FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding
CoRR(2024)
Abstract
Fine urban change segmentation using multi-temporal remote sensing images is
essential for understanding human-environment interactions. Despite advances in
remote sensing data for urban monitoring, coarse-grained classification systems
and the lack of continuous temporal observations hinder the application of deep
learning to urban change analysis. To address this, we introduce FUSU, a
multi-source, multi-temporal change segmentation dataset for fine-grained urban
semantic understanding. FUSU features the most detailed land use classification
system to date, with 17 classes and 30 billion pixels of annotations. It
includes bi-temporal high-resolution satellite images with 20-50 cm ground
sample distance and monthly optical and radar satellite time series, covering
847 km2 across five urban areas in China. The fine-grained pixel-wise
annotations and high spatial-temporal resolution data provide a robust
foundation for deep learning models to understand urbanization and land use
changes. To fully leverage FUSU, we propose a unified time-series architecture
for both change detection and segmentation and benchmark FUSU on various
methods for several tasks. Dataset and code will be available at:
https://github.com/yuanshuai0914/FUSU.
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