Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value

• Published in: Journal of cultural heritage 2021-05, Vol.49, p.1-9
• Main Authors: Koutsoudis, Anestis, Ioannakis, George, Pistofidis, Petros, Arnaoutoglou, Fotis, Kazakis, Nikolaos, Pavlidis, George, Chamzas, Chistodoulos, Tsirliganis, Nestor
• Format: Article
• Language: English
• Subjects: 3D digitisation; 3D segmentation; annotation; architecture; disaster management; machine learning; multispectral; structure from motion; urban
• ISSN: 1296-2074; 1778-3674
• DOI: 10.1016/j.culher.2021.04.004

•Urban areas 3D digitisation using multispectral aerial imagery•Experimental low-cost multispectral camera for drone development•Supervised machine learning methods for 3D mesh segmentation using multispectral data•Performance evaluation of the proposed methods on a large scale case study•Web-based visualisation and annotation tool for segmentation fine-tuning Disaster risk management of movable and immovable cultural heritage is a highly significant research topic. In this work, we present a pipeline for 3D digitisation, segmentation and annotation of large scale urban areas in order to produce data that can be exploited in disaster management simulators (e.g fire spreading, crowd movement, firefighting training, evacuation planning, etc.). We have selected the old town of Xanthi (Greece) as a challenging case study. We developed a custom multispectral camera to be carried by a commercial drone. Using the structure from motion / multiview stereo (SFM/MVS) approach, we produced a 3D model of the urban area covering 0.5km2 that is followed by a multilayer texture map which carries information from visible and near-infrared regions of the electromagnetic spectrum. We developed a set of machine learning approaches based on logistic regression, support vector machines and artificial neural networks that allow 3D model segmentation by exploiting not only morphological and structural features but also the multispectral behaviour of different material surfaces. We objectively evaluate the performance of the proposed segmentation approaches on six significant material-based classes (cobbled-roads granite kilns, building walls, ceramic roof-tiles, low-vegetation, high-vegetation and metal surfaces) that are used in simulating fire propagation and crowd movement. The experiments revealed that the segmentation accuracy can be enhanced by taking into consideration surface material multispectral properties as well as morphological features. A Web-based multi-user annotation tool complements our proposed pipeline by enabling further 3D model segmentation, fine tuning and semantics annotation (e.g. usage-based building classification and evacuation priorities, escape paths and gathering points).