Method and system for crop mapping across large regions with low sample dependence
US12423970B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 14, 2025 |
| Grant date | Sep 23, 2025 |
| Priority date | — |
| Expiry date | Jan 14, 2045 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/776
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention belongs to the technical field of crop mapping based on remote-sensing images, and relates to a method and system for crop mapping across large regions with low sample dependence. The method includes: acquiring remote sensing data, ground sample data, meteorological data, soil data, establishing geographically divided crop planting regions; establishing key growth period model libraries corresponding to individual crop regions; constructing machine learning models based on a plurality of machine learning algorithms, to obtain machine learning crop extraction models; selecting an optimal machine learning crop extraction model; acquiring a spatial crop distribution base map; performing product correction based on the disaster information; and acquiring a regional crop map using a target crop extraction model adapted for the disaster response. The present invention, achieve high-accuracy and large-scale crop mapping, and reduce the crop sample dependence of crop mapping.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.