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Open AccessArticle

Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network

1
Signal Processing in Earth Observation (Sipeo), Technical University of Munich (TUM), 80333 Munich, Germany
2
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
3
Remote Sensing Technology (LMF), Technical University of Munich (TUM), 80333 Munich, Germany
4
Bavarian Agency for Digitization, High-Speed Internet and Surveying (LDBV), 80538 Munich, Germany
5
Department of Geoinformatics, Munich University of Applied Sciences, 80333 Munich, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3537; https://doi.org/10.3390/rs12213537
Received: 27 September 2020 / Revised: 21 October 2020 / Accepted: 23 October 2020 / Published: 28 October 2020
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as “building” by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are “building” in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort. View Full-Text
Keywords: building detection; Convolutional Neural Network; deep learning; semantic segmentation; decision fusion building detection; Convolutional Neural Network; deep learning; semantic segmentation; decision fusion
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MDPI and ACS Style

Li, Q.; Shi, Y.; Auer, S.; Roschlaub, R.; Möst, K.; Schmitt, M.; Glock, C.; Zhu, X. Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network. Remote Sens. 2020, 12, 3537. https://doi.org/10.3390/rs12213537

AMA Style

Li Q, Shi Y, Auer S, Roschlaub R, Möst K, Schmitt M, Glock C, Zhu X. Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network. Remote Sensing. 2020; 12(21):3537. https://doi.org/10.3390/rs12213537

Chicago/Turabian Style

Li, Qingyu; Shi, Yilei; Auer, Stefan; Roschlaub, Robert; Möst, Karin; Schmitt, Michael; Glock, Clemens; Zhu, Xiaoxiang. 2020. "Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network" Remote Sens. 12, no. 21: 3537. https://doi.org/10.3390/rs12213537

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