{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:33:20Z","timestamp":1778081600574,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["COL0-FACT-2019"],"award-info":[{"award-number":["COL0-FACT-2019"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DARPA","award":["HR00112290074"],"award-info":[{"award-number":["HR00112290074"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1931363; CNS-2312319"],"award-info":[{"award-number":["OAC-1931363; CNS-2312319"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,9]]},"DOI":"10.1145\/3577190.3614116","type":"proceedings-article","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T22:30:48Z","timestamp":1696717848000},"page":"470-480","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Paying Attention to Wildfire: Using U-Net with Attention Blocks on Multimodal Data for Next Day Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5604-7920","authenticated-orcid":false,"given":"Jack","family":"Fitzgerald","sequence":"first","affiliation":[{"name":"Computer Science, Colorado State University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2268-9571","authenticated-orcid":false,"given":"Ethan","family":"Seefried","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado State University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6499-5645","authenticated-orcid":false,"given":"James E","family":"Yost","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado State University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7012-5528","authenticated-orcid":false,"given":"Sangmi","family":"Pallickara","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado State University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2653-0873","authenticated-orcid":false,"given":"Nathaniel","family":"Blanchard","sequence":"additional","affiliation":[{"name":"Computer Science, Colorado State University, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/joc.3413"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1175\/JCLI-D-13-00218.1"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/QRS-C57518.2022.00095"},{"key":"e_1_3_2_2_4_1","volume-title":"Emulation of wildland fire spread simulation using deep learning. Neural networks 141","author":"Allaire Fr\u00e9d\u00e9ric","year":"2021","unstructured":"Fr\u00e9d\u00e9ric Allaire , Vivien Mallet , and Jean-Baptiste Filippi . 2021. Emulation of wildland fire spread simulation using deep learning. Neural networks 141 ( 2021 ), 184\u2013198. https:\/\/doi.org\/10.1016\/j.neunet.2021.04.006 10.1016\/j.neunet.2021.04.006 Fr\u00e9d\u00e9ric Allaire, Vivien Mallet, and Jean-Baptiste Filippi. 2021. Emulation of wildland fire spread simulation using deep learning. Neural networks 141 (2021), 184\u2013198. https:\/\/doi.org\/10.1016\/j.neunet.2021.04.006"},{"key":"e_1_3_2_2_5_1","volume-title":"Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955","author":"Alom Md\u00a0Zahangir","year":"2018","unstructured":"Md\u00a0Zahangir Alom , Mahmudul Hasan , Chris Yakopcic , Tarek\u00a0 M Taha , and Vijayan\u00a0 K Asari . 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955 ( 2018 ). https:\/\/doi.org\/10.48550\/arXiv.1802.06955 10.48550\/arXiv.1802.06955 Md\u00a0Zahangir Alom, Mahmudul Hasan, Chris Yakopcic, Tarek\u00a0M Taha, and Vijayan\u00a0K Asari. 2018. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955 (2018). https:\/\/doi.org\/10.48550\/arXiv.1802.06955"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2011048118"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1071\/WF9980001"},{"key":"e_1_3_2_2_9_1","volume-title":"TN, USA","author":"Didan A","year":"2018","unstructured":"A Didan and A Barreto . 2018. VIIRS\/ NPP Vegetation Indices 16-Day L3 Global 500m SIN Grid V001. NASA EOSDIS Land Processes DAAC: Oak Ridge , TN, USA ( 2018 ). https:\/\/doi.org\/10.5067\/VIIRS\/VNP13A1.001 10.5067\/VIIRS A Didan and A Barreto. 2018. VIIRS\/NPP Vegetation Indices 16-Day L3 Global 500m SIN Grid V001. NASA EOSDIS Land Processes DAAC: Oak Ridge, TN, USA (2018). https:\/\/doi.org\/10.5067\/VIIRS\/VNP13A1.001"},{"key":"e_1_3_2_2_10_1","volume-title":"The shuttle radar topography mission. Reviews of geophysics 45, 2","author":"Farr G","year":"2007","unstructured":"Tom\u00a0 G Farr , Paul\u00a0 A Rosen , Edward Caro , Robert Crippen , Riley Duren , Scott Hensley , Michael Kobrick , Mimi Paller , Ernesto Rodriguez , Ladislav Roth , 2007. The shuttle radar topography mission. Reviews of geophysics 45, 2 ( 2007 ). https:\/\/doi.org\/10.1029\/2005RG000183 10.1029\/2005RG000183 Tom\u00a0G Farr, Paul\u00a0A Rosen, Edward Caro, Robert Crippen, Riley Duren, Scott Hensley, Michael Kobrick, Mimi Paller, Ernesto Rodriguez, Ladislav Roth, 2007. The shuttle radar topography mission. Reviews of geophysics 45, 2 (2007). https:\/\/doi.org\/10.1029\/2005RG000183"},{"key":"e_1_3_2_2_12_1","volume-title":"Version 4 (GPWv4): Population Density, Revision 11.","author":"Center for International Earth Science Information Network CIESIN Columbia\u00a0University. 2016.","year":"2016","unstructured":"Center for International Earth Science Information Network CIESIN Columbia\u00a0University. 2016. Gridded Population of the World , Version 4 (GPWv4): Population Density, Revision 11. ( 2016 ). https:\/\/doi.org\/10.7927\/H4NP22DQ 10.7927\/H4NP22DQ Center for International Earth Science Information Network CIESIN Columbia\u00a0University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. (2016). https:\/\/doi.org\/10.7927\/H4NP22DQ"},{"key":"e_1_3_2_2_13_1","volume-title":"NASA EOSDIS Land Processes DAAC 10","author":"Giglio Louis","year":"2015","unstructured":"Louis Giglio and Christopher Justice . 