A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery

Colomba, Luca; Farasin, Alessandro; Monaco, Simone; Greco, Salvatore; Garza, Paolo; Apiletti, Daniele; Baralis, Elena; Cerquitelli, Tania

doi:10.1145/3511808.3557528

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…Using a data augmentation method (rotation), U-Net obtained a Jaccard index of 93%. U-Net was also evaluated in Colomba et al [98] to map burned areas. It was trained and evaluated with 73 images downloaded from the satellite burned area dataset [98,99] and data augmentation techniques (rotation, shear, and vertical/horizontal flip), obtaining an accuracy of 94.3%.…”

Section: Deep Learning-based Approaches For Fire Mapping Using Satell...mentioning

confidence: 99%

“…U-Net was also evaluated in Colomba et al [98] to map burned areas. It was trained and evaluated with 73 images downloaded from the satellite burned area dataset [98,99] and data augmentation techniques (rotation, shear, and vertical/horizontal flip), obtaining an accuracy of 94.3%. Zhang et al [100] performed deep residual U-Net, which adopts the ResNet model as a feature extractor to map wildfires using the Sentinel-2 MSI time series and Sentinel-1 SAR data.…”

Section: Deep Learning-based Approaches For Fire Mapping Using Satell...mentioning

confidence: 99%

“…Khryashchev et al [68] used multiple techniques such as rotation, mirroring, shifting, and random chromatic distortion in HSV color format to augment the number of input images, resulting in eight times more images than the original training and testing data (1850 images). Colomba et al [98] used four data augmentation methods during the training phase, including rotation, shear, and vertical/horizontal flips, to improve the robustness of their proposed model for fire severity forecasting. In [149], three data augmentation techniques (rotation, vertical/horizontal flips, and shear) were used to change the data variability, especially for unbalanced classes.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Fire mapping [90] Resize, mirror, rotation, aspect, crop, and color jitter [93] Rotation, mirror, and horizontal/vertical flip [97] Rotation [98] Rotation, shear, and vertical/horizontal flip Fire damage prediction [144] Horizontal/vertical flip, rotation, and shear [150] Vertical/horizontal flip…”

Section: Data Augmentation Techniquesmentioning

confidence: 99%

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Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Ghali

Akhloufi

2023

Fire

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Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.

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Section: Deep Learning-based Approaches For Fire Mapping Using Satell...mentioning

confidence: 99%

Section: Deep Learning-based Approaches For Fire Mapping Using Satell...mentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Augmentation Techniquesmentioning

confidence: 99%

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Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Ghali

Akhloufi

2023

Fire

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“…In the EO domain, different public datasets are available to the research community tackling different problems, such as flood delineation [12], crop classification and segmentation [23] but, to the best of our knowledge, only two public datasets are available for the burned area delineation problem covering some countries in Europe [24] and Indonesia [25]. The dataset proposed in this paper collects both pre-and post-fire Sentinel-2 L2A data from California forest fires, limiting seasonal and phenological differences between the two acquisitions as explained in the following paragraphs.…”

Section: Related Workmentioning

confidence: 99%

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

Cambrin,

Colomba,

Garza

2023

IEEE Geosci. Remote Sens. Mag.

Self Cite

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Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools. This paper introduces a novel open dataset that tackles the burned area delineation problem, a binary segmentation problem applied to satellite imagery. The presented resource consists of pre-and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015. Raster annotations were generated from the data released by California's Department of Forestry and Fire Protection. Moreover, in conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models.

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FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images

Khankeshizadeh,

Tahermanesh,

Mohsenifar

et al. 2024

Ecological Indicators

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A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery

Cited by 9 publications

References 32 publications

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

FBA-DPAttResU-Net: Forest burned area detection using a novel end-to-end dual-path attention residual-based U-Net from post-fire Sentinel-1 and Sentinel-2 images

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