A method for rapid collection of agricultural land status data

Abstract

The invention discloses a method for quickly collecting data on the current situation of agricultural land, and relates to the field of geographic information. The invention designs the technology and system for collecting current data on agricultural land by integrating the Internet geographic information system and remote sensing technology, and establishes remote sensing interpretation signs of agricultural land. On the basis of the Internet-based remote sensing image interactive interpretation, and the collection, update and checking of agricultural land status data, it is realized based on the database and sample database. Compared with the traditional field mapping method for data collection and automatic interpretation of remote sensing images, the technical method and system of the present invention have the characteristics of fast, efficient and accurate, short data collection cycle, low cost, and easy maintenance.

Description

technical field [0001] The invention relates to the field of geographic information, in particular to a method for quickly collecting agricultural land current data. Background technique [0002] At present, the current data collection methods of agricultural land status mainly include field mapping and visual interpretation based on remote sensing images. At present, the technology and application of land resources investigation based on remote sensing images are becoming more and more mature. my country's land and resources department has used high-resolution satellite images for land and resources law enforcement monitoring (Yi, Ma et al. 2016). This technology is based on image change detection technology, assists manual interactive judgment, and realizes law enforcement inspection of illegal construction land. [0003] Some studies have adopted automatic interpretation based on remote sensing data, using methods such as object-based and support vector machines (Pena, G...

AI Technical Summary

Problems solved by technology

An important reason is that the visual interpretation uses a stand-alone version of professional remote sensing software, which is inefficient and cannot effectively integrate multi-source data to support accurate interpretation of land use types

When dealing with the interpretation of large-area complex land types, the automatic interpretation technology of remote sensing images cannot meet the requirements of finer classification and higher data collection accuracy (Srivastava, Han et al.2012, Mulla 2013)

For example, it is not currently possible to identify detailed land use classifications such as grain fields, vegetable fields, orchards, and to collect and update data on farmland plots whose accuracy is not greater than 1 mu.

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