@inproceedings{Stabile2025AutomaticDetection,

title = {Automatic Detection of Airstrips in the Amazon Using ICEYE SAR Imagery and U-Net Neural Networks},

author = {Gustavo Henrique de Queiroz Stabile and Dimas Irion Alves and Tahisa Neitzel Kuck and Paulo Ricardo Branco Silva and Saleh Javadi},

url = {https://www.sige.ita.br/edicoes-anteriores/2025/TRABALHOS/Artigos-com-ISSN/Automatic Detection of Airstrips in the Amazon Using ICEYE SAR Imagery and U-Net Neural Networks.pdf},

issn = {1983-7402},

year  = {2025},

date = {2025-01-01},

booktitle = {Anais do Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa (SIGE)},

pages = {164\textendash169},

address = {S\~{a}o Jos\'{e} dos Campos, Brasil},

organization = {Instituto Tecnol\'{o}gico de Aeron\'{a}utica},

abstract = {This study investigates convolutional neural networks, specifically the U-Net architecture, for the automated detection of irregular airstrips in the Amazon rainforest using Synthetic Aperture Radar (SAR) imagery from ICEYE satellites operating in the X-band. Detecting these airstrips is strategic for combating illicit activities and protecting the environment. SAR imagery is particularly effective in the Amazon, as it can penetrate cloud coverage, which is common in the region. A comprehensive pipeline was developed for data preparation, model training, and evaluation, utilizing the airstrips cataloged by the MapBiomas project for reference. Experiments were conducted by varying both the size of the input images and the balance of the training dataset. Results indicated that even with a limited number of images, the U-Net architecture can generate consistent outcomes. The study supports the development of operational solutions for the automated monitoring of irregular airstrips in the Amazon region.},

keywords = {airstrips, automatic detection, Deep Learning, Synthetic Aperture Radar},

pubstate = {published},

tppubtype = {inproceedings}

}