AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data

Xun Thong Cham; Ming Le Soh; Fedra Trujillano; Chun Yu Yau Peter; Chen Fung Choy Oliver; Xavern Cheh; Kimberly Fornace; Yi Jie Poh Nicholas; Chee Kiat Seow; Henrik Hesse; Qi Cao; Gabriel Jimenez Garay

doi:10.1109/WF-IoT58464.2023.10539446

AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data

Xun Thong Cham
, Ming Le Soh
, Fedra Trujillano
, Chun Yu Yau Peter
, Chen Fung Choy Oliver
, Xavern Cheh
, Kimberly Fornace
, Yi Jie Poh Nicholas
, Chee Kiat Seow
, Henrik Hesse
, Qi Cao
, Gabriel Jimenez Garay

Sección de Electricidad y Electrónica

University of Glasgow

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In recent years, the need for efficient geospatial image editing and analysis tools has increased significantly due to the rapid growth of satellite and aerial imagery data. An AI-Assisted Manual Segmentation Web Application, a ReactJs web application was designed for editing GeoTiffs and generating masks using advanced algorithms such as K-means and U-Net. The application offers a comprehensive suite of editing tools, including pencil, eraser, undo, redo, zoom, magic wand, import mask, export mask, and layers management. These features allow users to manipulate geospatial imagery data effectively and intuitively. Furthermore, our application employs a Flask server to facilitate the generation of K-means or U-Net masks, depending on the user's choice. Upon pressing the corresponding button, the server processes the image and sends the generated mask back to the ReactJs web application. The application seamlessly integrates the generated mask as a new layer, enabling users to further refine their edits and analysis. By combining advanced AI algorithms with a user-friendly interface, our AI-Assisted Manual Segmentation Web Application aims to achieve the goal of improving the current methodology of analysing aerial imagery.

Original language	English
Title of host publication	2023 IEEE World Forum on Internet of Things
Subtitle of host publication	The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350311617
DOIs	https://doi.org/10.1109/WF-IoT58464.2023.10539446
State	Published - 2023
Event	9th IEEE World Forum on Internet of Things, WF-IoT 2023 - Hybrid, Aveiro, Portugal Duration: 12 Oct 2023 → 27 Oct 2023

Publication series

Name	2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023

Conference

Conference	9th IEEE World Forum on Internet of Things, WF-IoT 2023
Country/Territory	Portugal
City	Hybrid, Aveiro
Period	12/10/23 → 27/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CNN
Drones
K-means
Malaria
Satellite Imagery
U-Net

Access to Document

10.1109/WF-IoT58464.2023.10539446

Cite this

Cham, X. T., Soh, M. L., Trujillano, F., Yau Peter, C. Y., Choy Oliver, C. F., Cheh, X., Fornace, K., Poh Nicholas, Y. J., Seow, C. K., Hesse, H., Cao, Q., & Garay, G. J. (2023). AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data. In 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023 (2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WF-IoT58464.2023.10539446

Cham, Xun Thong ; Soh, Ming Le ; Trujillano, Fedra et al. / AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data. 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023).

@inproceedings{3bc133012e9844fda46aba319afeecb5,

title = "AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data",

abstract = "In recent years, the need for efficient geospatial image editing and analysis tools has increased significantly due to the rapid growth of satellite and aerial imagery data. An AI-Assisted Manual Segmentation Web Application, a ReactJs web application was designed for editing GeoTiffs and generating masks using advanced algorithms such as K-means and U-Net. The application offers a comprehensive suite of editing tools, including pencil, eraser, undo, redo, zoom, magic wand, import mask, export mask, and layers management. These features allow users to manipulate geospatial imagery data effectively and intuitively. Furthermore, our application employs a Flask server to facilitate the generation of K-means or U-Net masks, depending on the user's choice. Upon pressing the corresponding button, the server processes the image and sends the generated mask back to the ReactJs web application. The application seamlessly integrates the generated mask as a new layer, enabling users to further refine their edits and analysis. By combining advanced AI algorithms with a user-friendly interface, our AI-Assisted Manual Segmentation Web Application aims to achieve the goal of improving the current methodology of analysing aerial imagery.",

keywords = "CNN, Drones, K-means, Malaria, Satellite Imagery, U-Net",

author = "Cham, \{Xun Thong\} and Soh, \{Ming Le\} and Fedra Trujillano and \{Yau Peter\}, \{Chun Yu\} and \{Choy Oliver\}, \{Chen Fung\} and Xavern Cheh and Kimberly Fornace and \{Poh Nicholas\}, \{Yi Jie\} and Seow, \{Chee Kiat\} and Henrik Hesse and Qi Cao and Garay, \{Gabriel Jimenez\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 9th IEEE World Forum on Internet of Things, WF-IoT 2023 ; Conference date: 12-10-2023 Through 27-10-2023",

year = "2023",

doi = "10.1109/WF-IoT58464.2023.10539446",

language = "English",

series = "2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 IEEE World Forum on Internet of Things",

