Alignment of Building Footprints Using Quasi-Nadir Aerial Photography

Bulatov, Dimitri

doi:10.1007/978-3-030-20205-7_30

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, conducting the cross-correlation is a time-consuming process. In energy minimization-based approaches, the alignment problem is solved by designing and minimizing an energy function [143] [152]. Nevertheless, the algorithm for energy minimization encompasses a considerable number of parameters.…”

Section: G Annotation Data Correctionmentioning

confidence: 99%

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

Li,

Mou,

Sun

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

In the remote sensing community, extracting buildings from remote sensing imagery has triggered great interest. While many studies have been conducted, a comprehensive review of these approaches that are applied to optical and synthetic aperture radar (SAR) imagery is still lacking. Therefore, we provide an in-depth review of both early efforts and recent advances, which are aimed at extracting geometrical structures or semantic attributes of buildings, including building footprint generation, building facade segmentation, roof segment and superstructure segmentation, building height retrieval, building type classification, building change detection, and annotation data correction. Furthermore, a list of corresponding benchmark datasets is given. Finally, challenges and outlooks of existing approaches as well as promising applications are discussed to enhance comprehension within this realm of research.

show abstract

Section: G Annotation Data Correctionmentioning

confidence: 99%

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

Li,

Mou,

Sun

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

Localization and Grading of Building Roof Damages in High-Resolution Aerial Images

Böge

Bulatov

Lucks

2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

Using road detection and OSM reference data to refine airborne camera extrinsics in the Ahr Valley flooding use case

Qiu,

Demeur,

Piltz

et al. 2024

Earth Resources and Environmental Remote Sensing/Gis Applications XV

View full text Add to dashboard Cite

Aerial mapping provides high-resolution spatial data over large areas and is a cornerstone in urban planning, environmental monitoring and disaster management. To ensure geo-referencing and orthorectification, accurate external camera orientations (EO) are pivotal and indispensable. Often, the position and orientation data provided by the aerial platform is not accurate enough to create accurately georeferenced imagery. With our method, we improve the EO in a post-processing chain by referencing the extracted road network with the OpenStreetMap (OSM) road network. The dataset used in this paper was collected in the aftermath of the July 2021 Ahr Valley flooding disaster along the Ahr river in Germany, employing an aircraft operating at an altitude of 3000 feet. Encompassing an expansive area of 230 sq km, it includes the diverse landscapes of the Eifel mountains, urban and rural locales, as well as the inundated and devastated regions. We implement a machine learning semantic segmentation model, namely DeepLab V3+, utilizing the RGB imagery images captured by the aircraft to identify road center lines. It is necessary to employ this rather sophisticated method because traditional road detectors were thrown o↵ by the flooded areas, increasing false positive detections and limiting the EO optimization. We compute 3D reference points from available OSM road vector data combined with digital terrain model (DTM) elevation data. Using camera intrinsics and initial values for EOs, we project the reference points into the images and compute the distance to the detected road feature points. This distance is minimized to optimize EO parameters. Subsequently, we project the images onto an ortho-mosaic using the DTM, enhancing accuracy beyond that of the raw EO data measured by the aircraft. The resulting orthophoto map is almost free of visual artifacts and accurate in terms of geolocation, and can thus be utilized for disaster response applications.

show abstract

Alignment of Building Footprints Using Quasi-Nadir Aerial Photography

Cited by 3 publications

References 15 publications

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes

Localization and Grading of Building Roof Damages in High-Resolution Aerial Images

Using road detection and OSM reference data to refine airborne camera extrinsics in the Ahr Valley flooding use case

Contact Info

Product

Resources

About