Combining LiDAR and Photogrammetry to Generate Up-to-date 3D City Models

Abstract

3D city models are increasingly used to maintain and improve urban infrastructure. Keeping 3D city models accurate and up-to-date is essential for municipalities to make decisions in a time of strongly increasing urbanization. 3D information provided by airborne laser scanning (ALS) is widely used for generating 3D city models. However, ALS data is sparse and irregularly spaced, and not frequently acquired due to its high costs. Airborne camera imagery (ACIM) is an alternative to extract denser but less accurate 3D information. Given these limitations in acquisition frequency and quality, using either ALS or ACIM to generate up-to-date large-scale 3D city models is sub-optimal. Therefore, we combine the complementary characteristics of both data sources to achieve two objectives: (i) 3D change detection and updating of buildings in ALS data using ACIM data, and (ii) improving the planimetric accuracy of building extraction from ALS data using ACIM data. ALS data is integrated with a single image or a single stereo pair for the first objective, and with multiple stereo pairs for the second objective. Our methods are validated over three areas: Vaihingen, Germany, and Amersfoort and Assen, the Netherlands. Shadow in a single image is indicative for a 3D object and is represented in the image by RGB color values. However, these color values are not unique, as they depend on the local conditions, such as material and environment. We propose a supervised machine learning approach, random forest, to effectively characterize the color properties. To generate training samples, accelerated ray tracing is used to efficiently reconstruct shadow locations in the image using 3D ALS data. Using shadow alone is not sufficient to detect accurate building changes, as shadows only partially represent 3D information. 3D information can be extracted from corresponding pixels in a stereo pair, but this information is not accurate in shadow and low texture areas. To address this, we propose LEAD-Matching (LiDAR-guided edge-aware dense matching). It starts from using accurate plane information extracted from ALS data to densify sparse ALS points. Three candidate heights are then obtained for each densified point to guide the dense matching in these problematic areas. Subsequently, detailed building information in the stereo pair is integrated to choose the final optimal height. If the optimal height obtained by LEAD-Matching points to corresponding pixels of different color, a likely building change is found. Test results on the Amersfoort and Assen data show a successful verification of unchanged buildings while changes are detected starting from 2 × 2 × 2 m 3 , as conventionally required for large-scale 3D mapping, with an F1 score of 0.8 and 0.9 respectively. To achieve the second objective, we extend LEAD-Matching to multiple stereo pairs, to improve the planimetric accuracy of building extraction in ALS data. E-LEAD-Matching integrates building boundaries of high planimetric accuracy from multiple stereo pairs to the ALS data. Using multiple stereo pairs, occlusions in single stereo pairs are compensated, while the accuracy of building boundaries is improved. Compared to using ALS alone, the planimetric accuracy of extracted buildings improves from 0.40 m to 0.22 m in Vaihingen, and from 0.48 m to 0.21 m in Amersfoort. This improved planimetric accuracy actually meets conventional requirements of large-scale mapping. Our methods enable us to integrate the beneficial aspects from ALS and ACIM to generate accurate and up-to-date large-scale 3D city models. We anticipate that our research will save both money and time in generating future up-to-date large-scale 3D city models.