Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study

• URL: http://arxiv.org/abs/2101.03409v1
• Date: Sat, 9 Jan 2021 19:05:03 GMT
• Title: Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study
• Authors: Gabriel Henrique de Almeida Pereira and Andr\'e Minoro Fusioka and Bogdan Tomoyuki Nassu and Rodrigo Minetto
• Abstract summary: This paper introduces a new large-scale dataset for active fire detection using deep learning techniques. We present a study on how different convolutional neural network architectures can be used to approximate handcrafted algorithms. The proposed dataset, source codes and trained models are available on Github.
• Score: 1.3764085113103217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement. This is a well established field, with many techniques being proposed over the years, usually based on pixel or region-level comparisons involving sensor-specific thresholds and neighborhood statistics. In this paper, we address the problem of active fire detection using deep learning techniques. In recent years, deep learning techniques have been enjoying an enormous success in many fields, but their use for active fire detection is relatively new, with open questions and demand for datasets and architectures for evaluation. This paper addresses these issues by introducing a new large-scale dataset for active fire detection, with over 150,000 image patches (more than 200 GB of data) extracted from Landsat-8 images captured around the world in August and September 2020, containing wildfires in several locations. The dataset was split in two parts, and contains 10-band spectral images with associated outputs, produced by three well known handcrafted algorithms for active fire detection in the first part, and manually annotated masks in the second part. We also present a study on how different convolutional neural network architectures can be used to approximate these handcrafted algorithms, and how models trained on automatically segmented patches can be combined to achieve better performance than the original algorithms - with the best combination having 87.2% precision and 92.4% recall on our manually annotated dataset. The proposed dataset, source codes and trained models are available on Github (https://github.com/pereira-gha/activefire), creating opportunities for further advances in the field

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