Multimodal Wildland Fire Smoke Detection

• URL: http://arxiv.org/abs/2212.14143v1
• Date: Thu, 29 Dec 2022 01:16:06 GMT
• Title: Multimodal Wildland Fire Smoke Detection
• Authors: Siddhant Baldota, Shreyas Anantha Ramaprasad, Jaspreet Kaur Bhamra, Shane Luna, Ravi Ramachandra, Eugene Zen, Harrison Kim, Daniel Crawl, Ismael Perez, Ilkay Altintas, Garrison W. Cottrell, Mai H.Nguyen
• Abstract summary: Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the U.S. We present our work on integrating multiple data sources in SmokeyNet, a deep learning model usingtemporal information to detect smoke from wildland fires. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.
• Score: 5.15911752972989
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. We present our results comparing performance in terms of both accuracy and time-to-detection for multimodal data vs. a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Related papers

• A Synergistic Approach to Wildfire Prevention and Management Using AI, ML, and 5G Technology in the United States [44.99833362998488]
  This research investigates proactive methods for detecting and handling wildfires in the United States. The specific objective of this research covers proactive detection and prevention of wildfires using advanced technology. Various methods, such as AI-enabled remote sensing and 5G-based active monitoring, can enhance proactive wildfire detection and management.
  arXiv  Detail & Related papers  (2024-02-27T04:09:30Z)
• A Multimodal Supervised Machine Learning Approach for Satellite-based Wildfire Identification in Europe [0.34410212782758043]
  We propose a wildfire identification solution to improve the accuracy of automated satellite-based hotspot detection systems. We cross-reference the thermal anomalies detected by the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) hotspot services. Then, we propose a novel supervised machine learning approach to disambiguate hotspot detections, distinguishing between wildfires and other events.
  arXiv  Detail & Related papers  (2023-07-27T08:28:57Z)
• FORFIS: A forest fire firefighting simulation tool for education and research [90.40304110009733]
  We present a forest fire firefighting simulation tool named FORFIS that is implemented in Python. Our tool is published underv3 license and comes with a GUI as well as additional output functionality.
  arXiv  Detail & Related papers  (2023-05-29T09:14:38Z)
• Image-Based Fire Detection in Industrial Environments with YOLOv4 [53.180678723280145]
  This work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector.
  arXiv  Detail & Related papers  (2022-12-09T11:32:36Z)
• Image-based Early Detection System for Wildfires [2.8494271563126676]
  Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. We present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily.
  arXiv  Detail & Related papers  (2022-11-03T07:38:30Z)
• FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection [0.0]
  Fire Ignition Library (FIgLib) is a publicly-available dataset of nearly 25,000 labeled wildfire smoke images. SmokeyNet is a novel deep learning architecture usingtemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance.
  arXiv  Detail & Related papers  (2021-12-16T03:49:58Z)
• Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks [0.0]
  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. The objective of the research is to detect forest fires in their earlier stages to prevent them from spreading.
  arXiv  Detail & Related papers  (2021-09-10T19:30:37Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)
• STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection [52.648906951532155]
  We propose a novel Spatio-Temporal Cross Network (STCNet) to recognize industrial smoke emissions. The proposed STCNet involves a spatial to extract texture features and a temporal pathway to capture smoke motion information. We show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%.
  arXiv  Detail & Related papers  (2020-11-10T02:28:47Z)
• Dynamic Community Detection into Analyzing of Wildfires Events [55.72431452586636]
  We investigate the information that dynamic community structures reveal about the dynamics of wildfires. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.
  arXiv  Detail & Related papers  (2020-11-02T17:31:47Z)
• Uncertainty Aware Wildfire Management [6.997483623023005]
  Recent wildfires in the United States have resulted in loss of life and billions of dollars. There are limited resources to be deployed over a massive area and the spread of the fire is challenging to predict. This paper proposes a decision-theoretic approach to combat wildfires.
  arXiv  Detail & Related papers  (2020-10-15T17:47:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.