Zero-Shot Tree Detection and Segmentation from Aerial Forest Imagery

• URL: http://arxiv.org/abs/2506.03114v1
• Date: Tue, 03 Jun 2025 17:44:43 GMT
• Title: Zero-Shot Tree Detection and Segmentation from Aerial Forest Imagery
• Authors: Michelle Chen, David Russell, Amritha Pallavoor, Derek Young, Jane Wu,
• Abstract summary: Current RGB tree segmentation methods rely on training specialized machine learning models with labeled tree datasets.<n>In this paper, we investigate the efficacy of using a state-of-the-art image segmentation model, Segment Anything Model 2 (SAM2) in a zero-shot manner for individual tree detection and segmentation.<n>Our results suggest that SAM2 not only has impressive generalization capabilities, but also can form a natural synergy with specialized methods trained on in-domain labeled data.
• Score: 1.2770132985501168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale delineation of individual trees from remote sensing imagery is crucial to the advancement of ecological research, particularly as climate change and other environmental factors rapidly transform forest landscapes across the world. Current RGB tree segmentation methods rely on training specialized machine learning models with labeled tree datasets. While these learning-based approaches can outperform manual data collection when accurate, the existing models still depend on training data that's hard to scale. In this paper, we investigate the efficacy of using a state-of-the-art image segmentation model, Segment Anything Model 2 (SAM2), in a zero-shot manner for individual tree detection and segmentation. We evaluate a pretrained SAM2 model on two tasks in this domain: (1) zero-shot segmentation and (2) zero-shot transfer by using predictions from an existing tree detection model as prompts. Our results suggest that SAM2 not only has impressive generalization capabilities, but also can form a natural synergy with specialized methods trained on in-domain labeled data. We find that applying large pretrained models to problems in remote sensing is a promising avenue for future progress. We make our code available at: https://github.com/open-forest-observatory/tree-detection-framework.

Related papers

• Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery [68.69685477556682]
  Current monitoring methods involve ground measurements, requiring extensive cost, time and labor.<n>Drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale.<n>We compare methods leveraging Segment Anything Model (SAM) for the task of automatic tree crown instance segmentation in high resolution drone imagery.<n>We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery.
  arXiv  Detail & Related papers  (2025-06-05T12:43:11Z)
• Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery [68.69685477556682]
  Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour.<n>Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery.<n>We compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations.<n>We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further.
  arXiv  Detail & Related papers  (2025-03-26T03:45:36Z)
• Exploring space efficiency in a tree-based linear model for extreme multi-label classification [11.18858602369985]
  Extreme multi-label classification (XMC) aims to identify relevant subsets from numerous labels. Among the various approaches for XMC, tree-based linear models are effective due to their superior efficiency and simplicity. In this work, we conduct both theoretical and empirical analyses on the space to store a tree model under the assumption of sparse data.
  arXiv  Detail & Related papers  (2024-10-12T15:02:40Z)
• Towards general deep-learning-based tree instance segmentation models [0.0]
  Deep-learning methods have been proposed which show the potential of learning to segment trees. We use seven diverse datasets found in literature to gain insights into the generalization capabilities under domain-shift. Our results suggest that a generalization from coniferous dominated sparse point clouds to deciduous dominated high-resolution point clouds is possible.
  arXiv  Detail & Related papers  (2024-05-03T12:42:43Z)
• SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data [15.438892555484616]
  This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types. It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The model, based on PointGroup architecture, is a 3D CNN with separate heads for semantic and instance segmentation.
  arXiv  Detail & Related papers  (2024-01-28T19:47:17Z)
• TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds [40.46280139210502]
  TreeLearn is a deep learning approach for tree instance segmentation of forest point clouds.<n>TreeLearn is trained on already segmented point clouds in a data-driven manner.<n>We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
  arXiv  Detail & Related papers  (2023-09-15T15:20:16Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Tree Detection and Diameter Estimation Based on Deep Learning [0.0]
  Tree perception is an essential building block toward autonomous forestry operations. Deep neural network models trained on our datasets achieve a precision of 90.4% for tree detection. Results offer promising avenues toward autonomous tree felling operations.
  arXiv  Detail & Related papers  (2022-10-31T15:51:32Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• Pre-Trained Models for Heterogeneous Information Networks [57.78194356302626]
  We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
  arXiv  Detail & Related papers  (2020-07-07T03:36:28Z)
• Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax [88.11979569564427]
  We provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors.
  arXiv  Detail & Related papers  (2020-06-18T10:24:26Z)

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.