Enhancing USDA NASS Cropland Data Layer with Segment Anything Model
Author:
Affiliation:
1. George Mason University,Center for Spatial Information Science and Systems,Fairfax,VA,USA,22030
2. U.S. Department of Agriculture National Agricultural Statistics Service,Washington,DC,USA,20250
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10233256/10233125/10233404.pdf?arnumber=10233404
Reference28 articles.
1. Segment anything;kirillov;arXiv preprint arXiv 2304 02643,2023
2. Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data
3. The segment anything model (sam) for remote sensing applications: From zero to one shot;osco;arXiv preprint arXiv 2306 16623,2023
4. Deep learning universal crater detection using segment anything model (sam);giannakis;arXiv preprint arXiv 2304 07764,2023
5. Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer
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4. Deep learning in cropland field identification: A review;Computers and Electronics in Agriculture;2024-07
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