Machine-learned 3D Building Vectorization from Satellite Imagery
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and panchromatic (PAN) image as input, we first filter out non-building objects and refine the building shapes of input DSM with a conditional generative adversarial network (cGAN). The refined DSM and the input PAN image are then used through a semantic segmentation network to detect edges and corners of building roofs. Later, a set of vectorization algorithms are proposed to build roof polygons. Finally, the height information from the refined DSM is added to the polygons to obtain a fully vectorized level of detail (LoD)-2 building model. We verify the effectiveness of our method on large-scale satellite images, where we obtain state-of-the-art performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkSemantic SegmentationSimilar Papers 제목 키워드 기반
Synthesis and Completion of Facades from Satellite Imagery
Automatic satellite-based reconstruction enables large and widespread creation of urban areas. However, satellite imagery is often noisy and incomplete, and is not suitable for reconstructing detailed building facades. W…
Now you see it, Now you don't: Damage Label Agreement in Drone & Satellite Post-Disaster Imagery
This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02% label disagreement and significantly different di…
Building Damage AssessmentBuilding Damage Detection in Satellite Imagery Using Convolutional Neural Networks
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides th…
BIG-bench Machine LearningGeoFormer: A Multi-Polygon Segmentation Transformer
In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmentation maps into the final scale invaria…
SENSE: Satellite-based ENergy Synthesis for Sustainable Environment
Urban Building Energy Modeling plays a critical role in achieving the United Nations' Sustainable Development Goals 7 and 11. Although existing studies based on satellite imagery and deep learning have achieved remarkabl…