Decoding (urban) form and function using spatially explicit deep learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10483562" target="_blank" >RIV/00216208:11310/24:10483562 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6T_LVou94i" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=6T_LVou94i</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compenvurbsys.2024.102147" target="_blank" >10.1016/j.compenvurbsys.2024.102147</a>

Result language

angličtina

Original language name

Decoding (urban) form and function using spatially explicit deep learning

Original language description

This paper explores how can geographical dimension be incorporated into deep learning designed to understand the composition of urban landscapes based on Sentinel 2 satellite imagery. Compared to standard computer vision, satellite imagery is unique as images sampled from the data form a continuous array, rather than being fully independent. We argue that the spatial configuration of the images is as important as the content of each image when attempting to capture a pattern that reflects the structure of the urban environment. We propose a series of approaches explicitly incorporating spatial dimension in the predictive pipeline based on the EfficientNetB4 convolutional neural network (CNN) and experimentally test their effect on model performance. The experiments in this study cover the scale of the sampled area, the effect of spatial augmentation, and the role of modelling (logit ensemble and histogram-based gradient-boosted classifiers) with and without the spatial context on the outputs of the neural network-generated vector of probabilities while trying to predict spatial signatures, a classification of primarily urban landscape based on form and function. The results suggest that certain ways of embedding spatial information, especially in the modelling step, consistently significantly improve the prediction accuracy and shall be considered on top of standard CNNs.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50701 - Cultural and economic geography

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers, Environment and Urban Systems

ISSN

0198-9715

e-ISSN

1873-7587

Volume of the periodical

112

Issue of the periodical within the volume

September

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

102147

UT code for WoS article

001271704800001

EID of the result in the Scopus database

2-s2.0-85198291248