AI-Driven Tree Species Classification & Biodiversity Mapping

Feiyan integrates the Y-1 UAV System, AIMS Multi-sensor Camera, and Swift4D Platform to deliver AI-driven tree species classification and biodiversity mapping. Using high-resolution imagery, multispectral data, and LiDAR with deep learning, it enables individual tree-level species identification and spatial diversity mapping for forest inventory, invasive species monitoring, and precision silviculture.

Our Differentiated Solution Framework

Built on Swift4D, the framework integrates multi-modal acquisition, spectral-structural fusion, AI recognition, and biodiversity mapping:

Multi-Modal Acquisition: Y-1 captures RGB and multispectral data; AIMS adds LiDAR point clouds and hyperspectral data, overcoming ground survey limitations.

Spectral-Structural Fusion: Swift4D fuses multi-temporal imagery and point clouds to extract spectral, textural, and height features, significantly improving diversity estimation accuracy.

SwiftAI Species Identification: CNNs and instance segmentation extract individual tree crowns, integrating spectral and height data for classification, generating single-tree species polygons.

Biodiversity Mapping & Monitoring: Grid-based calculation of richness, Shannon, and Simpson indices; multi-temporal comparison tracks disease spread and vegetation succession.

 Application Scenarios & Client Value

Application Scenarios

Forest inventory and species distribution mapping

Biodiversity baseline surveys for protected areas

Invasive species (e.g., Spartina alterniflora) monitoring and control

Early detection and spread tracking of pine wilt disease

Precision thinning and carbon accounting

Client Value

Efficiency: AI replaces manual work, reducing field time by 80% and compressing cycles from months to days.

Accuracy: Individual tree-level classification provides high-precision data for conservation and restoration.

Early Warning: High-frequency patrols combined with change detection narrow the risk window for outbreaks.

Sustainability: Long-term monitoring supports forest health assessment and carbon accounting.