Wildfire segmentation analysis from edge computing for on-board real-time alerts using hyperspectral imagery

Spiller, Dario; Thangavel, Kathiravan; Sasidharan, Sarathchandrakumar T.; Amici, S.; Ansalone, Luigi; Sabatini, Roberto

doi:10.1109/metroxraine54828.2022.9967553

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…The 1D-CNN architecture was described and exploited in previous works [30,35,36,39,[45][46][47]. Its input consists of pixel spectral signatures, represented as an array with a length of 230, defined by SWIR and VNIR PRISMA channels.…”

Section: One-dimensional Convolutional Neural Networkmentioning

confidence: 99%

“…The quality of the information that can be extracted from PRISMA HS imagery was investigated in [29], where analytical methodologies were proposed to locate wildfires and estimate the temperature of active fire pixels. At the same time, we showed the possibility of implementing Trusted Autonomous Satellite Operations [30][31][32] by utilizing artificial intelligence [33,34] on-board satellites with astrionics for data processing [35][36][37]. For the purpose of providing real-time or near real-time disaster management, the same has been used in Distributed Satellite Systems [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

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Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Spiller,

Carbone,

Amici

et al. 2023

Remote Sensing

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The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model’s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.

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Section: One-dimensional Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Spiller,

Carbone,

Amici

et al. 2023

Remote Sensing

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“…Section 4 delves deeply into the results and findings, as well as their applicability, and is followed by a conclusion in Section 5 . This is an extended version of an article [ 27 ] that was presented at the 2022 IEEE MetroXRAINE Conference.…”

Section: Introductionmentioning

confidence: 99%

Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors

Thangavel

Spiller

Sabatini

et al. 2023

Sensors

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Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.

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“…Terrestrial, airborne, and satellite systems are the three basic types of widely used technologies that can detect or monitor active wildfire or smoke conditions in real or near real time. These technologies are commonly equipped with optical and thermal sensors; once the data are acquired, it can be processed by suitable artificial intelligence (AI) algorithms, which are typically based on a machine learning approach [1], [4], [5], [6]. In order to recognize forest fires in their early phases and model how smoke and fires behave, these strategies rely on either hand-crafted features or sophisticated AI technologies [7].…”

mentioning

confidence: 99%

“…In order to recognize forest fires in their early phases and model how smoke and fires behave, these strategies rely on either hand-crafted features or sophisticated AI technologies [7]. This research concentrates on space-based detection of fire by using suitable AI algorithms for on-board wildfire detection and analysis [4], [5]. The purpose of this research is to determine whether the Distributed Satellite System (DSS) and the on-board computing resources can be employed to monitor disaster scenarios, such as wildfire monitoring, using hyperspectral satellite imagery.…”

mentioning

confidence: 99%

Near Real-Time Wildfire Management Using Distributed Satellite System

Thangavel

Spiller

Sabatini

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Climate action (SDG-13) is an integral part of the Sustainable Development Goals (SDGs) set by the United Nations (UN), and wildfire is one of the catastrophic events related to climate change. Large-scale forest fires have drastically increased in frequency and size in recent years in Australia and other nations. These wildfires endanger the forests and urban areas of the world, demolish vast amounts of property, and frequently result in fatalities. There is a requirement for real-time/near realtime catastrophic event monitoring of fires due to their growing frequency. In order to effectively monitor disaster events, it will be feasible to manage them in real time or near real time due to the advent of the Distributed Satellite System (DSS). This research examines the possible applicability of DSS for wildfire surveillance. For spacecraft to continually monitor the dynamically changing environment, satellite missions must have broad coverage and revisit intervals that DSS can fulfill. A feasibility analysis, as well as a model and scenario prototype for a satellite artificial intelligence (AI) system, is included in this letter to enable prompt action and swiftly provide alerts. In our previous research, it is shown that on-board implementation, i.e., data processing utilizing hardware accelerators, is feasible. To enable Trusted Autonomous Satellite Operation (TASO), the same will be included in the proposed DSS architecture, and the outcomes will be provided. To demonstrate the applicability, the suggested DSS architecture will be tested in several geographic locations to demonstrate the system-wide coverage.

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Wildfire segmentation analysis from edge computing for on-board real-time alerts using hyperspectral imagery

Cited by 21 publications

References 23 publications

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors

Near Real-Time Wildfire Management Using Distributed Satellite System

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