DimitrisMantas/RoofSense

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Model Card for RoofSense

An encoder-decoder semantic segmentation model for multimodal roofing material classification.

Model Details

Model Description

The model adopts an encoder-decoder architecture, pairing ResNet-18-D with DeepLabv3+. Following hyperparameter optimisation, the encoder blocks were augmented with anti-aliasing and efficient channel attention modules. In addition, the global average pooling blocks in the encoder were replaced with the mean of average and maximum pooling. Furthermore, dilation rates of the atrous spatial pyramid pooling block of the decoder were set to $\left(20, 15, 6\right)$. Finally, address any labelling errors and improve predicitions in small regions, the decorer output stride was set to sixteen.

• Developed by: Dimitris Mantas, Delft University of Technology, The Netherlands
• Model type: Fully Convolutional Neural Network
• License: Creative Commons Attribution 4.0 International
• Base Model: timm/resnet18d.ra2_in1k (Transfer Learning)

Model Sources

• Repository: https://github.com/DimitrisMantas/RoofSense
• Resources: https://repository.tudelft.nl/record/uuid:c463e920-61e6-40c5-89e9-25354fadf549

Uses

Direct Use

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How to Get Started with the Model

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Training Details

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Evaluation

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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