Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine

Aninda Wisaksanti Rudiastuti; Nur Mohammad Farda; Dadan Ramdani

doi:10.1117/12.2619493

22 December 2021 Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine

Aninda Wisaksanti Rudiastuti, Nur Mohammad Farda, Dadan Ramdani

Author Affiliations +

Proceedings Volume 12082, Seventh Geoinformation Science Symposium 2021; 1208206 (2021) https://doi.org/10.1117/12.2619493
Event: Seventh Geoinformation Science Symposium (GSS 2021), 2021, Yogyakarta, Indonesia

Abstract

Monitoring the growth of built-up land finds its challenge as a never-ending mapping process. Since the built-up maps are taken into account in development planning and measuring the achievement of SDGs objectives (Goal 11 - indicator 11.3.1), the best mapping method should be attempted. Therefore, all efforts were worked on to speed up the mapping process without compromising accuracy. Various methods have been proposed, but numerous difficulties remain in accurate and efficient built-up and settlement extraction. With the combination of passive and active sensors images, we try several machine learning methods using Google Earth Engine (GEE) platforms to map built-up land and settlements in Purwokerto, Banyumas, Central Java. Around 369 samples were occupied to distinguish four classes of land covers: settlements, built-up land, waters, and others. The decision tree-based algorithms give the best performances, scilicet Random Forest (RF) and Gradient Tree Boost (GTB). Random forest is a collection of many decision trees, while Gradient Boosting is a machine learning algorithm that uses an ensemble of decision trees to predict values. Thus, the algorithms can handle complex patterns and data when linear models cannot. On the whole, RF and GTB classifiers can distinguish between settlements and non-settlement with an overall accuracy of 80%. The Support Vector Machine (SVM) classifier produces 71.43% accuracy with Kappa = 0.61, and the Minimum Distance (Mahalanobis) classifier gain overall accuracy of 74.29% (Kappa = 0.64).

Citation Download Citation

Aninda Wisaksanti Rudiastuti, Nur Mohammad Farda, and Dadan Ramdani "Mapping built-up land and settlements: a comparison of machine learning algorithms in Google Earth engine", Proc. SPIE 12082, Seventh Geoinformation Science Symposium 2021, 1208206 (22 December 2021); https://doi.org/10.1117/12.2619493

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Machine learning

Buildings

Mahalanobis distance

Detection and tracking algorithms

Synthetic aperture radar

Vegetation

Associative arrays

Show All Keywords

Keywords/Phrases

Search In:

Publication Years