Oil spill monitoring using satellite imagery in the Sharm El-Maya Bay of Sharm El-Sheikh, Egypt

Sharm el-Sheikh, in Egypt, is a prominent tourist destination. The city attracts millions of visitors annually due to its exceptional location and pleasant climate. Owing to its natural ecosystem and marine diversity, Sharm El-Maya Bay in Sharm el-Sheikh attracts beachgoers and vacationers. In 1999, however, an oil spill occurred at the site. Previous investigations detected a network of buried steel pipelines and a number of buried reinforced concrete tanks, both of which may have contributed to the contamination problem. Although the problem is so detrimental to health and the environment, no follow-up studies were conducted after 2013. Therefore, the author chose to monitor oil leaks over the headland using frequent, high-resolution Google Earth Pro remote sensing data for the years 2017 to 2022. To disclose whether any corrective measures were taken to address the contamination problem. Moreover, to demonstrate if any unanticipated variations have occurred over many years due to climatic factors. The elucidation of the aforementioned issues demonstrates Google Earth Pro's effectiveness in monitoring pollution problems. The results revealed that the area and perimeter of four oil spots had changed slightly over time. During the specified time period, the standard deviations of the four monitored locations fluctuated between 111.1 m2, 71.6 m2, 83.7 m2, and 254.3 m2. The research proved that the pollution problem has not improved over time because stakeholders have not reacted. In addition, it highlighted the uniqueness of Google Earth Pro in tracking the changes in oil spot size over a time series

Integrating Motion Priors For End-To-End Attention-Based Multi-Object Tracking

Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method

Preface: Workshop “Semantics3D - Semantic Scene Analysis and 3D Reconstruction from Images and Image Sequences”

[no abstract available

Enhancing The Quality Of CNN-Based Burned Area Detection In Satellite Imagery Through Data Augmentation

This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services

Bedoi: Benchmarks For Determining Overlapping Images With Photogrammetric Information

For conventional SfM pipeline, image matching is enduring limitation when considering the time efficiency. In the last few years, to speed up image matching procedure, many image retrieval works were proposed to fast find overlapping image pairs, e.g., bag-of-word that clusters hand-crafted local features in a hierarchical way for efficient similar image retrieval, or learning-based global features (such as, VGG or ResNet) are used to represent image in a global compact manner. However, there are rarely benchmarks with referenced overlapping information to: first, evaluate the retrieval performance; second, fine tune deep-learning models along the direction that is more capable to deal with overlapping image pairs. In this work, based on traditional photogrammetric procedures, relevant photogrammetric information is obtained including image orientation parameters, 3D mesh model and etc., we then generate a benchmark for determining Overlapping Images - BeDOI, in which referenced pairwise overlapping relationships are estimated via rigorous photogrammetric geometry. To extend the generality, in total, BeDOI contains 13667 images which are basically UAV and close-range images of various scene categories, e.g., urban cities, campus, village, historical relics, green land, buildings and etc. Lastly, to demonstrate the efficacy of the proposed BeDOI, several image retrieval methods are tested and the experimental results are reported as a competition challenge