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Tifffile vs Opencv in Python

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If you’re using opencv and/or tifffile to prepare your dataset for training deep learning models, beware the difference between the imread function in both libraries, i.e., cv2.imread and tifffile.imread. It makes a huge difference, especially if you have custom, multi-channel images. Read more

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publications

Coverage for character based neural machine translation

Published in Procesamiento del Lenguaje Natural, 2017

This paper is about application of neural machine translation on German-English and Catalan-Spanish data Read more

Recommended citation: Kazimi, B. and Costa-jussà, M.R. (2017). "Coverage for Character Based Neural Machine Translation." Procesamiento del Lenguaje Natural. 59, pp.99-106.

Classification of laser scanning data using deep learning

Published in Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF), 2018

Deep learning is used to classify objects in airborne laser scanning data Read more

Recommended citation: Politz, F., Kazimi, B. and Sester, M. (2018). "Classification of laser scanning data using deep learning." Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF). 38, pp.7-9.

Deep learning for archaeological object detection in airborne laser scanning data

Published in Proceedings of the 2nd Workshop On Computing Techniques for Spatio-Temporal Data in Archaeology and Cultural Heritage, Melbourne, Australia, 2018

Deep learning is used for detection of archaeological objects in airborne laser scanning data Read more

Recommended citation: Kazimi, B., Thiemann, F., Malek, K., Sester, M. and Khoshelham, K. (2018). "Deep learning for archaeological object detection in airborne laser scanning data." Proceedings of the 2nd Workshop On Computing Techniques for Spatio-Temporal Data in Archaeology and Cultural Heritage, Melbourne, Australia. 15, pp.25-28.

Semantic segmentation of manmade landscape structures in digital terrain models

Published in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019

Deep learning is used for semantic segmentation of historical terrain structures in digital terrain models Read more

Recommended citation: Kazimi, B., Thiemann, F. and Sester, M. (2019). "Semantic segmentation of manmade landscape structures in digital terrain models." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. 42, pp.87-94.

Object instance segmentation in digital terrain models

Published in International Conference on Computer Analysis of Images and Patterns, 2019

Deep learning is used for instance segmentation of historical terrain structures in digital terrain models Read more

Recommended citation: Kazimi, B., Thiemann, F. and Sester, M. (2019). "Object instance segmentation in digital terrain models." International Conference on Computer Analysis of Images and Patterns. pp.488-495.

Detection of terrain structures in airborne laser scanning data using deep learning

Published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020

Deep learning is used for detection of terrain structures in airborne laser scanning data Read more

Recommended citation: Kazimi, B., Thiemann, F. and Sester, M. (2020). "Detection of terrain structures in airborne laser scanning data using deep learning." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2, pp.493-500.

Semi supervised learning for archaeological object detection in digital terrain models

Published in Proceedings of the 24th International Conference on Cultural Heritage and New Technologies, held in Vienna, Austria, November 2019, 2021

Semi supervised learning is used for detection of archaeological objects in digital terrain models Read more

Recommended citation: Kazimi, B., Malek, K., Thiemann, F. and Sester, M. (2021). "Semi supervised learning for archaeological object detection in digital terrain models." Proceedings of the 24th International Conference on Cultural Heritage and New Technologies, held in Vienna, Austria, November 2019. pp.219-225.

Self Supervised Learning for Detection of Archaeological Monuments in LiDAR Data

Published in Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover, 2021

Ph.D. dissertation on application of deep learning with LiDAR data in Archaeology Read more

Recommended citation: Kazimi, B. (2021). "Self Supervised Learning for Detection of Archaeological Monuments in LiDAR Data (Doctoral Dissertation)." Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover. 379

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.

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