Enhancing Land Cover Classification Accuracy in Cloud-Prone Tropical Regions Using Majority Filtering and Google Earth Engine Yastika, P.E. 1*, Sudipa I.N.1, Gunantara I.M.O.2 and Karmadi K.A.3
1 Regional and Rural Planning, Universitas Mahasaraswati Denpasar, Denpasar, Indonesia
2. Environmental Sciences, Udayana University, Denpasar Indonesia
3. Environmental Engineering, Universitas Mahasaraswati Denpasar, Denpasar, Indonesia
Abstract
As a region develops, its land use patterns become increasingly dynamic. To support sustainable development and minimize conflicts, accurate and timely land use data is essential, particularly in the form of regional-scale land cover maps. Satellite imagery is commonly used for this purpose due to its capability to efficiently cover large areas. However, in tropical regions, cloud cover often interferes with the quality of optical imagery. This study proposes a method called majority filtering to enhance land cover classification accuracy. A total of 831 Sentinel-2 images from 2019 to 2024, covering the Badung-Denpasar region in Bali, were processed using Google Earth Engine cloud computing. An initial classification using the Random Forest algorithm was conducted in a time-series framework. The application of a majority filter improved the overall classification accuracy to 85%, particularly in areas frequently affected by cloud-related distortions. Additionally, the filter helped to smooth class boundaries and reduce classification noise, resulting in more coherent and reliable mapping outputs. By addressing the limitations of optical imagery in cloudy regions, this research offers a simple yet effective improvement for land cover mapping that can be applied in various planning and environmental monitoring efforts.
Keywords: Google Earth Engine, Land Cover Classification, Majority Filtering, Random Forest, Sentinel-2
Topic: Topic C: Emerging Technologies in Remote Sensing
