# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 1 Introduction  
Environmental conservation is a highly relevant topic in the global environmental context, especially with the ongoing climate changes. In this sense, the conservation of the Amazon holds special significance, given its vast area of over 5 million square kilometers [1]. Due to the large quantity and diversity of natural resources, the Amazon is often targeted by illegal exploratory activities, with illegal mining on indigenous lands being one of the most prominent [2]. According to [3], 91% of Brazilian mining is located in the Legal Amazon, which can lead to humanitarian problems due to the violent actions of such criminals against indigenous tribes. According to [4], if the Amazon were considered a country, it would have had the fourth highest homicide rate in the world in 2017, demonstrating how ingrained criminality is in this region. However, for such criminal activities to be carried out, it is necessary to establish consistent supply lines [5], given the high forest density. In this context, clandestine aviation acts as a logistical ally [6], transporting materials necessary for the maintenance of these activities. In 2021, there were 456 illegal airstrips within 5 km of mining sites [3], demonstrating the connection between these two factors. Additionally, in 2021 there were 804 airstrips within environmental conservation areas [3], showing the link between this activity and the deforestation of this biome. Thus, it becomes necessary to quickly and accurately map airstrips in the Amazon. In this context, a mapping of such targets already exists, carried out by [3]. However, some weaknesses in this mapping hinder its use by the competent authorities. Firstly, this mapping was conducted through visual inspection of satellite images, which complicates its reproducibility given the vastness of the Amazon biome, making the task less dynamic. Moreover, images from 2021 were used for this task, making the results less relevant to the present day. Some works aim to automate the search for such targets. One of the seminal works for identifying these targets was conducted by [PERSON] et al. [7]. In their approach, images from Synthetic Aperture Radar (SAR) sensors were used, and the Circularity Ratio (CR) [8] was calculated to assess the shape of the targets, selecting those that were more elongated and straight. Additionally, ref. [9] conducted a study to perform segmentation without using Artificial Neural Networks (ANNs). The proposed methodology focused on decomposing the RGB bands into three matrices and then generating a fourth grayscale matrix from them. After that, attributes with greater variability were searched for and grouped using clustering techniques. In this context, Convolutional Neural Networks (CNNs) have shown very promising results in target detection tasks since the work of [10], with new architectures frequently being launched that present superior results. For example, the You Only Look Once (YOLO) architecture, initially conceived by [11], shows very promising results in object detection tasks. Over the years, several versions of this architecture have been created, with new tasks being added. The authors of [12] presented the 8 th version of this network, which added the capability to perform image classification and segmentation. The latest version of YOLO is its 11 th version [13], which presents improvements over previous versions but only performs object detection tasks. A brief evolution in research related to CNNs shows how each study has contributed to the advancements in this field. Beginning with [14], their work had a significant impact on the development of CNNs, as their discoveries about the functional organization of the visual cortex, including receptive fields, orientation columns, and binocular interaction, provided a crucial theoretical foundation that inspired the structure and function of CNNs.