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\begin{document}

\title{Improved Automated Building Extraction from High Resolution Remote
Sensing Imagery using Time-optimized Deep Learning Techniques}



\author[1]{Chintan Maniyar}%
\author[1]{Minakshi Kumar}%
\affil[1]{Indian Institute of Remote Sensing}%


\vspace{-1em}



  
  \date{June 03, 2024}


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\begin{abstract}
Deep learning techniques are being increasingly used in earth science
applications - from climate change modelling to feature extraction from
remote sensing imagery, given their advantage of increased contextual
and hierarchical feature representation. However, deep learning comes at
an expense of extensive computational resources and long training time
to achieve benchmark results. This study suggests time-optimized deep
learning techniques for training deep convolutional networks for one of
the most sought after feature extraction subsets -- building extraction
from satellite/aerial imagery. Building extraction is one of the most
important tasks in the dynamic pipeline of urban applications such as
urban planning and management, disaster management, urban mapping etc.
among other geospatial applications. Automatically extracting buildings
from remotely sensed imagery has always been a challenging task, given
the spectral homogeneity of buildings with the non-building features as
well as the complex structural diversity within the image. With the
availability of high resolution open-source satellite and UAV data, deep
learning techniques have greatly improved building extraction. However,
training on such high resolution data requires the networks to be
significantly deeper, resulting in long model training and inference
times. This study proposes a combination of two time efficient methods
to train a Dynamic Res-U-Net for building extraction in less time
without decreasing the training parameters: 1) Using Cyclical Learning
and SuperConvergence concepts by dynamically changing the learning rate
while training the network to achieve very high accuracy in very less
time and 2) Using a specific order to train the layers of the network(s)
to specially have the last layers of the networks perform better,
leading to an overall improved network performance in lesser time.
Building extraction results are gauged using the metrics of Accuracy,
Dice Score and Intersection over Union (IoU) and F1-Score. The metrics
comparison of training the Res-U-Net in the conventional way vs the
proposed techniques shows an evident optimisation in terms of time.
Better results are achieved in lesser training epochs using the proposed
time-optimised training techniques.%
\end{abstract}%



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