kashmir Himalayan region for assessing deforestation and forest degradation in the context of redd+ policy. Journal of Mountain Science, 13, 1431–1441.
Leh, M., Bajwa, S., & Chaubey, I. (2013). Impact of land use change on erosion risk: An integrated remote sensing, geographic information system and modeling methodology. Land Degradation & Development, 24(5), 409–421.
Gascon, F., Biasutti, R., Ferrara, R., Fischer, P., Galli, L., Hoersch, B., Hopkins, S., Jackson, J., Lavender, S., Mica, S., Beaton, A., Paciucci, A., Paul, F., Pinori, S., & Saunier, S. (2014). European space agency (esa) landsat mss/tm/etm+ archive bulk-processing: Processor improvements and data quality.
Rufin, P., Rabe, A., Nill, L., & Hostert, P. (2021). Gee timeseries explorer for QGIS—Instant access to petabytes of earth observation data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVI-4/W2-2021, 155–158.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ashraf, T., Bashir, J. (2024). Climate Change Parameter Dataset (CCPD): A Benchmark Dataset for Climate Change Parameters in Jammu and Kashmir. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 818. Springer, Singapore. https://doi.org/10.1007/978-981-99-7862-5_1
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DOI: https://doi.org/10.1007/978-981-99-7862-5_1
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