Dual-Task Learning for Dead Tree Detection and Segmentation with Hybrid Self-Attention U-Nets in Aerial Imagery

• URL: http://arxiv.org/abs/2503.21438v1
• Date: Thu, 27 Mar 2025 12:25:20 GMT
• Title: Dual-Task Learning for Dead Tree Detection and Segmentation with Hybrid Self-Attention U-Nets in Aerial Imagery
• Authors: Anis Ur Rahman, Einari Heinaro, Mete Ahishali, Samuli Junttila,
• Abstract summary: This study introduces a hybrid postprocessing framework that refines deep learning-based tree segmentation.<n>Tested on high-resolution aerial imagery from boreal forests, the framework improved instance-level segmentation accuracy by 41.5%.<n>The framework's computational efficiency supports scalable applications, such as wall-to-wall tree mortality mapping.
• Score: 1.693687279684153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mapping standing dead trees is critical for assessing forest health, monitoring biodiversity, and mitigating wildfire risks, for which aerial imagery has proven useful. However, dense canopy structures, spectral overlaps between living and dead vegetation, and over-segmentation errors limit the reliability of existing methods. This study introduces a hybrid postprocessing framework that refines deep learning-based tree segmentation by integrating watershed algorithms with adaptive filtering, enhancing boundary delineation, and reducing false positives in complex forest environments. Tested on high-resolution aerial imagery from boreal forests, the framework improved instance-level segmentation accuracy by 41.5% and reduced positional errors by 57%, demonstrating robust performance in densely vegetated regions. By balancing detection accuracy and over-segmentation artifacts, the method enabled the precise identification of individual dead trees, which is critical for ecological monitoring. The framework's computational efficiency supports scalable applications, such as wall-to-wall tree mortality mapping over large geographic regions using aerial or satellite imagery. These capabilities directly benefit wildfire risk assessment (identifying fuel accumulations), carbon stock estimation (tracking emissions from decaying biomass), and precision forestry (targeting salvage loggings). By bridging advanced remote sensing techniques with practical forest management needs, this work advances tools for large-scale ecological conservation and climate resilience planning.

Related papers

• Multimodal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds [47.679877727066206]
  Assessment of forest biodiversity is crucial for ecosystem management and conservation. Deep learning-based fusion of close-range sensing data from 2D orthophotos and 3D airborne laser scanning (ALS) point clouds can reliable assess the biodiversity potential of forests. BioVista dataset comprises 44 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark.
  arXiv  Detail & Related papers  (2025-01-03T09:42:25Z)
• Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation [78.54656076915565]
  Topological correctness plays a critical role in many image segmentation tasks. Most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. We propose a novel, graph-based framework for topologically accurate image segmentation.
  arXiv  Detail & Related papers  (2024-11-05T16:20:14Z)
• Enhancing Tree Type Detection in Forest Fire Risk Assessment: Multi-Stage Approach and Color Encoding with Forest Fire Risk Evaluation Framework for UAV Imagery [0.0]
  Forest fires pose a significant threat to ecosystems, economies, and human health worldwide. Unmanned Aerial Vehicles equipped with advanced computer vision algorithms offer a promising solution for forest fire detection and assessment. We optimize an integrated forest fire risk assessment framework using UAVs and multi-stage object detection algorithms.
  arXiv  Detail & Related papers  (2024-07-27T05:52:31Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar [14.07306593230776]
  This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020. We also introduce a post-processing step using a convolutional network trained on GEDI observations.
  arXiv  Detail & Related papers  (2023-04-14T15:52:57Z)
• Classification of Single Tree Decay Stages from Combined Airborne LiDAR Data and CIR Imagery [1.4589991363650008]
  This study, for the first time, automatically categorizing individual trees (Norway spruce) into five decay stages. Three different Machine Learning methods - 3D point cloud-based deep learning (KPConv), Convolutional Neural Network (CNN), and Random Forest (RF) All models achieved promising results, reaching overall accuracy (OA) of up to 88.8%, 88.4% and 85.9% for KPConv, CNN and RF, respectively.
  arXiv  Detail & Related papers  (2023-01-04T22:20:16Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery [0.5276232626689566]
  Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework.
  arXiv  Detail & Related papers  (2021-12-06T00:53:51Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• Deep Learning based Automated Forest Health Diagnosis from Aerial Images [0.0]
  Aerial image-based forest analysis can give an early detection of dead trees and living trees. We present a new framework for automated dead tree detection from aerial images using a re-trained Mask RCNN approach. We are able to automatically produce and calculate number of dead tree masks to label the dead trees in an image.
  arXiv  Detail & Related papers  (2020-10-16T15:07:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.