Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, our data set combines 2D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning. To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.
With the increased size and frequency of wildfire eventsworldwide, accurate real-time prediction of evolving wildfirefronts is a crucial component of firefighting efforts and for-est management practices. We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in24 hour periods. The fire spreading simulation is based ona deep convolutional neural network (CNN) that is trainedon remotely sensed atmospheric and environmental time se-ries data. We show that the model is able to learn wildfirespreading dynamics from real historic data sets from a seriesof wildfir…
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topo…
Deep Learning for Wildfire Risk Prediction: Integrating Remote Sensing and Environmental Data
Schulich School of Engineering, Department of Geomatics Engineering, University of Calgary（Schulich 工程学院，测绘工程系，卡尔加里大学）；Department of Geography and Environmental Management, University of Waterloo（地理与环境管理系，滑铁卢大学）；Department of Systems Design Engineering, University of Waterloo（系统设计工程系，滑铁卢大学）；School of Emergency Management, Nanjing University of Information Science and Technology（应急管理学院，南京信息工程大学）；Division of Geoinformatics, KTH Royal Institute of Technology（地理信息学系，皇家理工学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（遥感与测绘工程学院，南京信息工程大学）；Department of Software Engineering, University of Waterloo（软件工程系，滑铁卢大学）；Department of Management Science and Engineering, University of Waterloo（管理科学与工程系，滑铁卢大学）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；University of Waterloo（滑铁卢大学）；Institut Polytechnique de Paris（巴黎综合理工学院）；EDF R&D（法国电力公司研发部门）；CEREA；ENPC（巴黎路桥学院）
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Imperial College London（帝国理工学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；École des Ponts（巴黎路桥学院）；EDF R&D（法国电力研发中心）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Reading（雷丁大学）
Wildfires are uncontrolled fires in the environment that can be caused by humans or nature. In 2020 alone, wildfires in California have burned 4.2 million acres, damaged 10,500 buildings or structures, and killed more than 31 people, exacerbated by climate change and a rise in average global temperatures. This also means there has been an increase in the costs of extinguishing these treacherous wildfires. The objective of the research is to detect forest fires in their earlier stages to prevent them from spreading, prevent them from causing damage to a variety of things, and most importantly, …
Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the Machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked Image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classi…
Université de Moncton（蒙克顿大学）；Universidad Técnica Federico Santa María（费德里科圣玛丽亚技术大学）
Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground…
RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
School of Earth Sciences, Zhejiang University（浙江大学地球科学学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Provincial Key Laboratory of Geographic Information Science（浙江省地理信息科学重点实验室）；Key Laboratory of Spatio-temporal Information and Intelligent Services (LSIIS), Ministry of Natural Resources of the People’s Republic of China（国家自然资源部空间时空信息与智能服务重点实验室）
Universitat de València（瓦伦西亚大学）；Descartes Lab（笛卡尔实验室）
Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured (EC) distribution and extend them to their nonlinear counterparts based on the theory of reproducing kernels' Hilbert space. We illustrate the performance of the kernel methods introduced in both perv…
Raster Forge: Interactive Raster Manipulation Library and GUI for Python
COPELABS, Lusófona University（卢索福纳大学COPELABS实验室）；Center of Technology and Systems (UNINOVA-CTS)（技术与系统中心（UNINOVA-CTS））；Associated Lab of Intelligent Systems (LASI)（智能系统联合实验室）
Development and Application of Self-Supervised Machine Learning for Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets
Spatial Informatics Group, LLC.（空间信息集团有限公司）；Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；University of California at Berkeley（加州大学伯克利分校）；Fowler School of Engineering, Chapman University（查普曼大学福勒工程学院）；Machine Learning and Assistive Technology Lab (MLAT), Chapman University（机器学习与辅助技术实验室（MLAT），查普曼大学）
Google Research（谷歌研究院）；Stanford University（斯坦福大学）；United Nations World Food Programme（联合国世界粮食计划署）
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from satellite imagery, reducing manual labor and turn-around time. A major impediment to using machine learning methods in real disaster response scenarios is the difficulty of obtaining a sufficient amount of labeled data to train a model for a…
Multi-modal Uncertainty Robust Tree Cover Segmentation For High-Resolution Remote Sensing Images
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德雷塞尔大学）；Swiss Federal Institute for Forest, Snow, and Landscape Research WSL（瑞士森林、雪和景观研究联邦 institute WSL）
