Rooftops Classification and Solar Installation Acceleration using Deep Learning

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Jul 29, 2021
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Rooftops Classification and Solar Installation Acceleration using Deep Learning

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In this article, we will go through the process of commercial rooftops classification using Deep Learning methods. And accelerate the installation of solar energy in North America, as a part of Omdena’s challenge with a Techstars EnergyTech startup.

A walkthrough in understanding the use of labeling tools like CVAT in labeling satellite imagery, the difference between semantic and instance segmentations with rooftops images pixels, and training different pre-trained deep learning models to get the best roof classification results.

Solar energy hasn’t reached its full potential as a clean energy source for the United States yet. According to National Renewable Energy Laboratory (NREL) analysis in 2016, there are over 8 billion square meters of rooftops on which solar panels could be installed in the United States, representing over 1 terawatt of potential solar capacity.

Therefore, significant work remains to be done to accelerate the deployment of solar energy in the US by identifying potential locations and rooftops classification.

Problem Statement

Data preparation and images labeling

Roof classification - Source: Omdena

Demonstration of labeling done on a rooftop in CVAT — source: Omdena

Roof classification - Source: Omdena

GIF showing CVAT export formats — source: Omdena

  • Flat: Rooftops with a single flat surface with/without clutters
  • Complex Rooftop: Rooftops with multiple surfaces at different heights
  • Existing Solar: Rooftops with solar panels
  • Heavy Industrial: Rooftops with pipes, and cluttered with machinery
  • Slope: Rooftops with an inclined surface
Image labeling for roof classification - Omdena

Examples of images and their label — Source: Omdena

Rooftop Segmentation

We needed to try different segmentation methods to get the best results.

Semantic Segmentation

Example of mask used for labeling — source: Omdena

Example of mask used for labeling — source: Omdena

U-Net binary segmentation of rooftops — source: Omdena

U-Net binary segmentation of rooftops — source: Omdena

Multi-classes semantic segmentation results with U-Net — source: Omdena

Multi-classes semantic segmentation results with U-Net — source: Omdena

Deep-Lab predictions — source: Omdena

Deep-Lab predictions — source: Omdena

Instance Segmentation

Labeled rooftops images exported into COCO format — source: Omdena

Labeled rooftops images exported into COCO format — source: Omdena

‘AP-flat’: 34.46745607306496
‘AP-slope’: 9.342863008612126
‘AP-existing_solar’: 1.7519702923446772
‘AP-complex_rooftop’: 14.343733930311137
‘AP-heavy_industrial’: 5.055339100777756

Mask R-CNN results — source: Omdena

Mask R-CNN results — source: Omdena

Examples of the wrong classification with Mask R-CNN — source: Omdena

Examples of the wrong classification with Mask R-CNN — source: Omdena

Predictions Mask R-CNN — source: Omdena

Predictions Mask R-CNN — source: Omdena

Mask R-CNN improved results with learning rate = 0.001 and 4000 epochs — source: Omdena

Mask R-CNN improved results with learning rate = 0.001 and 4000 epochs — source: Omdena

Box Localization Loss — 0.70
Confidence Loss — 1.6
Mask Loss — 1.1
Semantic Segmentation Loss — 0.40

Yolact’s results for roof classification — source: Omdena

Yolact’s results for roof classification — source: Omdena

 

Final Deliverable

Visual of the rooftops classification Streamlit application in the class prediction section of the application — source: Omdena

Visual of the roof classification Streamlit application in the class prediction section of the application — source: Omdena

Conclusion

More approaches accomplished by Omdena in the Energy sector:

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Margaux Masson-Forsythe

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Margaux Masson-Forsythe

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