# Self-Supervised Learning and Multi-Sensor Fusion for Alpine Wetland Vegetation Mapping: Bayinbuluke, China

## TL;DR

This paper introduces a new method using self-supervised learning and multi-sensor data to map wetland vegetation and detect invasive species in Bayinbuluke, China.

## Contribution

The novel framework combines self-supervised learning with multi-sensor fusion to improve vegetation classification and invasive species detection.

## Key findings

- SSL methods like BYOL, DINO, and MoCo v3 improve feature learning without extensive labeled data.
- Multi-sensor fusion enhances detection of rare and fragmented vegetation patches.
- The framework enables early identification of invasive species and reduces reliance on field data.

## Abstract

Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management.