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

• URL: http://arxiv.org/abs/2604.23776v1
• Date: Sun, 26 Apr 2026 15:59:50 GMT
• Title: From Noisy Historical Maps to Time-Series Oil Palm Mapping Without Annotation in Malaysia and Indonesia (2020-2024)
• Abstract summary: We propose a deep learning framework to generate 10-meter resolution oil palm plantation maps for Indonesia and Malaysia from 2020 to 2024.<n>To address the resolution mismatch between 100-meter historical labels and 10-meter imagery, we employ a U-Net architecture optimized with Determinant-based Mutual Information (DMI)<n>Our comprehensive analysis reveals that oil palm coverage in the region peaked in 2022 before experiencing a decline in 2024.
• Score: 13.463323389954844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• From Forest to Future Capital: Tracking Land Cover Change in Ibu Kota Nusantara (IKN) from 2021 to 2026 with PlanetScope Imagery [4.1690310588947765]
  Indonesia's relocation to Ibu Kota Nusantara raises concerns over land conversion, vegetation loss, and carbon stock decline.<n>This study applies remote sensing techniques to assess land use and vegetation cover change in KIPP from 2021 to 2026 using PlanetScope SuperDove satellite imagery.<n>Results show substantial environmental transformation, with mean NDVI declined by 17.1%, total carbon stock decreased by 0.28%, developed land expanded by 672%, and total vegetation declined by 18.1%.
  arXiv  Detail & Related papers  (2026-08-02T13:28:06Z)
• Predicting Pseudo-nitzschia harmful algal blooms along the Portuguese Coast using satellite-derived predictors [69.38367341895632]
  Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and harvesting along the Portuguese Atlantic coast.<n>We develop and evaluate a machine-learning framework to predict harmful algal bloom occurrence using exclusively satellite-derived predictors.
  arXiv  Detail & Related papers  (2026-07-08T18:14:24Z)
• A Dynamic Learning Observatory Reveals the Rapid Salinization of Satkhira, Bangladesh [1.5469452301122175]
  This study develops a machine-learning-based framework to predict and map soil salinity in Satkhira district.<n>A total of 205 soil samples collected during 2024-2025 were used to train an Extreme Gradient Boosting model.<n>Vegetation indices, particularly NDVI, along with salinity-related spectral indicators, were identified as key predictors.
  arXiv  Detail & Related papers  (2026-04-25T03:49:04Z)
• SafeLand: Safe Autonomous Landing in Unknown Environments with Bayesian Semantic Mapping [41.90149506430796]
  We introduce SafeLand, a vision-based system for safe autonomous landing (SAL)<n>Our approach constructs an online semantic ground map via deep learning-based semantic segmentation, optimized for embedded deployment.<n>We extensively evaluate our method in 200 simulations and 60 end-to-end field tests across industrial, urban, and rural environments at altitudes up to 100m.
  arXiv  Detail & Related papers  (2026-03-18T07:11:54Z)
• FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring [40.631100826517375]
  We introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024) nationwide mapping of forest canopy height at 1.5 m resolution.<n>To enable robust change detection across heterogeneous acquisitions, we developed a dedicated post-processing pipeline.<n>In mountainous forests, where disturbances are small and spatially fragmented, FORMSpoT-$$ achieves an F1-score of 0.44, representing an order of magnitude higher than existing benchmarks.
  arXiv  Detail & Related papers  (2025-12-18T19:35:09Z)
• An Open Benchmark Dataset for GeoAI Foundation Models for Oil Palm Mapping in Indonesia [0.0]
  Oil palm cultivation remains one of the leading causes of deforestation in Indonesia.<n>We present an open-access dataset of oil palm plantations and related land cover types in Indonesia, produced through expert labeling of high-resolution satellite imagery from 2020 to 2024.<n>The dataset provides polygon-based, wall-to-wall annotations across a range of agro-ecological zones and includes a hierarchical typology that distinguishes oil palm planting stages as well as similar perennial crops.
  arXiv  Detail & Related papers  (2025-09-10T05:59:00Z)
• TerraTrace: Temporal Signature Land Use Mapping System [2.963329014505718]
  We observe that metrics such as the Normalized Difference Vegetation Index (NDVI), based on plant photosynthesis, have unique temporal signatures that reflect agricultural practices and seasonal cycles.<n>We develop a novel longitudinal NDVI dataset for the state of California from 2020-2023 with 500m resolution and over 70 million points.<n>We use this to develop the TerraTrace platform, an end-to-end analytic tool that classifies land use using NDVI signatures.
  arXiv  Detail & Related papers  (2025-02-25T23:54:45Z)
• Detection and Geographic Localization of Natural Objects in the Wild: A Case Study on Palms [10.342516627774438]
  We develop PRISM (Processing, Inference, and Mapping), a flexible pipeline for detecting and localizing palms in dense tropical forests using large orthomosaic images.<n>Our contributions are threefold. First, we construct a large UAV-derived orthomosaic dataset collected across 21 ecologically diverse sites in western Ecuador, annotated with 8,830 bounding boxes and 5,026 palm center points.<n>Second, we evaluate multiple state-of-the-art object detectors based on efficiency and performance, integrating zero-shot SAM 2 as the segmentation backbone. Third, we apply calibration methods to align confidence scores with IoU and explore s
  arXiv  Detail & Related papers  (2025-02-18T16:43:11Z)
• Automating global landslide detection with heterogeneous ensemble deep-learning classification [44.99833362998488]
  Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. Deep learning models have recently been applied for landside mapping using medium- to high-resolution satellite images as input.
  arXiv  Detail & Related papers  (2023-09-12T10:56:16Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.