Segmentation of 3D reconstruction panels

Using Deep Learning and Aerial Imagery, we first detect roof edges from the high res. aerial imagery and then post-process it to respective roof segments.

For 3D reconstruction, we use elevation data to approximate roof height and tilt which then is being used for a geometrical 3D reconstruction of the house based on the surface of each roof segment.

Using the same ConvNet architecture we predict roof obstructions, and then we place the maximum allowed number of solar panels considering on fire setback areas (yellow colored areas) and the predicted obstructions.

By performing shading analysis using elevation data and approximated sun positions, we then select optimized panels for energy production.

3D Home Reconstruction by the Advances in Computer Vision

3D building reconstruction from point clouds is an active research topic in remote sensing, photogrammetry and computer vision. LiDAR (light detection and ranging: is a remote sensing technology that measures distance by illuminating a target with a laser and analyzing the reflected light. Among other applications, LiDAR is useful for detailed mapping of terrain, elevation, structures, and change detection in disaster management at several levels. The field is rapidly maturing in capabilities, applications, and utility. LiDAR data have rich use cases in city management and damage assessment applications. One crucial processing task in these LiDAR applications is building detection. Digital 3D city models serve nowadays a wide range of application fields, such as urban planning, environmental simulations, navigation, location-based services, virtual 3D globes and 3D landscape visualizations, etc. Automated building detection from airborne LiDAR data sets is a challenging task. We briefly review recent approaches to reconstruct 3D buildings from multi-view images or photogrammetric point clouds/DSMs (Digital Surface Model). We divide these approaches as follows: data-driven methods and model-driven methods the data-model hybrid methods are classified as model-driven methods. Our proposed method belongs to model–driven. The framework for our 3D building reconstruction method consists of three main parts. Building footprint extraction. Building height estimation (Building height is the distance from a building’s base to its rooftop. Each LoD1 building (i.e., the prismatic model with horizontal roof and base) has a constant height value. The commonly used approach for estimating a building’s height is subtracting a produced DTM (digital terrain model) from the DSM within a building footprint. In this part we are going to use Gevaert idea which proposed a method for DTM extraction from imagery which first applies morphological filters to the Digital Surface Model to obtain candidate ground and off-ground training samples) Combination of the footprint and the height between the roof and base form a water-tight LoD1 building model.