  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
