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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Improving Object Detection Performance on Hard to Detect Instances in DOTA
This project improved mean Average Precision (mAP) for hard-to-detect instances in the DOTA dataset. I implemented dataset augmentation (oversampling, copy-pasting hard-to-detect objects and their bounding boxes within images), preprocessing pipelines, training routines, object detection evaluation metrics, and post-processing in PyTorch to train a YOLOv5 model for object detection. The approach increased mAP for hard-to-detect instances significantly in comparison to the baseline. (Paper, Code)
Entropy Maximization for Anomaly Detection in Complex Driving Scenes Using a UNet Architecture
This project trained deep learning segmentation models to detect out-of-distribution objects in driving scenes using entropy maximization. Real-world scenarios often feature classes not included in training datasets. To detect these objects, I implemented entropy maximization, which modifies the standard cross-entropy loss function to boost the model’s prediction entropy for out-of-distribution objects. A U-Net model was trained using this modified loss function and compared to baseline models. The results indicate a substantial improvement in the anomaly detection capability of the U-Net model, with a significant increase in segmentation metrics like AUROC and AUPRC and a decrease in FPR95. (Paper, Code) 
From Scratch Repository
To gain a deeper understanding of modern chat tools like ChatGPT, I implemented a decoder-only transformer model from scratch using PyTorch. Additionally, I developed a pipeline to extract song lyrics of any given artist from the web, enabling the transformer model to be trained on these lyrics for the creation of new songs. For further comprehension, I also implemented backpropagation, Multi-Layer-Perceptron (MLP) and Batch Normalization from scratch.
Master’s Thesis: Reconstructing CAD models of non-complex objects from point clouds: A data-driven approach
Point cloud data from laser scanners can be acquired in large quantities, yet it lacks explicit connectivity. CAD models, in contrast, offer structured and parametric representations used widely across industry. In this work I developed a deep learning model that transforms point clouds of scanned objects into their corresponding CAD models (sketch-and-extrude operations). The model is trained on the publicly available DeepCAD dataset. To improve reconstruction performance, a segmentation approach is applied that splits each CAD model into its individual extrusions, forming primitive CAD models. Experiments show that training on these segmented primitives not only increases reconstruction quality and model generalizability but also enables the inference of more complex CAD models. (Paper, Code)
PC2Model: LiDAR simulation for ISPRS benchmark dataset
Developed and curated a point cloud to 3D model registration dataset within a small team. Investigated multiple 3D model datasets to select samples. Created a Python Blender add-on to interface with the Helios++ laser-scanning simulation software and conducted simulations of objects and buildings. Co-authored the paper for the dataset for submission to ISPRS Toronto 2026 and gained hands-on experience with real LiDAR scanners. (Paper, Code)
publications
Multi-temporal Crack Segmentation in Concrete Structures using Deep Learning Approaches
Published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025
This paper addresses the importance of structural health monitoring for civil concrete structures, which are vital to modern society. Cracks are one of the earliest indicators of structural decay, therefore, early detection is important. Using deep learning, the study focuses on pixel-level image segmentation to identify cracks. For this, the temporal domain of crack progression is addressed in this work. I manually created and annotated a multi-temporal crack propagation dataset for semantic segmentation, consisting of 1356 samples, each illustrating crack growth on concrete over a series of 32 sequential images. A Swin Transformer adapted for three-dimensional input (Swin UNETR) was trained on the multi-temporal dataset. Its performance was compared to a mono-temporal model (U-Net) that was trained on exactly the same, but deserialized, data. Results demonstrate that the multi-temporal approach surpasses the mono-temporal model in terms of IoU and F1-score significantly, alongside superior visual predictions, all while using half the parameters of the mono-temporal model. The multi-temporal approach holds potential for long-term structural health monitoring of concrete structures, even with limited sequential data. (Paper, Code)
Recommended citation: Harb, S., Achanccaray Diaz, P., Maboudi, M., and Gerke, M.: Multi-temporal crack segmentation in concrete structures using deep learning approaches, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 341–348, https://doi.org/10.5194/isprs-annals-X-G-2025-341-2025, 2025.</i>
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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