Recent Publications
- Nealey, I., Encinas Pacheco, D., Gomez Moreno, I., Floca, M., Crawl, D., and Altintas, I. "A Science-Enabled Virtual Reality Demonstration to Increase Social Acceptance of Prescribed Burns," Under review for presentation at IEEE eScience 2022.
- Tan, L., de Callafon, R. A., Block, J., Crawl, D., Tolga Çağlar, Altıntaş, I., "Estimation of wildfire wind conditions via perimeter and surface area optimization", in the Journal of Computational Science, Volume 61, 2022, https://doi.org/10.1016/j.jocs.2022.101633.
- Tan, L., de Callafon, R.A., Altıntaş, I. (2022). Characterizing Wildfire Perimeter Polygons from QUIC-Fire. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_44
- Altintas, I., Perez, I., Mishin, D., Trouillaud, A., Irving, C., Graham, J., Tatineni, M., DeFanti, T., Strande, S., Smarr, L., and Norman, M.L. "Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing," in the proceedings of the IEEE 18th International Conference on e-Science, October 11-14, 2022, Salt Lake City, Utah, USA. https://arxiv.org/abs/2211.06918
- Roten, D., Block, J., Crawl, D., Lee, J., and Altintas, I., “Machine Learning for Improved Post-fire Debris Flow Likelihoods Prediction," in the proceedings of the 2022 IEEE International Conference on Big Data (IEEE BigData 2022). Dec 17-20, 2022, Osaka, Japan.
- Baru, C., Pozmantier, M., Altintas, I., Baek, S., Cohen, J., Condon, L., Fanti, G., Fernandez, R. C., Jackson, E., Lall, U., Landman, B., Li, H. H., Marin, C., Lopez, B. M., Metaxas, D., Olsen, B., Page, G., Shang, J., Turkan, Y., and Zhang, P. (2022). "Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D." AI magazine, 43(1), 93-104.https://doi.org/10.
1002/aaai.12042 . - Dewangan, A., Pande, Y., Braun, H-W., Vernon, F., Perez, I., Altintas, I., Cottrell, G.W., and Nguyen, M.H. (2022). "FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection." Remote Sensing, 14(4):1007. https://doi.org/10.3390/
rs14041007 .
Overview paper:
- Altintas I., Block J., de Callafon R., Crawl D., Cowart C., Gupta A., Nguyen M., Braun H.W., Schulze J., Gollner M., Trouve A., Smarr L., Towards an Integrated Cyberinfrastructure for Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience of Wildfires. In Proceeedings of the Workshop on Dynamic Data-Driven Application Systems (DDDAS) at the 15th International Conference on Computational Science (ICCS 2015). doi:10.1016/j.procs.2015.05.296. Best Workshop Paper Award.
Ensembles and Data Assimilation
- Srivas, T., de Callafon, R., Crawl, D., Altintas, I., Data Assimilation of Wildfires with Fuel Adjustment Factors in FARSITE using Ensemble Kalman Filtering, In Proceedings of the Workshop on Data-Driven Computational Sciences (DDCS) at the 17th International Conference on Computational Science (ICCS 2017), 2017.
- Srivas, T., Artés, T., de Callafon, R., Altintas, I., Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering, In the Data-Driven Computational Sciences Workshop at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.328
Fire Modeling
- R.R. Linn, S.L. Goodrick, S. Brambilla, M.J. Brown, R.S. Middleton, J.J. O'Brien, J.K. Hiers, QUIC-fire: A fast-running simulation tool for prescribed fire planning, Environmental Modelling & Software, Volume 125, 2020, 104616, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2019.104616.
- Mélanie Rochoux, Cong Zhang, Michael Gollner and Arnaud Trouvé "Designing the Future of Wildfire Modeling." Wildfire magazine (March-April 2017)
- Cong Zhang, Mélanie Rochoux, Wei Tang, Michael Gollner, Jean-Baptiste Filippi, and Arnaud Trouvé (2017). "Evaluation of a data-driven wildland fire spread forecast model with spatially-distributed parameter estimation in simulations of the FireFlux I field-scale experiment." 12th International Symposium on Fire Safety Science, Lund, Sweden.
