Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Computer Analysis of Images and Patterns |
| Untertitel | 18th International Conference, CAIP 2019, Proceedings, part II |
| Herausgeber/-innen | Mario Vento, Gennaro Percanella |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 488-495 |
| Seitenumfang | 8 |
| Auflage | 1. |
| ISBN (elektronisch) | 978-3-030-29891-3 |
| ISBN (Print) | 978-3-030-29890-6 |
| Publikationsstatus | Veröffentlicht - 22 Aug. 2019 |
| Veranstaltung | 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italien Dauer: 3 Sept. 2019 → 5 Sept. 2019 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 11679 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings, part II. Hrsg. / Mario Vento; Gennaro Percanella. 1. Aufl. Springer Verlag, 2019. S. 488-495 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11679 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Object Instance Segmentation in Digital Terrain Models
AU - Kazimi, Bashir
AU - Thiemann, Frank
AU - Sester, Monika
PY - 2019/8/22
Y1 - 2019/8/22
N2 - 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.
AB - 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.
KW - Deep learning
KW - Digital terrain models
KW - Instance segmentation
UR - http://www.scopus.com/inward/record.url?scp=85072854149&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29891-3_43
DO - 10.1007/978-3-030-29891-3_43
M3 - Conference contribution
AN - SCOPUS:85072854149
SN - 978-3-030-29890-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 488
EP - 495
BT - Computer Analysis of Images and Patterns
A2 - Vento, Mario
A2 - Percanella, Gennaro
PB - Springer Verlag
T2 - 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019
Y2 - 3 September 2019 through 5 September 2019
ER -