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An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets

Hou, Yu; Chen, Meida; Volk, Rebekka; Soibelman, Lucio

As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000139644
Veröffentlicht am 05.11.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000139644
Erschienen in Remote sensing
Verlag MDPI
Band 13
Heft 21
Seiten Article no: 4357
Vorab online veröffentlicht am 29.10.2021
Schlagwörter building thermal modeling; building semantic segmentation; energy audits; instance segmentation; thermal and RGB data fusion
Nachgewiesen in Web of Science
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