Exploring Various Applicable Techniques to Detect Smoke on the Satellite Images

Huang, Chau-Lin Charly; Munasinghe, Thilanka

doi:10.1109/bigdata50022.2020.9378466

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…developing an efficient and accurate wildfire modeling framework [4], [5]. Several studies have been conducted using various physics-based parameters and environmental factors to model the growth and spread of fire during a wildfire event.…”

Section: Manuscript Id Numbermentioning

confidence: 99%

A Geospatial Approach to Wild fire Risk Modeling using Machine Learning and Remote Sensing Data

Gupta,

Kim

2023

Preprint

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<p>In recent years, the likelihood of wildfire occurrence has increased in many North American communities as changes in climate have led to longer, more deadly fire seasons. Many Americans, especially those living in Western states, have reported frequent drought and wildfire conditions, leading to an increased need for a modeling program to assess wildfire risk at a low computational cost. The research objective of this paper was to develop a machine learning model capable of producing real-time wildfire risk assessments using five geospatial datasets: Land Fire Mean Return, Annual Precipitation, Sentinel-2 Imagery, Land Cover, and Moisture Deficit & Surplus. To create the model, three separate machine learning architectures were implemented (U-Net, DeepLabV3, and the Pyramid Scene Parsing Network) and then applied to the study area of San Bernardino County, CA for the year 2020. In addition, this study demonstrated a proof of concept for further inquiry into combining artificial intelligence and geospatial datasets to create useful insights. </p>

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Section: Manuscript Id Numbermentioning

confidence: 99%

A Geospatial Approach to Wild fire Risk Modeling using Machine Learning and Remote Sensing Data

Gupta,

Kim

2023

Preprint

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“…A vehicle is designed to float on the [13]. Various technologies use one you look family (YOLO) algorithms based on machine and deep learning to detect smoke early in forests satellite imagery [14]. Object recognition, speech processing, and image classification technologies have been implemented using the Jetson Nano Board to detect obstacles for blind people [15].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation monitoring robotic system based on you only look once model using deep learning technique

Al-Tameemi

Hasan²,

Oleiwi³

2023

IJ-AI

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<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.</span>

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A Weakly-Supervised Change Detection for Multispectral Earth Observation Imagery using a Long Short- Term Memory Classifier with a Virtual Training Data Neural Generator

Girla

Neagoe

2022

2022 14th International Conference on Communications (COMM)

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Exploring Various Applicable Techniques to Detect Smoke on the Satellite Images

Cited by 8 publications

References 17 publications

A Geospatial Approach to Wild fire Risk Modeling using Machine Learning and Remote Sensing Data

A Geospatial Approach to Wild fire Risk Modeling using Machine Learning and Remote Sensing Data

Design and implementation monitoring robotic system based on you only look once model using deep learning technique

A Weakly-Supervised Change Detection for Multispectral Earth Observation Imagery using a Long Short- Term Memory Classifier with a Virtual Training Data Neural Generator

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