elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Building facade segmentation of oblique aerial images using convolutional neural networks for urban climate modeling

Mönks, Milena (2019) Building facade segmentation of oblique aerial images using convolutional neural networks for urban climate modeling. Master's, Universität Greifswald.

Full text not available from this repository.

Abstract

In this thesis essential information about the urban surface is derived from oblique aerial images. This information serves as input for a modern and high-efficient urban climate model that is developed within the MOSAIK project. It aims at providing a tool of reliable urban climate models to evaluate and access changes of environmental conditions to support urban planning. First of all, segmentation of building facades allow to determine a window fraction. For this task, the state-of-the-art convolutional neural network architecture Mask R-CNN is trained with labeled terrestrial open-source data. During training image augmentation is applied to simulate the aerial perspectives. The knowledge about the window fraction paves the way to estimate radiation transfer and heat diffusion through windows. Furthermore, predominant colors of the facade are extracted to provide the wall albedo by applying a K-means clustering. The facade color could help to determine the surface material and thereby the heat capacity of the facade.

Item URL in elib:https://elib.dlr.de/126855/
Document Type:Thesis (Master's)
Title:Building facade segmentation of oblique aerial images using convolutional neural networks for urban climate modeling
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Mönks, Milenamilena.moenks (at) dlr.deUNSPECIFIED
Date:February 2019
Refereed publication:No
Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:63
Status:Published
Keywords:urban climate modeling, building facade segmentation, convolutional neural networks
Institution:Universität Greifswald
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Mönks, Milena
Deposited On:19 Mar 2019 09:25
Last Modified:19 Mar 2019 09:25

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.