People Detection Using Artificial Intelligence with Panchromatic Satellite Images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255675" target="_blank" >RIV/61989100:27240/24:10255675 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27350/24:10255675

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/14/18/8555" target="_blank" >https://www.mdpi.com/2076-3417/14/18/8555</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app14188555" target="_blank" >10.3390/app14188555</a>

Jazyk výsledku

angličtina

Název v původním jazyce

People Detection Using Artificial Intelligence with Panchromatic Satellite Images

Popis výsledku v původním jazyce

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R-CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm. (C) 2024 by the authors.

Název v anglickém jazyce

People Detection Using Artificial Intelligence with Panchromatic Satellite Images

Popis výsledku anglicky

The detection of people in urban environments from satellite imagery can be employed in a variety of applications, such as urban planning, business management, crisis management, military operations, and security. A WorldView-3 satellite image of Prague was processed. Several variants of feature-extracting networks, referred to as backbone networks, were tested alongside the Faster R-CNN model. This model combines region proposal networks with object detection, offering a balance between speed and accuracy that is well suited for dense and varied urban environments. Data augmentation was used to increase the robustness of the models, which contributed to the improvement of classification results. Achieving a high level of accuracy is an ongoing challenge due to the low spatial resolution of available imagery. An F1 score of 54% was achieved using data augmentation, a 15 cm buffer, and a maximum distance limit of 60 cm. (C) 2024 by the authors.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10500 - Earth and related environmental sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

18

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

001323310900001

EID výsledku v databázi Scopus

2-s2.0-85205293744