Related papers: Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery

Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery

URL: http://arxiv.org/abs/2311.17368v1
Date: Wed, 29 Nov 2023 05:42:25 GMT
Title: Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery
Authors: Ian Mancilla-Wulff, Jaime Carrasco, Cristobal Pais, Alejandro Miranda, Andres Weintraub
Abstract summary: This study explores two proposed approaches based on the U-Net model for automating and optimizing the burned-area mapping process. Tests based on 195 representative images of the study area show that increasing dataset balance using the AS model yields better performance.
Score: 39.91303506884272
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Monitoring wildfires is an essential step in minimizing their impact on the planet, understanding the many negative environmental, economic, and social consequences. Recent advances in remote sensing technology combined with the increasing application of artificial intelligence methods have improved real-time, high-resolution fire monitoring. This study explores two proposed approaches based on the U-Net model for automating and optimizing the burned-area mapping process. Denoted 128 and AllSizes (AS), they are trained on datasets with a different class balance by cropping input images to different sizes. They are then applied to Landsat imagery and time-series data from two fire-prone regions in Chile. The results obtained after enhancement of model performance by hyperparameter optimization demonstrate the effectiveness of both approaches. Tests based on 195 representative images of the study area show that increasing dataset balance using the AS model yields better performance. More specifically, AS exhibited a Dice Coefficient (DC) of 0.93, an Omission Error (OE) of 0.086, and a Commission Error (CE) of 0.045, while the 128 model achieved a DC of 0.86, an OE of 0.12, and a CE of 0.12. These findings should provide a basis for further development of scalable automatic burned-area mapping tools.

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