Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Google（谷歌）；Stanford（斯坦福大学）
Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-Fan Chen, John Anderson
备注：
Presented at 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Artificial Intelligence for Humani- tarian Assistance and Disaster Response Workshop, Vancouver, Canada
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 topography with an AUC of 83%.
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 demonst…
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）
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…
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）
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）
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Harokopio University of Athens（雅典哈罗科皮奥大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Stanford University School of Medicine（斯坦福大学医学院）；The Harker School（哈克学校）
Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data fro…
AI-driven Structure Detection and Information Extraction from Historical Cadastral Maps (Early 19th Century Franciscean Cadastre in the Province of Styria) and Current High-resolution Satellite and Aerial Imagery for Remote Sensing
University of Graz（格拉茨大学）；Know Center, Graz（格拉茨知识中心）
National Observatory of Athens（雅典国家天文台）；Universitat de València（瓦伦西亚大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade to predict next-day's fire danger. To that end, we collect, pre-process and harmonize an open-access datacube, featuring a set of covariates that jointly affect the fire occurrence and spread, such as weather conditions, satellite-derived products, topography features and variables related to human activity. We implement a variety of Deep Learning (DL) model…
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…
Modeling forests using historical data allows for more accurately evolution analysis, thus providing an important basis for other studies. As a recognized and effective tool, remote sensing plays an important role in forestry analysis. We can use it to derive information about the forest, including tree type, coverage and canopy density. There are many forest time series modeling studies using statistic values, but few using remote sensing images. Image prediction digital twin is an implementation of digital twin, which aims to predict future images bases on historical data. In this paper, we …
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…
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）
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）
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…
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…
School of Information, University of California, Berkeley（加州大学伯克利分校信息学院）；IBM Chief Analytics Office（IBM首席分析办公室）
Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, bu…
VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航天信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；School of Information Science and Technology, Beijing Forestry University（北京林业大学信息科学与技术学院）
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）
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Minnesota（明尼苏达大学）；Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of Southern California（南加州大学）
Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integra…
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences (CIRES)（环境科学合作研究所（CIRES））；Institute of Behavioral Science（行为科学研究所）
Despite abundant data on the spatial distribution of contemporary human settlements, historical data on the long-term evolution of human settlements at fine spatial and temporal granularity is scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used mapping methods (e.g., image classification) and suitable data sources (i.e., aerial imagery, multi-spectral remote sensing data, LiDAR) have only been available in recent decades. However, there are alternative data sources such as cadastral records that are digitally available, containi…
History-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images
School of Computer Science and Engineering, Macau University of Science and Technology（澳门科学技术大学计算机科学与工程学院）；State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology（澳门科学技术大学月球与行星科学国家重点实验室）；State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院遥感科学国家重点实验室，航天信息研究所）
Applications of Knowledge Distillation in Remote Sensing: A Survey
College of Engineering and Information Technology, University of Dubai（迪拜大学工程与信息技术学院）；University of Dubai（迪拜大学）；United Arab Emirates University（阿联酋大学）；Harokopio University of Athens（雅典哈科皮奥大学）
Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management
IRISA, Université Bretagne Sud, UMR 6074, Vannes, France（IRISA，布列塔尼大学，UMR 6074，瓦纳兹，法国）；University of Salzburg, Department of Geoinformatics, Salzburg, Austria（萨尔茨堡大学，地理信息学院，萨尔茨堡，奥地利）；ICube-SERTIT, Université de Strasbourg, Illkirch Graffenstaden, France（ICube-SERTIT，斯特拉斯堡大学，伊尔克里希格拉芬施塔登，法国）
Remote sensing data imputation using deep learning for multispectral imagery
Water Research Centre, University of New South Wales（新南威尔士大学水研究中心）；ARC ITTC Data Analytics for Resources and Environments, University of New South Wales（新南威尔士大学资源与环境数据分析师联盟）；Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales（新南威尔士大学数学与统计学过渡人工智能研究组）
