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Object Instance Segmentation in Digital Terrain Models

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Authors

Research Organisations

Details

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns
Subtitle of host publication18th International Conference, CAIP 2019, Proceedings, part II
EditorsMario Vento, Gennaro Percanella
PublisherSpringer Verlag
Pages488-495
Number of pages8
ISBN (electronic)978-3-030-29891-3
ISBN (print)978-3-030-29890-6
Publication statusPublished - 22 Aug 2019
Event18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
Duration: 3 Sept 2019 → 5 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11679 LNCS
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

We use an object instance segmentation approach in deep learning to detect and outline objects in Digital Terrain Models (DTMs) derived from Airborne Laser Scanning (ALS) data. Object detection methods in computer vision have been extensively applied to RGB images, and gained excellent results. In this work, we use Mask R-CNN, a famous object detection model, to detect objects in archaeological sites by feeding the model with DTM data. Our experiments show successful application of the Mask R-CNN model, originally developed for image data, on DTM data.

Keywords

    Deep learning, Digital terrain models, Instance segmentation

ASJC Scopus subject areas

Cite this

Object Instance Segmentation in Digital Terrain Models. / Kazimi, Bashir; Thiemann, Frank; Sester, Monika.
Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II. ed. / Mario Vento; Gennaro Percanella. 1. ed. Springer Verlag, 2019. p. 488-495 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11679 LNCS).

Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review

Kazimi, B, Thiemann, F & Sester, M 2019, Object Instance Segmentation in Digital Terrain Models. in M Vento & G Percanella (eds), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II. 1. edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11679 LNCS, Springer Verlag, pp. 488-495, 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, Salerno, Italy, 3 Sept 2019. https://doi.org/10.1007/978-3-030-29891-3_43
Kazimi, B., Thiemann, F., & Sester, M. (2019). Object Instance Segmentation in Digital Terrain Models. In M. Vento, & G. Percanella (Eds.), Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II (1. ed., pp. 488-495). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11679 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-29891-3_43
Kazimi B, Thiemann F, Sester M. Object Instance Segmentation in Digital Terrain Models. In Vento M, Percanella G, editors, Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II. 1. ed. Springer Verlag. 2019. p. 488-495. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29891-3_43
Kazimi, Bashir ; Thiemann, Frank ; Sester, Monika. / Object Instance Segmentation in Digital Terrain Models. Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II. editor / Mario Vento ; Gennaro Percanella. 1. ed. Springer Verlag, 2019. pp. 488-495 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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