- Wei Tang, Daniel J. Gorham, Mark A. Finney, Sara Mcallister, Jack Cohen, Jason Forthofer, and Michael J. Gollner (2017). "An experimental study on the intermittent extension of flames in wind-driven fires." 12th International Symposium on Fire Safety Science, Lund, Sweden.
- Cong Zhang, Mélanie Rochoux, Annabelle Collin, Wei Tang, Michael Gollner, Evan Ellicott, Philippe Moireau, and Arnaud Trouvé (2017) "Front Shape Comparison in Data-Driven Wildland Fire Spread Simulations. " 10th U.S. National Combustion Meeting, College Park, Maryland.
- Cong Zhang, Mélanie Rochoux, Wei Tang, Maria Theodori, Michael Gollner, and Arnaud Trouvé (2016) "Field-Scale Validation of Data-Driven Wildland Fire Spread Simulations." 5th International Fire Behavior and Fuels Conference, Portland, Oregon.
- Cong Zhang, Mélanie Rochoux, Wei Tang, Michael Gollner, Jean-Baptiste Filippi, and Arnaud Trouvé (2017). "Evaluation of a data-driven wildland fire spread forecast model with spatially-distributed parameter estimation in simulations of the FireFlux I field-scale experiment." Fire Safety Journal, in press. https://doi.org/10.1016/j.firesaf.2017.03.057
- Mélanie Rochoux, Annabelle Collin, Cong Zhang, Arnaud Trouvé, Didier Lucor and Philippe Moireau (2017). "Front shape similarity measure for front position sensitivity analysis and data assimilation." ESAIM: Proceedings and Surveys (submitted).
- Wei Tang, Daniel J. Gorham, Mark A. Finney, Sara Mcallister, Jack Cohen, Jason Forthofer, and Michael J. Gollner (2017). "An experimental study on the intermittent extension of flames in wind-driven fires." Fire Safety Journal, in press. https://doi.org/10.1016/j.firesaf.2017.03.030
- Wei Tang, Colin Miller, Michael Gollner (2016), "Local flame attachment and heat fluxes in wind-driven line fires." Proceedings of the Combustion Institute, 36(2), 3253-3261. http://dx.doi.org/10.1016/j.proci.2016.06.064
- Tang, W., Miller, C., Gollner, M., Local flame attachment and heat fluxes in wind-driven line fires, In Proceedings of the Combustion Institute, Available online 21 June 2016, ISSN 1540-7489, doi:10.1016/j.proci.2016.06.064.
- C. Zhang, M. Rochoux, W. Tang, M. Gollner, A. Trouve, Field-Scale Validation of Data-Driven Wildland Fire Spread Simulations, In 5th International Fire Behavior and Fuels Conference, 2016.
- Tang, W., Miller, C., Gollner, M., Forward Heating in Wind-Driven Fire Spread, In 5th International Fire Behavior and Fuels Conference, 2016.
- Miller, C.H., Tang, W., Verma, S., Trouve, A., Gollner, M.J. (2015). A fundamental exploration of Flame Structure in Wildland Fires, In 5th International Fire Behavior and Fuels Conference, 2016.
- Miller, C.H., Tang, W., Verma, S., Trouve, A., Gollner, M.J. (2015). A fundamental exploration of Flame Structure in Wildland Fires, In 6th International Fire Ecology & Management Congress, 2016.
- Gollner, M., Trouve, A., Altintas, I., Block, J., de Callafon, R., Clements, C., Cortes, A., Ellicott, E., Filippi, Jean Baptiste, Finney, M., Ide, K., Jenkins, Mary A., Jimenez, D., Lautenberger, C., Mandel, J., Rochoux, M., and Simeoni, A., Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop, 2015. [PDF]
Workflows and Services
- Nguyen, M., Crawl, D., Li, J., Uys, D., Altintas, I., Automated Scalable Detection of Location-Specific Santa Ana Conditions from Weather Data using Unsupervised Learning, In Proceedings of the 2017 IEEE International Conference on Big Data.
