From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)

2026-04-26

AI Summary

Existing oil palm plantation maps suffer from coarse spatial resolution and limited temporal coverage in recent years, hindering effective monitoring of rapid land-use changes in Southeast Asia. This study proposes a weakly supervised deep learning framework that integrates a U-Net architecture with a Determinant-based Mutual Information (DMI) loss to generate annual 10-meter-resolution oil palm maps for Malaysia and Indonesia from 2020 to 2024, using only historical 100-meter-resolution labels and Sentinel-2 imagery. The approach effectively mitigates label noise and scale mismatch issues, achieving overall accuracies of 70.64%, 63.53%, and 60.06% across three validation years based on 2,058 ground reference points. The resulting time series reveals that oil palm extent peaked in 2022 before declining, with concurrent expansion into flooded vegetation areas.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Semi-Supervised Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate monitoring of oil palm plantations is critical for balancing economic development with environmental conservation in Southeast Asia. However, existing plantation maps often suffer from low spatial resolution and a lack of recent temporal coverage, impeding effective surveillance of rapid land-use changes. In this study, we propose a deep learning framework to generate 10-meter resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024, utilizing Sentinel-2 imagery without requiring new manual annotations. To address the resolution mismatch between coarse 100-meter historical labels and 10-meter imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI). This approach effectively mitigates the influence of label noise. We validated our method against 2,058 manually verified points, achieving overall accuracies of 70.64%, 63.53%, and 60.06% for the years 2020, 2022, and 2024, respectively. Our comprehensive analysis reveals that oil palm coverage in the region peaked in 2022 before experiencing a decline in 2024. Furthermore, land cover transition analysis highlights a concerning trajectory of plantation expansion into flooded vegetation areas, despite a general stabilization in rotations with other crop types. These high-resolution maps provide essential data for monitoring sustainability commitments and deforestation dynamics in the region, and the generated datasets are made publicly available at https://doi.org/10.5281/zenodo.17768444.

Problem

• oil palm mapping
• historical map noise
• temporal coverage
• spatial resolution
• land-use change

Innovation

• deep learning
• label noise
• Determinant-based Mutual Information
• high-resolution mapping
• oil palm plantation