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Data · dataset · 2026

S3CD: SAR-Optical Sea-Land-Shore Scene Classification Dataset

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S3CD (SAR-Optical Sea-Land-Shore Scene Classification Dataset) is a multimodal remote sensing dataset constructed for coastal zone scene recognition.

Description

The study areas encompass the coastal region at the Hebei-Tianjin border (characterized by flat mudflats and intensive human activity) and the Fujian coastal area (dominated by rocky shores with complex geomorphology). The two regions differ significantly in landform type, coastline morphology, and intensity of human activity, supporting evaluation of model generalization and robustness.The dataset uses Sentinel-1 IW GRD data and Sentinel-2 Level-1C data acquired in October 2024.

Sentinel-1 data includes VV and VH polarizations at 10-meter spatial resolution, preprocessed through orbit correction, thermal noise removal, radiometric calibration, terrain correction, and decibel conversion. Sentinel-2 data uses bands B4 (red), B3 (green), and B2 (blue) to compose true-color images. Image pairs were selected with an acquisition time difference within 3 days, geometrically co-registered, and uniformly cropped into 256×256 pixel patches to ensure spatial consistency.The dataset contains 8,649 precisely labeled images across three categories: Sea, Land, and Coastal.

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Annotation was conducted using Sentinel-2 optical imagery as the primary reference under cloud-free conditions, and Sentinel-1 SAR backscattering features combined with temporally adjacent cloud-free optical images under cloudy conditions. Labeling criteria are as follows: artificial structures on land (buildings, roads, docks, etc.) are classified as Land; facilities in water bodies (aquaculture rafts, net cages, etc.) are classified as Sea; image patches containing both land and water pixels are labeled as Coastal, otherwise as a single category.

The dataset is split into training (6,919 images), validation (865 images), and test (865 images) sets at an 8:1:1 ratio, with 961, 5,359, and 2,329 images for the Coastal, Land, and Sea categories, respectively.

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Inferred from text
Geomatic engineering 69% · Image 75% · Satellite remote sensing 65%
Provenance · 1 source records, 14 field assertions
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