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RGB Point Cloud and Thermal Information Data Fusion for Building Thermal Map Modeling

  • Reconstruct 3D models using high-resolution RGB images and project thermal information onto the 3D models.

  • Investigate how the RGB and thermal data fusion approach performs under various conditions when modeling large areas.

  • Comparison between data fusion performances of tie points and all points in a 3D model.

  • Provide strategies for improving the accuracy of the data fusion approach in terms of UAS flight configurations.


Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets

  • We proposed to detect thermal bridges on roofs.

  • We proposed to study differentiating salient components, such as facades and roofs where energy loss was important to monitor, as well as from peripheral components such as cars and equipment where heat loss was not our focus, but which may interface with the study.

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Drone-Assisted Building Inspection Training

The Advanced Built Environments and Construction (ABEC) lab from Western New England University publishes a series of drone training and learning modules for the construction industry. This learning module introduces a virtual environment for participants to operate a virtual drone to inspect building envelopes. Participants can use RGB and thermal cameras to monitor the inspection on a virtual iPad tablet. The thermal camera has two modes: grayscale and heatmap.

Semantic Point Segmentation

  • Our project aims to provide an extensive database of annotated ground truth point clouds reconstructed by aerial image photogrammetry.

  • Training and validating 3D segmentation.



Drone-Human Interaction

Cyber-Physical-Human System Powered Immersive Training for Construction Workers with Disabilities to Inspect Building Envelopes Using Drones

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