Use of Aerial Images for Regular Updates of Buildings in the Fundamental Base of Geographic Data of the Czech Republic

Abstract

ABSTRACT:Digital aerial images (DAI) include position, elevation and also spectral information (visible bands and near-infrared band) about the captured area. The aim of this paper is to present the possibilities of automatic analysis of DAI for updating of the Fundamental Base of Geographic Data of the Czech Republic with a focus on buildings. Regular updates of buildings (automatic detection of new and demolished buildings) are based on the analysis of coloured point clouds created by an automatic image matching technique from each time period. The created approach compares point clouds from different time periods to each other. The advantage of this solution is that it is independent of the manner of keeping the buildings in the database. It does not matter whether the buildings in the database have correct positions and their footprints correspond to the roof shapes or external walls. The involved method is robust because a digital surface model generated by image matching techniques can contain numerous errors. Shaded areas and objects with blurred textures are problematic for automatic image correlation algorithms and lead to false results. For this reason, derived layers containing additional information are used. Shadow masks (layers with modelled shadows) are used for the verification of indications and to filter out errors in the shaded areas using a contextual evaluation. Furthermore, additional information about the road and railway networks and morphological operations of opening and closing were used to achieve more accurate results. All these information sources are then evaluated using decision logic, which uses the generally applicable rules that are available for different datasets without the need for modification. The method was tested on different datasets with various types of buildings (villages, suburbs and city centres) which cover more than 20 square kilometres. The developed solution leads to very promising results without the need of acquiring new data.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

Nationwide Hybrid Change Detection of Buildings

“…The method presented in this paper is an improved version of that presented in (Hron and Halounova, 2015). The previous version of this method distinguished only between new and demolished buildings.…”

Automatic Detection of Building Points From Lidar and Dense Image Matching Point Clouds

“…Since this colour channel is much more important than the blue channel, aerial images were prepared in false colours, created with the combination of near-infrared, red and green channels (Hron and Halounova, 2015). Noted, that in case of lack of NIR channel, machine learning techniques could be used to detect vegetation.…”

“…Also, alternative approaches combining the advantages of both LIDAR and multispectral images have been applied (Liao and Huang, 2012). Hron and Halounova (2015) focused on point clouds that derived from digital aerial images using derived layers that contained additional information about the road and railway networks, shadow and vegetation/NDVI masks, etc.…”

Aνίχνευση Και Εντοπισμός Μεταβολών Κτισμάτων Από Εικόνες Και Νέφη Σημείων

“…Παρά την εξαγωγή ιδιαίτερα ικανοποιητικών αποτελεσμάτων, η συνδυαστική χρήση ετερογενών δεδομένων υστερεί λόγω: 1) του πρόσθετου οικονομικού κόστους συλλογής τους, 2) του πρόσθετου υπολογιστικού κόστους επεξεργασίας τους, και 3) των πιθανών συστηματικών σφαλμάτων κατά τον συνδυασμό τους. Για τον λόγο αυτό, οι σύγχρονες τάσεις τείνουν προς την ανάπτυξη μεθοδολογιών που έχουν τη δυνατότητα αξιοποίησης δεδομένων από ένα δέκτη, όπως για παράδειγμα η αξιοποίηση των διαθέσιμων εικόνων και των αντίστοιχων παραγόμενων προϊόντων τους (νέφη σημείων από DIM) [191][192][193][194] (κεφάλαια 4 και 5). Κάποιες δημοφιλείς αρχιτεκτονικές CNNs είναι οι: AlexNet [265], VGGNet [266], GoogleNet [267], SqueezeNet [268] και Ensemble Convolutional Neural Networks [264] (Εικόνα 2.15).…”