###### Abstract  
Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%. f  
refirefront_gestosa; deep learning; deeplabv3+; backbone; dice loss; image processing Article  
Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+  
Footnote 3: [[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))  
**H.F. Assessing the Impact of the Loss Function and Encoder Architecture**  
**2022**  
**2022**  
the region with the most burned area was observed to be the cluster of Guarda, with 304 thousand hectares, accounting for 12% of the total burnt area in Portugal. The devastating wildfires in Portugal set most of the regions in the country on high alert for fires. Several factors contribute to wildfires [3]; factors like human negligence or other biomes due to lack of awareness toward the environment appear to be some predictable causes of wildfires [4]. Some unpredictable factors include lightning, high atmospheric temperature, or dryness due to global warming with rapid industry growth and air pollution [1]. Moreover, dryness, wind, and humidity in the atmosphere set favorable conditions for the propagation of forest fires [5; 6; 7]. In turn, it affects the inhabitant of the forest and eco-system to a greater extent. Besides, it impacts the economic growth and harmony of the agricultural and industrial sectors. Thus, generally, it is essential to identify the vulnerable forest fire regions and spot the real fire zones to eliminate unnecessary movement of fire fighters' teams [8]. The present work addresses the above challenge by implementing an on-site detection framework and spotting the fire pixels in the original picture in real-time. It is achieved by adopting a deep learning architecture, Deeplabv3+ [9], an extended version of the already existing model. This parameter of the Deeplabv3+ is lined tuned with several experimental trails that attain better performance. The experiments were conducted on two public aerial datasets: the Corsican dataset [10] and FLAME [11], and one private dataset, Firefront_Gestosa (The Firefront_Gestosa database is available upon request to the authors.). Compared to the public datasets, the private dataset comprises fewer fire pixels, however not labeled. Thus, for experiments, the data in the private dataset are labeled manually in the present work.