Automated Classification of Building Roofs for the Updating of 3D Building Models Using Heuristic Methods
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science - Tập 88 - Trang 85-97 - 2020
Tóm tắt
In the Bavarian Surveying Administration, remote sensing methods are applied in the context of nationwide airborne surveys for the acquisition of aerial photographs and airborne laser scanning for the derivation of the digital terrain model (DTM). At the Bavarian Agency for Digitisation, High-Speed Internet and Surveying (LDBV = Landesamt für Digitalisierung, Breitband und Vermessung), image-based digital surface models (bDOM) and digital orthophotos without building lean (trueDOP) are produced using the dense image matching (DIM) method. Buildings and their roofs are displayed in the trueDOP in the correct position to the cadastral ground plan. Based on these data, an expert system was developed for the investigation of construction cases and for the updating of 3D building models, which automatically calculates change notices and makes them available to the Agencies for Digitisation, High-Speed Internet and Surveying (ADBV = Amt für Digitalisierung, Breitband und Vermessung). The reliable detection of buildings plays a decisive role here. Representative reference classes for the classification of building roofs in the RGB colour space are formed and frequencies are calculated across the boundaries of the photo-flights. The classification is carried out with the aid of a normalized digital surface model (nDOM), which is calculated from the height differences between the bDOM and the DTM, and with heuristically defined threshold values for the colours in the representative RGB colour spaces. The presented method is transferable to all federal states.
Tài liệu tham khảo
AdV (2016) Plenumsbeschluss 128/6, 128. Tagung des Plenums der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland, Sep 2016 in Bad Neuenahr-Ahrweiler, Germany
AdV (2017) AK GT Beschluss 30/2, zur Überführung des ATKIS-DOP20 in die Qualitätsstufe TrueDOP, Apr 2017 in Saarlouis, Germany
AdV (2018) Produktstandardblätter mit Stand vom 31.12.2018: https://www.adv-online.de/AdV-Produkte/Standards-und-Produktblaetter/Produktblaetter/. Accessed 23 Jul 2019
Alidoost F, Arefi H (2018) A CNN-based approach for automatic building detection and recognition of roof types using a single arial image. PFG J Photogramm Remote Sens Geoinf Sci 86:235–248
Aringer K, Roschlaub R (2013) Calculation and update of a 3D building model of Bavaria using Lidar, image matching and cadaster information. In: ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences, Vol II-2/W1, ISPRS 8th 3DGeoInfo Conference & WG II/2 Workshop, 27–29 Nov 2013, Istanbul, Turkey, pp. 7–12
Aringer K, Roschlaub R (2014) Bavarian 3D building model and update concept based on LiDAR, image matching and cadaster information. In: Iskidag U (ed) Lecture notes in geoinformation and cartography, innovations in 3D geo-information sciences. Springer International Publishing, Switzerland, pp 143–157
Geßler S, Krey T, Möst K, Roschlaub R (2019) Mit Datenfusionierung Mehrwerte schaffen—Ein Expertensystem zur Baufallerkundung. DVW-Mitteilungen 2(2019):159–187
Hümmer F, Roschlaub R (2014) Die Zukunft ist dreidimensional—3D-Gebäudemodelle in Bayern. DVW-Mitteilungen 2(2014):165–176
Roschlaub R (1992) Berechnung von Quantilen verschiedener statistischer Verteilungen. Zeitschrift für Vermessungswesen 117(6):323–335
Shibiao X, Xingjia P, Er L, Baoyuan W, Shuhui B, Weiming D, Shiming X, Xiaopeng Z (2018) Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors. IEEE Trans Geosci Remote Sens 56(12):7369–7387
Valinger J (2015) Automatic rooftop segment extraction using point clouds generated from aerial high resolution photography, MSc thesis, Umeå University, Faculty of Science and Technology, Department of Computing Science. https://pdfs.semanticscholar.org/4a08/d12b549f4685d859c6432ae26db930b5c796.pdf. Accessed 23 Jul 2019