On the Application of Data Clustering Algorithm used in Information Retrieval for Satellite Imagery Segmentation

This study proposes an automated technique for segmenting satellite imagery using unsupervised learning. Autoencoders, a type of neural network, are employed for dimensionality reduction and feature extraction. The study evaluates different segmentation architectures and encoders and identifies the best performing combination as the DeepLabv3+ architecture with a ResNet-152 encoder. This approach achieves high performance scores across multiple metrics and can be beneficial in various fields, including agriculture, land use monitoring, and disaster response

Using Semantic Segmentation for the Damage Detection of Port and Marine Infrastructures

The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by sensing the entire infrastructure by hand. Such a process is costly as it requires a lot of time and manpower. To overcome these difficulties, we propose to map the harbour structure above and below water with a multi-sensor system and try to automate the classification process in terms of common damage types using deep learning approaches. In the images taken \rev{above} water, damaged and undamaged zones are localised using a semantic segmentation approach. We make use of a real data set captured at JadeWeserPort Wilhemlshaven to test our approach. The images are divided into smaller sections of 512x512 pixels and these are propagated through the DeepLabv3+ architecture, a modern convolutional neural network for semantic segmentation tasks, which is trained in particular to detect corrosion or rust anomalies. We achieve with a pre-trained ResNet-50 backbone and fully supervised data set IoU scores of 96.0 % and 55.9 % for undamaged and damaged zones as well as F1-scores of 98.0 % and 71.7 %. We show that our approach can achieve a fully automated and reproducible image segmentation and damage detection which can analyse the whole structure instead of the sample-wise manual method.Bundesministerium für Digitales und Verkehr/Innovative Hafentechnologien/19H18011