T

Tcd Segformer Mit B3

Developed by restor
This is a semantic segmentation model capable of delineating tree cover from high-resolution (10 cm/pixel) aerial images.
Downloads 49
Release Time : 5/20/2024

Model Overview

Trained on global aerial imagery, this model accurately delineates tree cover in similar images, providing pixel-level tree/non-tree classification.

Model Features

High-Resolution Aerial Image Processing
Optimized for 10 cm/pixel resolution aerial images, accurately identifying tree canopy coverage.
Global Diversity Training
Trained on globally diverse aerial imagery, adaptable to different ecological communities.
Pixel-Level Classification
Provides pixel-level tree/non-tree classification, not single-tree detection.
Efficient Inference
Supports efficient inference on single GPU or CPU, suitable for field use.

Model Capabilities

Aerial Image Analysis
Tree Canopy Coverage Detection
Pixel-Level Semantic Segmentation
Ecological Assessment

Use Cases

Ecological Research
Tree Canopy Coverage Assessment
Evaluates the percentage of a study area covered by tree canopy.
Provides accurate coverage percentage data
Environmental Monitoring
Forest Change Monitoring
Monitors changes in forest cover over time.
Generates time-series change maps
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