Ranga Vatsavai

Ramachandra, B., Jones, M. J., & Vatsavai, R. R. (2022). A Survey of Single-Scene Video Anomaly Detection. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 5. https://doi.org/10.1109/TPAMI.2020.3040591

Chen, Z., Dutton, B., Ramachandra, B., Wu, T., & Vatsavai, R. R. (2021). Local Clustering with Mean Teacher for Semi-supervised learning. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). https://doi.org/10.1109/ICPR48806.2021.9412469

Ramachandra, B., Jones, M., & Vatsavai, R. R. (2021). Perceptual metric learning for video anomaly detection. MACHINE VISION AND APPLICATIONS, 32(3). https://doi.org/10.1007/s00138-021-01187-5

Ramachandra, B., Dutton, B., & Vatsavai, R. R. (2019). Anomalous cluster detection in spatiotemporal meteorological fields. STATISTICAL ANALYSIS AND DATA MINING, 12(2), 88–100. https://doi.org/10.1002/sam.11398

Pool, N., & Vatsavai, R. R. (2018). Deformable Part Models for Complex Object Detection in Remote Sensing Imagery. BIGSPATIAL 2018: PROCEEDINGS OF THE 7TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ANALYTICS FOR BIG GEOSPATIAL DATA (BIGSPATIAL-2018), pp. 57–62. https://doi.org/10.1145/3282834.3282843

Shashidharan, A., Vatsavai, R. R., & Meentemeyer, R. K. (2018). FUTURES-DPE: Towards Dynamic Provisioning and Execution of Geosimulations in HPC environments. 26TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2018), pp. 464–467. https://doi.org/10.1145/3274895.3274948

Gadiraju, K. K., Vatsavai, R. R., Kaza, N., Wibbels, E., & Krishna, A. (2018). Machine Learning Approaches for Slum Detection Using Very High Resolution Satellite Images. 2018 18TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), pp. 1397–1404. https://doi.org/10.1109/ICDMW.2018.00198

Ramachandra, B., Nawathe, P., Monroe, J., Han, K., Ham, Y., & Vatsavai, R. R. (2018). Real-Time Energy Audit of Built Environments: Simultaneous Localization and Thermal Mapping. JOURNAL OF INFRASTRUCTURE SYSTEMS, 24(3). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000431

Chen, Z., Ramachandra, B., & Vatsavai, R. R. (2017). Hierarchical change detection framework for biomass monitoring. 2017 ieee international geoscience and remote sensing symposium (igarss), 620–623. https://doi.org/10.1109/igarss.2017.8127030

Bhangale, U., Durbha, S. S., King, R. L., Younan, N. H., & Vatsavai, R. R. (2017). High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data. Remote Sensing of Environment, 202, 28–44. https://doi.org/10.1016/j.rse.2017.03.024

Prasad, S. K., Aghajarian, D., McDermott, M., Shah, D., Mokbel, M., Puri, S., … Wang, S. W. (2017). Parallel processing over spatial-temporal datasets from geo, bio, climate and social science communities: A research roadmap. 2017 ieee 6th international congress on big data (bigdata congress 2017), 232–250. https://doi.org/10.1109/bigdatacongress.2017.39

Kurte, K. R., Durbha, S. S., King, R. L., Younan, N. H., & Vatsavai, R. R. (2017). Semantics-enabled framework for spatial image information mining of linked earth observation data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1), 29–44. https://doi.org/10.1109/jstars.2016.2547992

Connors, C., & Vatsavai, R. R. (2017). Semi-supervised deep generative models for change detection in very high resolution imagery. 2017 ieee international geoscience and remote sensing symposium (igarss), 1063–1066. https://doi.org/10.1109/igarss.2017.8127139

Hong, S., & Vatsavai, R. R. (2016). A A scalable probabilistic change detection algorithm for very high resolution (VHR) satellite imagery. 2016 ieee international congress on big data - bigdata congress 2016, 275–282. https://doi.org/10.1109/bigdatacongress.2016.42

Ramachandra, B., Gadiraju, K. K., Vatsavai, R. R., Kaiser, D. P., & Karnowski, T. P. (2016). Detecting Extreme Events in Gridded Climate Data. Procedia Computer Science, 80, 2397–2401. https://doi.org/10.1016/J.PROCS.2016.05.537

Vatsavai, R. R., & Chandola, V. (2016). Guest editorial: Big spatial data. Geoinformatica, 20(4), 797–799. https://doi.org/10.1007/s10707-016-0269-7

Idrobo, J. C., Rusz, J., Spiegelberg, J., McGuire, M. A., Symons, C. T., Vatsavai, R. R., … Lupini, A. R. (2016). Mapping Magnetic Ordering With Aberrated Electron Probes in STEM. Microscopy and Microanalysis, 22(S3), 1676–1677. https://doi.org/10.1017/S1431927616009223

