A REMOTE SENSING APPROACH TO IDENTIFY SHIPPING EMISSIONS

Abstract

Shipping emissions are recognized as a significant contributor to climate change, increasing the frequency of extreme weather events. It is important to monitor transportation mobility for estimating emissions, and remote sensing data and methods are valuable due to their continuity, wide coverage, and ease of integration with other relevant data. Sentinel-2 data enables monitoring of transportation mobility and can be used as proxy data to infer shipping emissions. For this purpose, this study aims to monitor ship movements on a regional scale with YOLOX-assisted ship detection to estimate emissions. A new dataset was curated from Sentinel-2 RGB images provided by the European Space Agency’s Copernicus program, in which environmental and climate monitoring data are available free of charge. The dataset covers various locations of busy container ports to support data diversity. The YOLOX object detector, introduced to the computer vision community in 2021, is known for its versatility in detecting multi-scale objects. Thus, YOLOX was chosen to perform ship detection, and each standard YOLOX model (YOLOX-s-m-l-x) was trained on the dataset in separate sessions with default hyperparameters. As a result, the model with the highest evaluation score, YOLOX-l, was utilized to extract ship instances and localize ships from the dataset. Average precision (AP), average recall (AR), and F1-score were found to be 56.39%, 65.45%, and 60.48%, respectively. Ship extractions were utilized to estimate carbon monoxide (CO) emissions for 2023 in the port of Rotterdam, and a total of 148.504 tons of shipping-related CO emissions were inferred as a result.

Keywords

• Shipping Emissions
• Remote Sensing
• Ship Detection
• Deep Learning.

DENİZ TAŞIMACILIĞI KAYNAKLI EMİSYONLARIN BELİRLENMESİNDE UZAKTAN ALGILAMA YAKLAŞIMI

Abstract

Aşırı hava olaylarının görülme sıklığının artması ve iklim değişikliğinin önemli nedenlerinden birinin ulaştırma kaynaklı emisyonlar olduğu bilinmektedir. Emisyonların tahmini için ulaştırma hareketliliğinin izlenmesi gerekli olup, uzaktan algılama veri ve yöntemleri; sürekliliği geniş kapsama alanı ve ilgili diğer veriler ile bütünleştirilme kolaylığı nedeniyle öne çıkmaktadır. Bu çalışmada deniz taşımacılığı kaynaklı emisyonların tahmin edilmesi için bölgesel ölçekte gemi hareketlerinin derin öğrenme algoritmalarından YOLOX ile tespit edilmesi amaçlanmıştır. Sentinel-2 verisi ulaştırma hareketliliğinin izlenmesinde ve emisyon tahmininde yardımcı bir veri kaynağıdır. Bu sebeple, kara-deniz, çevre ve iklim izleme verileri araştırmacılara ücretsiz sunulmakta olduğu Avrupa Uzay Ajansı Copernicus programının bir parçası olan Sentinel-2 RGB görüntülerinden yeni bir veri seti oluşturulmuş olup, bu veri seti gemilerin otomatik çıkarımında kullanılmıştır. Veri çeşitliliği, model eğitiminde ve modelin genelleme yeteneğini artırmada önemli bir parametredir. Veri çeşitliliğini sağlamak ve modelin öğrenme kapasitesini artırmak için dünyanın farklı konteyner limanlarından alınan Sentinel-2 görüntüleri bir araya getirilmiş ve çeşitli ön işlem adımlarından geçirilmiştir. İlk kez 2021 yılında kullanıcılara tanıtılan YOLOX algoritması farklı büyüklükteki nesneleri tespit edebilme kapasitesine sahip bir algoritma olup, bu yönü ile önceki diğer YOLO mimarilerinden ayrılmaktadır. Bu sebeple, bu çalışma özelinde YOLOX algoritması tercih edilmiştir. Tespit doğruluğunu artırmak amacıyla, her bir standart YOLOX modeli (YOLOX-s-m-l-x) ayrı oturumlarda, hiperparametreler üzerinde değişiklik yapılmadan eğitilmiştir ve aralarından en yüksek metrik değerlere ulaşan YOLOX-l modeli ile gemiler tespit edilmiştir. Model ortalama kesinlik, ortalama duyarlılık ve F1 puanı sırasıyla %56.39, %65.45 ve %60.48 bulunmuştur. Gemi çıkarımları, Rotterdam limanında 2023 yılı deniz taşımacılığı kaynaklı karbonmonoksit (CO) emisyonlarının tahmininde kullanılmıştır ve sonuç olarak emisyon miktarı 148,504-ton olarak öngörülmüştür.

Keywords

• Deniz Taşımacılığı Kaynaklı Emisyonlar
• Uzaktan Algılama
• Gemi Tespiti
• Derin Öğrenme.

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