# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 4 Conclusions  
The Amazon is the largest and most diverse biome on Earth, containing an unimaginable amount of natural resources. Due to this factor, irregular exploitation is an urgent issue in the Brazilian socio-environmental context, leading to increased crime rates and often causing deep scars on indigenous tribes. In this context, many of these activities face logistical problems, as in the Amazon, due to the high forest density, it is complex to establish supply lines to maintain activities. Therefore, clandestine aviation becomes an ally, enabling the delivery of necessary materials to any location in the forest with considerable speed.  
In this context, this work presented a modification of the seminal algorithm for solving this problem [33], aiming to address some of its shortcomings. For this purpose, pre-existing MapBiomas mappings were used to compose the training datasets for the neural networks. The modifications made caused a slight drop in the recall of the identified targets in both tests. However, there was a significant reduction in the number of false positives, which was a notable issue in the original algorithm.  
In the test conducted on a specific region of the biome, the recall dropped by less than 1%, but there was a 26.6% reduction in the number of false positives. For the test applied across the entire Amazon biome, the recall drop was slightly larger, at 1.7%, but the number of false positives decreased by 17.88%. These results indicate a substantial improvement in the algorithm, as the significant reduction in false positives leads to less time required for visual inspection of the predictions, thus enhancing the speed and efficiency of biome mapping. This, in turn, contributes significantly to Brazil's environmental protection efforts.  
One important point is that this improvement comes with a small loss in recall. However, since the recall values remain high, this trade-off is justified by the substantial reduction in false positives.  
Regarding future work, it would be important to explore how the modified algorithm can be implemented for large-scale, ongoing mapping of the biome. Furthermore, the recently released YOLOv11 network [13] could be an interesting addition to integrate into the presented algorithm for further performance enhancements. Additionally, an important aspect could be the implementation of the proposed methodology in other biomes worldwide to assess the overall effectiveness of the technique. Moreover, another relevant aspect is to check for the existence of water body maps of the Amazon to incorporate this information into the algorithm. Furthermore, it is relevant to conduct tests with other classical techniques to compare their performance against CNNs.  
[PERSON] designed the proposed model. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] supervised the article's writing and grammatical review. [PERSON] also wrote the theoretical part of the article. [PERSON] contributed to the introduction, specifically providing a brief evolution of research related to CNNs, highlighting how each study has contributed to advancements in the field. All authors have read and agreed to the published version of the manuscript.  
The authors thank the Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica--ITA). This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001. [PERSON] is partially funded by the National Research Council of Brazil (CNPq) through the grant 307525/2022-8. [PERSON] is also funded by the CNPq, under the grant process 316948/2023-3.  
Not applicable.  
Dataset available on request from the authors.  
I would like to thank the valuable improvement tips given by the good reviewers of this journal.  
The authors declare no conflicts of interest.