Authors:
Georgios-Fotios Angelis
;
Armando Domi
;
Alexandros Zamichos
;
Maria Tsourma
;
Ioannis Manakos
;
Anastasios Drosou
and
Dimitrios Tzovaras
Affiliation:
Information and Technologies Institute, Centre for Research and Technology Hellas, 6km. Harilaou - Thermi, Thessaloniki, Greece
Keyword(s):
Remote Sensing, Transformers, Building, Extraction, Segmentation.
Abstract:
Data visualization has received great attention in the last few years and gives valuable assets for better understanding and extracting information from data. More specifically, in Geospatial data, visualization includes
information about the location, the geometric shape of elements, and the exact position of elements that can
lead in enhances downstream applications such as damage detection, building energy consumption estimation,
urban planning and change detection. Extracting building footprints from remote sensing (RS) imagery can
help in visualizing damaged buildings and separate them form terrestrial objects. Considering this, the current manuscript provides a detailed comparison and a new benchmark for remote sensing building extraction.
Experiments are conducted in three publicly available datasets aiming to evaluate accuracy and performance
of the compared Transformer-based architectures. MiTNet and other five transformers architectures are introduced, namely DeepViTU
Net, DeepViTUNet++, Coordformer, PoolFormer, EfficientFormer. In these choices
we study design adjustments in order to obtain the best trade off between computational cost and performance.
Experimental findings demonstrate that MitNet, which learns features in a hierarchical manner can be established as a new benchmark.
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