Academic literature on the topic 'CAMELS dataset'
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Journal articles on the topic "CAMELS dataset"
Chagas, Vinícius B. P., Pedro L. B. Chaffe, Nans Addor, Fernando M. Fan, Ayan S. Fleischmann, Rodrigo C. D. Paiva, and Vinícius A. Siqueira. "CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil." Earth System Science Data 12, no. 3 (September 8, 2020): 2075–96. http://dx.doi.org/10.5194/essd-12-2075-2020.
Full textAlvarez-Garreton, Camila, Pablo A. Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano-Bigiarini, Antonio Lara, et al. "The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset." Hydrology and Earth System Sciences 22, no. 11 (November 13, 2018): 5817–46. http://dx.doi.org/10.5194/hess-22-5817-2018.
Full textFowler, Keirnan J. A., Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel. "CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia." Earth System Science Data 13, no. 8 (August 6, 2021): 3847–67. http://dx.doi.org/10.5194/essd-13-3847-2021.
Full textCoxon, Gemma, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, et al. "CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain." Earth System Science Data 12, no. 4 (October 12, 2020): 2459–83. http://dx.doi.org/10.5194/essd-12-2459-2020.
Full textJehn, Florian U., Konrad Bestian, Lutz Breuer, Philipp Kraft, and Tobias Houska. "Using hydrological and climatic catchment clusters to explore drivers of catchment behavior." Hydrology and Earth System Sciences 24, no. 3 (March 5, 2020): 1081–100. http://dx.doi.org/10.5194/hess-24-1081-2020.
Full textKlingler, Christoph, Karsten Schulz, and Mathew Herrnegger. "LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe." Earth System Science Data 13, no. 9 (September 16, 2021): 4529–65. http://dx.doi.org/10.5194/essd-13-4529-2021.
Full textKratzert, Frederik, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, and Grey Nearing. "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets." Hydrology and Earth System Sciences 23, no. 12 (December 17, 2019): 5089–110. http://dx.doi.org/10.5194/hess-23-5089-2019.
Full textHaq, Wajiha, Syed Hassan Raza, and Muhammad Wasif Malik. "Missed takes towards a pandemic of COVID-19? A systematic literature review of Coronavirus related diseases in Pakistan." Journal of Infection in Developing Countries 14, no. 07 (July 31, 2020): 726–31. http://dx.doi.org/10.3855/jidc.12771.
Full textAyzel, Georgy, and Maik Heistermann. "The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset." Computers & Geosciences 149 (April 2021): 104708. http://dx.doi.org/10.1016/j.cageo.2021.104708.
Full textArdabili, Ahad Khaleghi, Zied Othman Ahmed, and Ali Layth Abbood. "Solving Routing Problem Using Improved Camel Herds Algorithm." International Journal on Perceptive and Cognitive Computing 6, no. 2 (December 14, 2020): 53–59. http://dx.doi.org/10.31436/ijpcc.v6i2.157.
Full textDissertations / Theses on the topic "CAMELS dataset"
Bizzarri, Diletta. "Classification of large-scale catchments data-sets: use of seasonality statistics in the identification of flood typology." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textJenkins, Jacob Luke. "Navigating campus: a geospatial approach to 3-D routing." Thesis, Kansas State University, 2013. http://hdl.handle.net/2097/15638.
Full textDepartment of Landscape Architecture/Regional and Community Planning
Howard Hahn
Evolving needs for universities, municipalities, and corporations demand more sustainable and efficient techniques for data management. Geographic Information Systems (GIS) enables decision makers to spatially analyze the built environment to better understand facility usage by running test scenarios to evaluate current efficiencies and identify opportunities for investment. This can only be conducted when data is organized and leveraged across many departments in a collaborative environment. Data organization through GIS encourages interdepartmental collaboration uniting all efforts on a common front. An organized system facilitates a working relationship between the university and the community of Manhattan increasing efficiency, developing sustainable practices, and enhancing the health and safety of Kansas State University and larger community. Efficiency is increased through automation of many current practices such as work requests and routine maintenance. Sustainable practices will be developed by generating self-guided campus tours and identifying area appropriate for bioswales. Lastly, safety will be enhanced throughout campus by increasing emergency response access, determining areas within buildings difficult to reach in emergency situations, and identifying unsafe areas on campus. Evolving needs for universities, municipalities, and corporations demand more sustainable and efficient techniques for data management. Geographic Information Systems (GIS) enables decision makers to spatially analyze the built environment to better understand facility usage by running test scenarios to evaluate current efficiencies and identify opportunities for investment. This can only be conducted when data is organized and leveraged across many departments in a collaborative environment. Data organization through GIS encourages interdepartmental collaboration uniting all efforts on a common front. An organized system facilitates a working relationship between the university and the community of Manhattan increasing efficiency, developing sustainable practices, and enhancing the health and safety of Kansas State University and larger community. Efficiency is increased through automation of many current practices such as work requests and routine maintenance. Sustainable practices will be developed by generating self-guided campus tours and identifying area appropriate for bioswales. Lastly, safety will be enhanced throughout campus by increasing emergency response access, determining areas within buildings difficult to reach in emergency situations, and identifying unsafe areas on campus. Optimizing data management for Kansas State University was conducted in three phases. First, a baseline assessment for facility management at Kansas State University was conducted through discussions with campus departments. Second, case study interviews and research was conducted with leaders in GIS management. Third, practices for geospatial data management were adapted and implemented for Kansas State University: the building of a centralized database, constructing a 3-dimensional routing network, and modeling a virtual campus in 3D.