- Crawl, D., Block, J., Lin, K., Altintas, I., Firemap: A Dynamic Data-Driven Predictive Wildfire Modeling and Visualization Environment, In Proceedings of the Workshop on Urgent Computing (UC) at the 17th International Conference on Computational Sciences (ICCS 2017), 2017.
- Nguyen, M., Uys, D., Crawl, D., Cowart, C., and Altintas, I., A Scalable Approach for Location-Specific Detection of Santa Ana Conditions, In Proceedings of the 2016 IEEE International Conference on Big Data.
- Crawl, D., Singh, A., Altintas, I., Kepler WebView: A Lightweight, Portable Framework for Constructing Real-time Web Interfaces of Scientific Workflows, In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.361
- Artés, T., Crawl, D., Cortes A., Altintas, I. Forest fire spread prediction system workflow: an experience using Kepler, In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016).
- Wang J., Crawl D., Purawat S., Nguyen M., Altintas I., Big Data Provenance: Challenges, State of the Art and Opportunities, in 2nd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), 2015.
Machine Learning
- Nguyen, M. H., Block, J., Crawl, D., Siu, V., Bhatnagar, A., Rodriguez, F., Kwan, A., Baru, N., and Altintas, I. “Land Cover Classification at the Wildland Urban Interface using High-Resolution Satellite Imagery and Deep Learning,” in the 2018 IEEE International Conference on Big Data.
- Yazdani, M., Nguyen, M., Block, J., Crawl, D., Zurutuza, N., Kim, D., Hanson, G., and Altintas, I., Scalable Detection of Rural Schools in Africa using Convolutional Neural Networks and Satellite Imagery, In the fifth international workshop on Smart City Clouds: Technologies, Systems and Applications (SCCTSA) at the IEEE/ACM International Conference on Utility and Cloud Computing (UCC), 2018.
- Jessica Block, Mehrdad Yazdani, Mai Nguyen, Daniel Crawl, Marta Jankowska, John Graham, Tom DeFanti, and Ilkay Altintas, An Unsupervised Deep Learning Approach for Satellite Image Analysis with Applications in Demographic Analysis, In the thirteenth IEEE eScience conference, 2017.
- Nguyen, M., Crawl, D., Masoumi, T., Altintas, T., Integrated Machine Learning in the Kepler Scientific Workflow System, In proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.545
Posters
- Jason Koh, Sandeep Sandha, Bharathan Balaji, Daniel Crawl, Ilkay Altintas, Rajesh Gupta, Mani Srivastava, Data Hub Architecture for Smart Cities, In the 15th ACM Conference on Embedded Networked Sensor Systems (SenSys 2017), 2017.
- Crawl, D., Block, J., Artes, T., Cowart, C., de Callafon, R., DeFanti, T., Graham, J., Smarr, L., Srivas, T., Altintas, I., FireMap: A Web Tool for Dynamic Data-Driven Predictive Wildfire Modeling Powered by the WIFIRE Cyberinfrastructure, AGU Fall Meeting, 2016.
- Crawl, D., Block, J., Graham, J., Cowart, C., Gupta A., Nguyen, M., de Callafon, R., Smarr, L., Altintas, I., geoKepler Workflow Module for Computationally Scalable and Reproducible Geoprocessing and Modeling, AGU Fall Meeting, 2015. [PDF]
- Sale, J., Block, J., Crawl, D., Cowart, C., Altintas, I., Creation of a Geo Big Data Outreach and Training Collaboratory for Wildfire Community, AGU Fall Meeting 2015.
- Altintas, I., Crawl, D., Cowart, C., Gupta, A., Block, J., de Callafon, R., Braun, H.W., Gollner, M., Smarr, L., Trouve, A., Sale, J., WIFIRE Data Model and Catalog for Wildfire Data and Tools, AGU Fall Meeting, 2014. [PDF]