Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images

Monaco, Simone; Pasini, Andrea; Apiletti, Daniele; Colomba, Luca; Garza, Paolo; Baralis, Elena

doi:10.1109/bigdata50022.2020.9377867

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…We chose to use the IOU because it is a metric specifically dedicated to highlight the accuracy in predicting the number of susceptible pixels and their location in a raster image (Monaco et al, 2020). Furthermore, to visualize how the model performs at different pribability thresholds and what is the performance capacity of the model we also evaluated the Receiver Operating Characteristic (ROC) (Fawcett, 2006) curve which is generated from the True Positive Rate and False Positive Rate.…”

Section: Susceptibility Componentmentioning

confidence: 99%

Space-time landslide hazard modeling via Ensemble Neural Networks

Dahal¹,

Tanyaş²,

Westen³

et al. 2022

Preprint

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For decades, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physics-based models. The part of the geomorphology community focusing on data-driven model has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimated when landslides may occur via models that belong to the early-warning-system or to the rainfall-threshold themes. In this context, few published research have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size, has hardly ever been modeled over space and time. However, the technological advancements of data-driven models have reached a level of maturity that allows to model all three components (Where, When and Size) mentioned above. This work, takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this ambitious task, we have used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake on the 25th of April 2015. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 x 1 km and classified/regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6-month resolution depending on the mapped inventory dates. The results are promising as our model performs satisfactorily both in the classification (susceptibility) and regression (density prediction) tasks. We believe that the model we propose brings a level of novelty that has the potential to create a rift with respect to the common susceptibility literature, finally proposing an integrated framework for hazard modeling in a data-driven context.

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Section: Susceptibility Componentmentioning

confidence: 99%

Space-time landslide hazard modeling via Ensemble Neural Networks

Dahal¹,

Tanyaş²,

Westen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We chose to use the IOU because it is a metric specifically dedicated to highlighting the accuracy in predicting the number of susceptible pixels and their location in a raster image (Monaco et al, 2020). Furthermore, to visualize how the model performs at different probability thresholds and what the performance capacity of the model is, we also evaluated the Receiver Operating Characteristic (ROC, Fawcett, 2006) curve.…”

Section: Susceptibility Componentmentioning

confidence: 99%

Space–time landslide hazard modeling via Ensemble Neural Networks

Dahal,

Tanyas,

van Westen

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. Until now, a full numerical description of the spatio-temporal dynamics of a landslide could be achieved only via physically based models. The part of the geoscientific community in developing data-driven models has instead focused on predicting where landslides may occur via susceptibility models. Moreover, they have estimate when landslides may occur via models that belong to the early-warning system or to the rainfall-threshold classes. In this context, few published research works have explored a joint spatio-temporal model structure. Furthermore, the third element completing the hazard definition, i.e., the landslide size (i.e., areas or volumes), has hardly ever been modeled over space and time. However, technological advancements in data-driven models have reached a level of maturity that allows all three components to be modeled (Location, Frequency, and Size). This work takes this direction and proposes for the first time a solution to the assessment of landslide hazard in a given area by jointly modeling landslide occurrences and their associated areal density per mapping unit, in space and time. To achieve this, we used a spatio-temporal landslide database generated for the Nepalese region affected by the Gorkha earthquake. The model relies on a deep-learning architecture trained using an Ensemble Neural Network, where the landslide occurrences and densities are aggregated over a squared mapping unit of 1 km × 1 km and classified or regressed against a nested 30 m lattice. At the nested level, we have expressed predisposing and triggering factors. As for the temporal units, we have used an approximately 6 month resolution. The results are promising as our model performs satisfactorily both in the susceptibility (AUC = 0.93) and density prediction (Pearson r = 0.93) tasks over the entire spatio-temporal domain. This model takes a significant distance from the common landslide susceptibility modeling literature, proposing an integrated framework for hazard modeling in a data-driven context.

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“…Among many other semantic segmentation architectures which have been proposed in literature [47][48][49], the works in [50,51] demonstrated the U-Net architecture to be a valuable choice for the wildfire damage severity estimation problem. The state-of-the-art solution proposes a Double-Step U-Net architecture, addressing the wildfire delineation and the severity prediction tasks separately.…”

Section: Related Workmentioning

confidence: 99%

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

et al. 2021

Self Cite

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Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.

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Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images

Cited by 9 publications

References 10 publications

Space-time landslide hazard modeling via Ensemble Neural Networks

Space-time landslide hazard modeling via Ensemble Neural Networks

Space–time landslide hazard modeling via Ensemble Neural Networks

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

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