Buch, Englisch, Band 304, 597 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 925 g
Unmanned Aerial System in Geomatics
Buch, Englisch, Band 304, 597 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 925 g
Reihe: Lecture Notes in Civil Engineering
ISBN: 978-3-031-19311-8
Verlag: Springer Nature Switzerland
This volume gathers the latest advances, innovations, and applications in the field of geographic information systems and unmanned aerial vehicle (UAV) technologies, as presented by leading researchers and engineers at the 2 International Conference on Unmanned Aerial System in Geomatics (UASG), held in Roorkee, India on April 2-4, 2021. It covers highly diverse topics, including photogrammetry and remote sensing, surveying, UAV manufacturing, geospatial data sensing, UAV processing, visualization, and management, UAV applications and regulations, geo-informatics and geomatics. The contributions, which were selected by means of a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Technische Wissenschaften Bauingenieurwesen Boden- und Felsmechanik, Geotechnik
- Technische Wissenschaften Bauingenieurwesen Vermessungswesen
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Geowissenschaften Geographie | Raumplanung Geographie: Allgemeines, Karten & Atlanten
Weitere Infos & Material
Chapter 1: Comparison of DEM generated from UAV images and ICESat-1 Elevation Datasets with an assessment of the Cartographic Potential of UAV-based Sensor Datasets.- Chapter 2: UAV to Cadastral Parcel Boundary Translation and Synthetic UAV Image Generation Using Conditional-Generative Adversarial Network.- Chapter 3: UAV-based terrain-following mapping using LiDAR in high undulating catastrophic areas.- Chapter 4: Forest Fire Detection from UAV Images using Fusion of Pre-trained Mobile CNN Features.- Chapter 5: Deep Learning-based Improved Automatic Building Extraction from Open-Source High-Resolution Unmanned Aerial Vehicle (UAV) Imagery.
