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Dataset of looted archaeological sites 

Dataset recorded in front of the University of Luxembourg Belval Campus for the aim of drone visual localization algorithms evaluation. Included files: 5 fullHD video sequences numbered from 3 to 7 were sampled at 30FPS Calibration sequence and camera parameter file Poses files, i.e. extrinsic parameters of the cameras for each image Sparse reconstruction with GPS coordinates alignment Part 1 of this dataset is available at: 10.5281/zenodo.7242900

Dataset recorded in front of the University of Luxembourg Belval Campus for the aim of drone visual localization algorithms evaluation. Included files: 5 fullHD video sequences numbered from 3 to 7 were sampled at 30FPS Calibration sequence and camera parameter file Poses files, i.e. extrinsic parameters of the cameras for each image Sparse reconstruction with GPS coordinates alignment The Part 2 of this dataset is available at: 10.5281/zenodo.7243492

These are plot data from fifteen 40 m by 40 m sample plots established in Oban Division of Cross River National Park, Nigeria, between 23rd August 2019 and 9th September 2019. We have also included data summaries and RStudio codes used for analysis and generating results for the manuscript entitled: "Impact of human foraging on tree diversity, composition and abundance in a tropical rainforest", submitted for publication as an original research article in Biotropica. All data and R code required to generate the results as shown in the manuscript have been included. https://doi.org/10.5061/dryad.kh189328z

Project TISPLALI studied the use of drone-based thermal infrared imaging using known plastic targets deployed at sea. The published study can be found at: https://doi.org/10.3390/rs14133179. Created via Ocean Scan.Campaign ID: ee6a58ca-af2c-4813-a7ba-f2a099544c9a.

Dataset related to publication "Active hyperspectral sensing (AHS) applications for mineral deposit exploration"

ChinaHighPM10 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM10 dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.86 and a root-mean-square error (RMSE) of 24.28 µg m-3 on a daily basis. Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM10 dataset is available at: http://doi.org/10.5281/zenodo.3752465 More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290"]} Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).

ChinaHighPM2.5 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM2.5 dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with average cross-validation coefficient of determination (CV-R2) values ranging from 0.86 to 0.90, and RMSE values ranging from 10.0 to 18.4 μg/m3 on a daily basis. Note that this dataset is closed access since a longer-term (2000-2021), seamless, high-resolution (1 km), and higher quality ChinaHighPM2.5 dataset (Version 4) is available at: http://doi.org/10.5281/zenodo.3539349 More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136", "Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees, Atmospheric Chemistry and Physics, 2020, 20(6), 3273-3289. https://doi.org/10.5194/acp-20-3273-2020"]} Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).

ChinaHighPM10 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level ground-level PM10 products in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.86 and a root-mean-square error (RMSE) of 24.34 µg m-3 on a daily basis. Note that this dataset is closed access since a longer-term, high-resolution (1 km), and higher quality ChinaHighPM10 dataset is available at: http://doi.org/10.5281/zenodo.3752465 More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html {"references": ["Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]} Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).