Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhanceme...
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MDPI AG
2025-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/2/102 |
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| author | Gabriel R. Pardini Paulo M. Tasinaffo Elcio H. Shiguemori Tahisa N. Kuck Marcos R. O. A. Maximo William R. Gyotoku |
| author_facet | Gabriel R. Pardini Paulo M. Tasinaffo Elcio H. Shiguemori Tahisa N. Kuck Marcos R. O. A. Maximo William R. Gyotoku |
| author_sort | Gabriel R. Pardini |
| collection | DOAJ |
| description | The Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region. |
| format | Article |
| id | doaj-art-8d20ab3c48a84842aff8c03ec8a659ae |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-8d20ab3c48a84842aff8c03ec8a659ae2025-08-20T03:11:06ZengMDPI AGAlgorithms1999-48932025-02-0118210210.3390/a18020102Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForestGabriel R. Pardini0Paulo M. Tasinaffo1Elcio H. Shiguemori2Tahisa N. Kuck3Marcos R. O. A. Maximo4William R. Gyotoku5Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos 12228-900, SP, BrazilInstituto Tecnológico de Aeronáutica (ITA), São José dos Campos 12228-900, SP, BrazilInstituto de Estudos Avançados (IEAv), São José dos Campos 12228-900, SP, BrazilInstituto de Estudos Avançados (IEAv), São José dos Campos 12228-900, SP, BrazilInstituto Tecnológico de Aeronáutica (ITA), São José dos Campos 12228-900, SP, BrazilInstituto Tecnológico de Aeronáutica (ITA), São José dos Campos 12228-900, SP, BrazilThe Amazon biome is frequently targeted by illegal activities, with clandestine mining being one of the most prominent. Due to the dense forest cover, criminals often rely on covert aviation as a logistical tool to supply remote locations and sustain these activities. This work presents an enhancement to a previously developed landing strip detection algorithm tailored for the Amazon biome. The initial algorithm utilized satellite images combined with the use of Convolutional Neural Networks (CNNs) to find the targets’ spatial locations (latitude and longitude). By addressing the limitations identified in the initial approach, this refined algorithm aims to improve detection accuracy and operational efficiency in complex rainforest environments. Tests in a selected area of the Amazon showed that the modified algorithm resulted in a recall drop of approximately 1% while reducing false positives by 26.6%. The recall drop means there was a decrease in the detection of true positives, which is balanced by the reduction in false positives. When applied across the entire biome, the recall decreased by 1.7%, but the total predictions dropped by 17.88%. These results suggest that, despite a slight reduction in recall, the modifications significantly improved the original algorithm by minimizing its limitations. Additionally, the improved solution demonstrates a 25.55% faster inference time, contributing to more rapid target identification. This advancement represents a meaningful step toward more effective detection of clandestine airstrips, supporting ongoing efforts to combat illegal activities in the region.https://www.mdpi.com/1999-4893/18/2/102convolutional neural networksdeep learninglanding strip detection |
| spellingShingle | Gabriel R. Pardini Paulo M. Tasinaffo Elcio H. Shiguemori Tahisa N. Kuck Marcos R. O. A. Maximo William R. Gyotoku Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest Algorithms convolutional neural networks deep learning landing strip detection |
| title | Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest |
| title_full | Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest |
| title_fullStr | Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest |
| title_full_unstemmed | Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest |
| title_short | Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest |
| title_sort | improved algorithm to detect clandestine airstrips in amazon rainforest |
| topic | convolutional neural networks deep learning landing strip detection |
| url | https://www.mdpi.com/1999-4893/18/2/102 |
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