[論文レビュー] LandCoverNet: A global benchmark land cover classification training dataset

Hamed Alemohammad, Kevin T. Booth|arXiv (Cornell University)|Dec 5, 2020

Remote-Sensing Image Classification被引用数 35

ひとこと要約

LandCoverNet は、三名のアノテータの合意と補助的な時系列モデルによって生成されたピクセルレベルのラベルを持つ、グローバルに代表性のある 10m Sentinel-2 ベースのランドカバー訓練データセットをオープンアクセスで提供します。

ABSTRACT

Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse training dataset. Here, we present LandCoverNet, a global training dataset for land cover classification based on Sentinel-2 observations at 10m spatial resolution. Land cover class labels are defined based on annual time-series of Sentinel-2, and verified by consensus among three human annotators.

研究の動機と目的

• Define a globally representative land cover taxonomy via expert consensus for Sentinel-2 based classification.
• Create a large-scale, open training dataset with time-series labeled pixels to support global LC mapping.
• Implement a consensus labeling workflow to reduce human error at 10 m resolution.
• Provide a sampling scheme and data specification to enable reproducible benchmarking.

提案手法

• Define a hierarchical land cover taxonomy through an expert workshop.
• Sample Sentinel-2 tiles (300 total) across continents using MODIS-based class distributions as sampling features.
• Extract 256x256 chips (30 chips per selected tile; ~9000 chips globally; ~589 million pixels).
• Use a time-series based label generation approach with a Random Forest guess label to aid annotators.
• Collect per-pixel labels from three annotators per chip and compute a Bayesian consensus score to determine the final label.

実験結果

リサーチクエスチョン

• RQ1 How to construct a globally representative, open-access land cover training dataset for 10 m Sentinel-2 imagery?
• RQ2 What sampling strategy ensures continental diversity and class balance for CHIPS in a global LC benchmark?
• RQ3 How can consensus labeling and annotator reliability be used to produce high-quality pixel-level LC labels at 10 m resolution?

主な発見

• LandCoverNet v1.0 covers Africa with 1980 chips (256x256) and excludes permanent snow/ice typical for Africa.
• Consensus scores are generally high; 60% of pixels have a consensus score of 100%.
• The per-pixel consensus score can be used to adjust model training confidence for high- vs. lower-score pixels.
• A time-series based labeling approach using 24 Sentinel-2 scenes per tile and a simple Random Forest guess label facilitated annotator labeling.
• Labels are released under CC BY 4.0 via Radiant MLHub; dataset emphasizes global diversity and open access.