Mapping and Classification of Trees Outside Forests using Deep Learning

TL;DR

This study evaluates deep learning models for classifying Trees Outside Forests using high-resolution aerial imagery across multiple landscapes, highlighting the importance of spatial context and diverse training data for accurate ecological mapping.

Contribution

It introduces a new dataset and comprehensive comparison of deep learning architectures for TOF classification, emphasizing the effectiveness of hybrid models like FT-UNetFormer.

Findings

FT-UNetFormer achieved the highest accuracy (IoU 0.74, F1 0.84)

Models performed well on Forest and Linear classes

Challenges remain in classifying complex structures like Patch and Tree

Abstract

Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microclimates. Yet, most studies have treated TOF as a single class or relied on rigid rule-based thresholds, limiting ecological interpretation and adaptability across regions. To address this, we evaluate deep learning for TOF classification using a newly generated dataset and high-resolution aerial imagery from four agricultural landscapes in Germany. Specifically, we compare convolutional neural networks (CNNs), vision transformers, and hybrid CNN-transformer models across six semantic segmentation architectures (ABCNet, LSKNet, FT-UNetFormer, DC-Swin, BANet, and U-Net) to map four categories of woody vegetation: Forest, Patch, Linear, and Tree, derived from previous studies and governmental products. Overall, the models achieved good classification accuracy across the four landscapes, with the FT-UNetFormer performing best (mean Intersection-over-Union 0.74; mean F1 score 0.84), underscoring the importance of spatial context understanding in TOF mapping and classification. Our results show good results for Forest and Linear class and reveal challenges particularly in classifying complex structures with high edge density, notably the Patch and Tree class. Our generalization experiments highlight the need for regionally diverse training data to ensure reliable large-scale mapping. The dataset and code are openly available at https://github.com/Moerizzy/TOFMapper