}

Cham, XT, Soh, ML, Trujillano, F, Yau Peter, CY, Choy Oliver, CF, Cheh, X, Fornace, K, Poh Nicholas, YJ, Seow, CK, Hesse, H, Cao, Q & Garay, GJ 2023, AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data. in 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023, Institute of Electrical and Electronics Engineers Inc., 9th IEEE World Forum on Internet of Things, WF-IoT 2023, Hybrid, Aveiro, Portugal, 12/10/23. https://doi.org/10.1109/WF-IoT58464.2023.10539446

AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data. / Cham, Xun Thong; Soh, Ming Le; Trujillano, Fedra et al.
2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data

AU - Cham, Xun Thong

AU - Soh, Ming Le

AU - Trujillano, Fedra

AU - Yau Peter, Chun Yu

AU - Choy Oliver, Chen Fung

AU - Cheh, Xavern

AU - Fornace, Kimberly

AU - Poh Nicholas, Yi Jie

AU - Seow, Chee Kiat

AU - Hesse, Henrik

AU - Cao, Qi

AU - Garay, Gabriel Jimenez

PY - 2023

Y1 - 2023

N2 - In recent years, the need for efficient geospatial image editing and analysis tools has increased significantly due to the rapid growth of satellite and aerial imagery data. An AI-Assisted Manual Segmentation Web Application, a ReactJs web application was designed for editing GeoTiffs and generating masks using advanced algorithms such as K-means and U-Net. The application offers a comprehensive suite of editing tools, including pencil, eraser, undo, redo, zoom, magic wand, import mask, export mask, and layers management. These features allow users to manipulate geospatial imagery data effectively and intuitively. Furthermore, our application employs a Flask server to facilitate the generation of K-means or U-Net masks, depending on the user's choice. Upon pressing the corresponding button, the server processes the image and sends the generated mask back to the ReactJs web application. The application seamlessly integrates the generated mask as a new layer, enabling users to further refine their edits and analysis. By combining advanced AI algorithms with a user-friendly interface, our AI-Assisted Manual Segmentation Web Application aims to achieve the goal of improving the current methodology of analysing aerial imagery.

AB - In recent years, the need for efficient geospatial image editing and analysis tools has increased significantly due to the rapid growth of satellite and aerial imagery data. An AI-Assisted Manual Segmentation Web Application, a ReactJs web application was designed for editing GeoTiffs and generating masks using advanced algorithms such as K-means and U-Net. The application offers a comprehensive suite of editing tools, including pencil, eraser, undo, redo, zoom, magic wand, import mask, export mask, and layers management. These features allow users to manipulate geospatial imagery data effectively and intuitively. Furthermore, our application employs a Flask server to facilitate the generation of K-means or U-Net masks, depending on the user's choice. Upon pressing the corresponding button, the server processes the image and sends the generated mask back to the ReactJs web application. The application seamlessly integrates the generated mask as a new layer, enabling users to further refine their edits and analysis. By combining advanced AI algorithms with a user-friendly interface, our AI-Assisted Manual Segmentation Web Application aims to achieve the goal of improving the current methodology of analysing aerial imagery.

KW - CNN

KW - Drones

KW - K-means

KW - Malaria

KW - Satellite Imagery

KW - U-Net

UR - https://www.scopus.com/pages/publications/85195392432

U2 - 10.1109/WF-IoT58464.2023.10539446

DO - 10.1109/WF-IoT58464.2023.10539446

M3 - Conference contribution

AN - SCOPUS:85195392432

T3 - 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023

BT - 2023 IEEE World Forum on Internet of Things

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th IEEE World Forum on Internet of Things, WF-IoT 2023

Y2 - 12 October 2023 through 27 October 2023

ER -

Cham XT, Soh ML, Trujillano F, Yau Peter CY, Choy Oliver CF, Cheh X et al. AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data. In 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023). doi: 10.1109/WF-IoT58464.2023.10539446

AI-Assisted Manual Segmentation Web Application for Geospatial Satellite and Imagery Data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this