# U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery

Source: [U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery](<https://www.sota2.com/research/paper/68fa66541e209131cea0f87f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:40.249Z

Authors: Chunxue Wu, Bobo Ju, Naixue Xiong, Guisong Yang, Yan Wu, Hongming Yang, Jiaying Huang, Zhiyong Xu

Published: 2019-09-10T11:13:55.000Z

## Abstract

Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic segmentation, and then uses the integral progressive method to calculate the forestland change rate, and finally realizes automated valuation of woodland change rate.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.04410>)

[PDF](<https://arxiv.org/pdf/1909.04410.pdf>)