Books on the topic "CAMELS dataset"
Bazen, Jacques. University spin-offs and economic impact on semi-peripheral regions in the Netherlands. Hogeschool Saxion, lectoraat Regio Ontwikkeling, 2020. http://dx.doi.org/10.14261/f58678f3-daa8-4422-aab7c7fcafa8966d.
Full textBook chapters on the topic "CAMELS dataset"
Bukhari, Syed Saqib, Faisal Shafait, and Thomas M. Breuel. "The IUPR Dataset of Camera-Captured Document Images." In Camera-Based Document Analysis and Recognition, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_13.
Full textNagy, Robert, Anders Dicker, and Klaus Meyer-Wegener. "NEOCR: A Configurable Dataset for Natural Image Text Recognition." In Camera-Based Document Analysis and Recognition, 150–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_12.
Full textKumar, Jayant, Peng Ye, and David Doermann. "A Dataset for Quality Assessment of Camera Captured Document Images." In Camera-Based Document Analysis and Recognition, 113–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3_9.
Full textZhu, Ziyi, Liangcai Gao, Yibo Li, Yilun Huang, Lin Du, Ning Lu, and Xianfeng Wang. "NTable: A Dataset for Camera-Based Table Detection." In Document Analysis and Recognition – ICDAR 2021, 117–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_8.
Full textJanoch, Allison, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, and Trevor Darrell. "A Category-Level 3D Object Dataset: Putting the Kinect to Work." In Consumer Depth Cameras for Computer Vision, 141–65. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4640-7_8.
Full textBanerjee, Soumya. "Analysis of a Planetary Scale Scientific Collaboration Dataset Reveals Novel Patterns." In First Complex Systems Digital Campus World E-Conference 2015, 85–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45901-1_7.
Full textDenina, Giovanni, Bir Bhanu, Hoang Thanh Nguyen, Chong Ding, Ahmed Kamal, Chinya Ravishankar, Amit Roy-Chowdhury, Allen Ivers, and Brenda Varda. "VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication." In Distributed Video Sensor Networks, 335–47. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_23.
Full textZhu, Zunjie, Feng Xu, Mingzhu Li, Zheng Wang, and Chenggang Yan. "Challenges from Fast Camera Motion and Image Blur: Dataset and Evaluation." In Computer Vision – ECCV 2020 Workshops, 211–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-68238-5_16.
Full textWang, Zhe, Daeyun Shin, and Charless C. Fowlkes. "Predicting Camera Viewpoint Improves Cross-Dataset Generalization for 3D Human Pose Estimation." In Computer Vision – ECCV 2020 Workshops, 523–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66096-3_36.
Full textLiciotti, Daniele, Marina Paolanti, Emanuele Frontoni, Adriano Mancini, and Primo Zingaretti. "Person Re-identification Dataset with RGB-D Camera in a Top-View Configuration." In Video Analytics. Face and Facial Expression Recognition and Audience Measurement, 1–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56687-0_1.
Full textConference papers on the topic "CAMELS dataset"
Watanabe, Tatsuhisa, Tomoharu Nakashima, and Yoshifumi Kusunoki. "Change Detection For Area Surveillance Using A Moving Camera." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0220.
Full textMalon, Thierry, Geoffrey Roman-Jimenez, Patrice Guyot, Sylvie Chambon, Vincent Charvillat, Alain Crouzil, André Péninou, Julien Pinquier, Florence Sèdes, and Christine Sénac. "Toulouse campus surveillance dataset." In MMSys '18: 9th ACM Multimedia Systems Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3204949.3208133.
Full textLiu, Qianhui, Dong Xing, Huajin Tang, De Ma, and Gang Pan. "Event-based Action Recognition Using Motion Information and Spiking Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/240.
Full textScheerlinck, Cedric, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. "CED: Color Event Camera Dataset." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2019. http://dx.doi.org/10.1109/cvprw.2019.00215.
Full textSharma, Anil. "Intelligent Querying in Camera Networks for Efficient Target Tracking." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/918.
Full textYu, Zhengxu, Zhongming Jin, Long Wei, Jishun Guo, Jianqiang Huang, Deng Cai, Xiaofei He, and Xian-Sheng Hua. "Progressive Transfer Learning for Person Re-identification." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/586.
Full textZheng, Feng, Xin Miao, and Heng Huang. "Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/514.
Full textLi, Wenhui, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, and Mohan Kankanhalli. "Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking." In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017. http://dx.doi.org/10.1109/wacv.2017.28.
Full textXiong, Shaomin, Haoyu Wu, and Toshiki Hirano. "A New Human Intruder Detection Scheme for Video Surveillance." In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7490.
Full textAbdelhamed, Abdelrahman, Stephen Lin, and Michael S. Brown. "A High-Quality Denoising Dataset for Smartphone Cameras." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00182.
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