2015. MOD14A1 MODIS\/ Terra thermal anomalies\/fire daily L3 global 1km SIN grid V006. NASA EOSDIS Land Processes DAAC 10 ( 2015 ). Louis Giglio and Christopher Justice. 2015. MOD14A1 MODIS\/Terra thermal anomalies\/fire daily L3 global 1km SIN grid V006. NASA EOSDIS Land Processes DAAC 10 (2015)."},{"key":"e_1_3_2_2_14_1","volume-title":"Wildland fire spread modeling using convolutional neural networks. Fire technology 55","author":"Hodges L","year":"2019","unstructured":"Jonathan\u00a0 L Hodges and Brian\u00a0 Y Lattimer . 2019. Wildland fire spread modeling using convolutional neural networks. Fire technology 55 ( 2019 ), 2115\u20132142. https:\/\/doi.org\/10.1007\/s10694-019-00846-4 10.1007\/s10694-019-00846-4 Jonathan\u00a0L Hodges and Brian\u00a0Y Lattimer. 2019. Wildland fire spread modeling using convolutional neural networks. Fire technology 55 (2019), 2115\u20132142. https:\/\/doi.org\/10.1007\/s10694-019-00846-4"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3192974"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/iot4010001"},{"key":"#cr-split#-e_1_3_2_2_17_1.1","unstructured":"Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. (2017) 2980-2988. https:\/\/doi.org\/10.48550\/arXiv.1708.02002 10.48550\/arXiv.1708.02002"},{"key":"#cr-split#-e_1_3_2_2_17_1.2","unstructured":"Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal loss for dense object detection. (2017) 2980-2988. https:\/\/doi.org\/10.48550\/arXiv.1708.02002"},{"key":"e_1_3_2_2_18_1","volume-title":"Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999","author":"Oktay Ozan","year":"2018","unstructured":"Ozan Oktay , Jo Schlemper , Loic\u00a0Le Folgoc , Matthew Lee , Mattias Heinrich , Kazunari Misawa , Kensaku Mori , Steven McDonagh , Nils\u00a0 Y Hammerla , Bernhard Kainz , 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 ( 2018 ). https:\/\/doi.org\/10.48550\/arXiv.1804.03999 10.48550\/arXiv.1804.03999 Ozan Oktay, Jo Schlemper, Loic\u00a0Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils\u00a0Y Hammerla, Bernhard Kainz, 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.03999"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/math10030300"},{"key":"#cr-split#-e_1_3_2_2_20_1.1","doi-asserted-by":"crossref","unstructured":"David Radke Anna Hessler and Dan Ellsworth. 2019. FireCast: Leveraging Deep Learning to Predict Wildfire Spread.. In IJCAI. 4575-4581. https:\/\/doi.org\/10.24963\/ijcai.2019\/636 10.24963\/ijcai.2019","DOI":"10.24963\/ijcai.2019\/636"},{"key":"#cr-split#-e_1_3_2_2_20_1.2","doi-asserted-by":"crossref","unstructured":"David Radke Anna Hessler and Dan Ellsworth. 2019. FireCast: Leveraging Deep Learning to Predict Wildfire Spread.. In IJCAI. 4575-4581. https:\/\/doi.org\/10.24963\/ijcai.2019\/636","DOI":"10.24963\/ijcai.2019\/636"},{"key":"e_1_3_2_2_21_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger , Philipp Fischer , and Thomas Brox . 2015 . U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference , Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer , 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 10.1007\/978-3-319-24574-4_28 Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_2_22_1","volume-title":"Predictive modeling of wildfires: A new dataset and machine learning approach. Fire safety journal 104","author":"Sayad Younes\u00a0Oulad","year":"2019","unstructured":"Younes\u00a0Oulad Sayad , Hajar Mousannif , and Hassan Al\u00a0Moatassime . 2019. Predictive modeling of wildfires: A new dataset and machine learning approach. Fire safety journal 104 ( 2019 ), 130\u2013146. https:\/\/doi.org\/10.1016\/j.firesaf.2019.01.006 10.1016\/j.firesaf.2019.01.006 Younes\u00a0Oulad Sayad, Hajar Mousannif, and Hassan Al\u00a0Moatassime. 2019. Predictive modeling of wildfires: A new dataset and machine learning approach. Fire safety journal 104 (2019), 130\u2013146. https:\/\/doi.org\/10.1016\/j.firesaf.2019.01.006"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2013.12.008"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2023.3249643"},{"key":"e_1_3_2_2_25_1","volume-title":"IEEE","author":"Yang Tien-Ju","year":"2017","unstructured":"Tien-Ju Yang , Yu-Hsin Chen , Joel Emer , and Vivienne Sze . 2017 . A method to estimate the energy consumption of deep neural networks. In 2017 51st asilomar conference on signals, systems, and computers . IEEE , 1916\u20131920. https:\/\/doi.org\/10.1109\/ACSSC.2017.8335698 10.1109\/ACSSC.2017.8335698 Tien-Ju Yang, Yu-Hsin Chen, Joel Emer, and Vivienne Sze. 2017. A method to estimate the energy consumption of deep neural networks. In 2017 51st asilomar conference on signals, systems, and computers. IEEE, 1916\u20131920. https:\/\/doi.org\/10.1109\/ACSSC.2017.8335698"}],"event":{"name":"ICMI '23: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","location":"Paris France","acronym":"ICMI '23","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3577190.3614116","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3577190.3614116","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:11Z","timestamp":1750182671000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3577190.3614116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":25,"alternative-id":["10.1145\/3577190.3614116","10.1145\/3577190"],"URL":"https:\/\/doi.org\/10.1145\/3577190.3614116","relation":{},"subject":[],"published":{"date-parts":[[2023,10,9]]},"assertion":[{"value":"2023-10-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}