1 research outputs found
Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds
3D urban reconstruction of buildings from remotely sensed imagery has drawn
significant attention during the past two decades. While aerial imagery and
LiDAR provide higher resolution, satellite imagery is cheaper and more
efficient to acquire for large scale need. However, the high, orbital altitude
of satellite observation brings intrinsic challenges, like unpredictable
atmospheric effect, multi view angles, significant radiometric differences due
to the necessary multiple views, diverse land covers and urban structures in a
scene, small base-height ratio or narrow field of view, all of which may
degrade 3D reconstruction quality. To address these major challenges, we
present a reliable and effective approach for building model reconstruction
from the point clouds generated from multi-view satellite images. We utilize
multiple types of primitive shapes to fit the input point cloud. Specifically,
a deep-learning approach is adopted to distinguish the shape of building roofs
in complex and yet noisy scenes. For points that belong to the same roof shape,
a multi-cue, hierarchical RANSAC approach is proposed for efficient and
reliable segmenting and reconstructing the building point cloud. Experimental
results over four selected urban areas (0.34 to 2.04 sq km in size) demonstrate
the proposed method can generate detailed roof structures under noisy data
environments. The average successful rate for building shape recognition is
83.0%, while the overall completeness and correctness are over 70% with
reference to ground truth created from airborne lidar. As the first effort to
address the public need of large scale city model generation, the development
is deployed as open source software