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

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DimitrisMantas/RoofSense

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