Building footprint extraction using National Agriculture Imagery Program imagery and 3D Elevation Program lidar data a deep learning approach

Category

Published in:

Abstracts of the ICA
Vol. 10.2025 (12.12.2025), art. no. 177, p. 1-2

Volume:

10.2025

Publication date:

12.12.2025

URN:

urn:nbn:de:0307-20260226-000-8

DOI:

10.5194/ica-abs-10-177-2025

Language:

English

Type of Resource:

Text

Extent:

1 Online-Ressource (Seite 1-2, 729,40 kB) : 1 Textdatei (PDF)

Keywords:

building footprints; National Agricultural Imagery Program; NAIP; 3D Elevation Program; 3DEP; Lidar^GND; Deep Learning^GND; Gebäudegrundriss; Nationales Programm für landwirtschaftliche Bilddaten; Tiefgehendes Lernen

DDC subject of DNB:

520 Astronomy & allied sciences

URL:

https://doi.org/10.5194/ica-abs-10-177-2025

URL:

https://ica-abs.copernicus.org/articles/10/177/2025/

Link to Raw Object:

https://ica-abs.copernicus.org/articles/10/177/2025/ica-abs-10-177-2025.pdf

Hosting institution:

Ohio State University [US]

Hosting institution:

United States Geological Survey (USGS) [US]

Hosting institution:

Internationale Forschungseinrichtungen

Statement of responsibility:

Jung kuan Liu, Rongjun Qin, and Samantha Arundel

Bibliographic notes:

Published in: Abstracts of the ICA, Volume 10, 2025 - 32nd International Cartographic Conference (ICC 2025); 17–22 August 2025, Vancouver, Canada; Editors: Mir Abolfazl Mostafavi, Jonathan Li, and Emmanuel Stefanakis | https://ica-abs.copernicus.org/articles/10/index.html