A Comparative Study of Knowledge Transfer Methods for Misaligned Urban
  Building Labels
Bipul Neupane Jagannath Aryal Abbas Rajabifard
Misalignment in Earth observation (EO) images and building labels impact the
training of accurate convolutional neural networks (CNNs) for semantic
segmentation of building footprints. Recently, three Teacher-Student knowledge
transfer methods have been introduced to address this issue: supervised domain
adaptation (SDA), knowledge distillation (KD), and deep mutual learning (DML).
However, these methods are merely studied for different urban buildings
(low-rise, mid-rise, high-rise, and skyscrapers), where misalignment increases
with building height and spatial resolution. In this study, we present a
workflow for the systematic comparative study of the three methods. The
workflow first identifies the best (with the highest evaluation scores)
hyperparameters, lightweight CNNs for the Student (among 43 CNNs from Computer
Vision), and encoder-decoder networks (EDNs) for both Teachers and Students.
Secondly, three building footprint datasets are developed to train and evaluate
the identified Teachers and Students in the three transfer methods. The results
show that U-Net with VGG19 (U-VGG19) is the best Teacher, and
U-EfficientNetv2B3 and U-EfficientNet-lite0 are among the best Students. With
these Teacher-Student pairs, SDA could yield upto 0.943, 0.868, 0.912, and
0.697 F1 scores in the low-rise, mid-rise, high-rise, and skyscrapers
respectively. KD and DML provide model compression of upto 82%, despite
marginal loss in performance. This new comparison concludes that SDA is the
most effective method to address the misalignment problem, while KD and DML can
efficiently compress network size without significant loss in performance. The
158 experiments and datasets developed in this study will be valuable to
minimise the misaligned labels.