Karpatne, A., Jiang, Z., Vatsavai, R. R., Shekhar, S., & Kumar, V. (2016). Monitoring land-cover changes: A machine-learning perspective. IEEE Geoscience and Remote Sensing Magazine, 4(2), 8–21. https://doi.org/10.1109/mgrs.2016.2528038

Chen, Z. X., Vatsavai, R. R., Ramachandra, B., Zhang, Q., Singh, N., & Sukumar, S. (2016). Scalable nearest neighbor based hierarchical change detection framework for crop monitoring. 2016 IEEE International Conference on Big Data (Big Data), 1309–1314. https://doi.org/10.1109/bigdata.2016.7840735

Hong, S., & Vatsavai, R. R. (2016). Sliding Window-based Probabilistic Change Detection for Remote-sensed Images. Procedia Computer Science, 80, 2348–2352. https://doi.org/10.1016/J.PROCS.2016.05.438

Shashidharan, A., Berkel, D. B., Vatsavai, R. R., & Meentemeyer, R. K. (2016). pFUTURES: A parallel framework for cellular automaton based urban growth models. Geographic information science, (giscience 2016), 9927, 163–177. https://doi.org/10.1007/978-3-319-45738-3_11

Vatsavai, R. R. (2015). A A scalable complex pattern mining framework for global settlement mapping. 2015 IEEE International Congress on Big Data - BigData congress 2015, 514–521. https://doi.org/10.1109/bigdatacongress.2015.81

Vatsavai, R. R. (2015). Multitemporal data mining: From biomass monitoring to nuclear proliferation detection. 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp). https://doi.org/10.1109/multi-temp.2015.7245751

Vatsavai, R. R., & Graesser, J. (2012). Probabilistic Change Detection Framework for Analyzing Settlement Dynamics Using Very High-resolution Satellite Imagery. Procedia Computer Science, 9, 907–916. https://doi.org/10.1016/j.procs.2012.04.097

Vatsavai, R. R., & Bhaduri, B. (2011). A hybrid classification scheme for mining multisource geospatial data. GeoInformatica, 15(1), 29–47. https://doi.org/10.1007/S10707-010-0113-4

Chandola, V., & Vatsavai, R. R. (2011). A scalable gaussian process analysis algorithm for biomass monitoring. Statistical Analysis and Data Mining, 4(4), 430–445. https://doi.org/10.1002/sam.10129

Hoffman, F. M., Larson, J. W., Mills, R. T., Brooks, B.-G. J., Ganguly, A. R., Hargrove, W. W., … Vatsavai, R. R. (2011). Data Mining in Earth System Science (DMESS 2011). Procedia Computer Science, 4, 1450–1455. https://doi.org/10.1016/j.procs.2011.04.157

Vatsavai, R. R., Symons, C. T., Chandola, V., & Jun, G. (2011). GX-Means: A model-based divide and merge algorithm for geospatial image clustering. Procedia Computer Science, 4, 186–195. https://doi.org/10.1016/j.procs.2011.04.020

Vatsavai, R. R. (2009). Incremental Clustering Algorithm for Earth Science Data Mining. In G. Allen, J. Nabrzyski, E. Seidel, G. D. van Albada, J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2009 (pp. 375–384). https://doi.org/10.1007/978-3-642-01973-9_42

Vatsavai, R. R., Shekhar, S., & Bhaduri, B. (2008). A Learning Scheme for Recognizing Sub-classes from Model Trained on Aggregate Classes. In Lecture Notes in Computer Science (pp. 967–976). https://doi.org/10.1007/978-3-540-89689-0_100

Vatsavai, R. R., Shekhar, S., & Burk, T. E. (2007). An efficient spatial semi-supervised learning algorithm. International Journal of Parallel, Emergent and Distributed Systems, 22(6), 427–437. https://doi.org/10.1080/17445760701207546

Mignet, L., Basak, J., Bhide, M., Roy, P., Roy, S., Sengar, V. S., … Vadapalli, S. (2006). Improving DB2 Performance Expert – A Generic Analysis Framework. In Lecture Notes in Computer Science (pp. 1097–1101). https://doi.org/10.1007/11687238_68

Vatsavai, R. R., Shekhar, S., Burk, T. E., & Lime, S. (2006). UMN-MapServer: A High-Performance, Interoperable, and Open Source Web Mapping and Geo-spatial Analysis System. In Geographic Information Science (pp. 400–417). https://doi.org/10.1007/11863939_26

Kazar, B. M., Shekhar, S., Lilja, D. J., Vatsavai, R. R., & Pace, R. K. (2004). Comparing Exact and Approximate Spatial Auto-regression Model Solutions for Spatial Data Analysis. In Geographic Information Science (pp. 140–161). https://doi.org/10.1007/978-3-540-30231-5_10