2024年4月5日 · The Crop Sequence Boundaries (CSB) developed with USDA's Economic Research Service, produces estimates of field boundaries, crop acreage, and crop rotations across the contiguous United States. It uses satellite imagery with other public data and is open source allowing users to conduct area and statistical analysis of planted U.S. commodities ...

This study proposed a sample-free method for delineating crop field boundaries from free Sentinel-2 (S2) or low-cost PlanetScope (PS) images by integrating Super-resolution Image reconstruction and Dual Edge-corrected Segment anyThing model (SIDEST).

2024年6月27日 · Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy. 摘要： 地块级的农田图谱是作物产量估算、精确农业和许多其他农学应用的重要数据来源。在此，我们提出了一种将农田边界提取与精细分辨率卫星图像和 ...

2022年9月28日 · This study proposed a crop mapping framework that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise crop detection with time series SAR imagery. We solved the prediction errors and conflicts near the border of the patches with a smooth blending strategy from multiple weighted predictions.

Leveraging Segment-Anything Model (SAM) to delineate crop field boundaries on Sentinel-2 images Topics

2023年1月18日 · There are three broad methods to map field boundaries: deep learning, object-based image segmentation, and conventional (edge-detection) filters (Waldner and Diakogiannis, 2020). Deep learning methods can extract field boundaries from satellite/aerial images better than object-based image analysis (e.g. multiresolution segmentation) or ...

Utilizing advanced machine learning and computer vision algorithms, Map My Crop can automatically identify and map the boundaries of farms or crop fields from remote sensing data, including satellite images and aerial photographs. Accurately delineating land boundaries is essential for effective land-use identification and management planning.

2021年12月25日 · The main contributions of this paper are three-fold: (a) proposed a robust agricultural field delineation strategy, an automated technique based on a deep recurrent residual U-Net and boundary connection method, to delineate field boundaries; (b) developed a fast training-dataset generation strategy to meet the requirement of labeling a very ...

2023年6月5日 · We propose a Semantic Feature Pyramid Network (FPN)-based algorithm to derive agricultural field boundaries and internal non-planting regions from satellite imagery. It is aimed at providing guidance not only for land use management, but more importantly for harvest or crop protection machinery planning.

Large-scale digitalization of crop field boundaries provides vital information for smart agriculture applications. Due to the high cost of high-resolution remote sensing images and the time-consuming manual annotation of data, effective and budget-friendly solutions for extracting closed agricultural field boundaries remain scarce.