Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy

• URL: http://arxiv.org/abs/2608.26471v1
• Date: Wed, 26 Aug 2026 23:54:54 GMT
• Title: Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy
• Abstract summary: Tree cover mapping requires reliable and high-quality imagery, free of cloud and weather defects to ensure accurate model outputs.<n>Deep learning approaches can generate high quality maps with minimal human intervention but require large amounts of human annotated data to be successful.<n>We propose a framework consisting of methods that aim to improve the data efficiency and robustness of deep learning models.
• Score: 0.04024586290381058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tree cover maps are a fundamental remote sensing product, used to derive ecological insights about the landscape and are essential to change detection, vegetation mapping and fire monitoring programs. However, comprehensive tree cover mapping requires reliable and high-quality imagery, free of cloud and weather defects to ensure accurate model outputs. Deep learning approaches can generate high quality maps with minimal human intervention but require large amounts of human annotated data to be successful. In this work we propose a framework consisting of methods that aim to improve the data efficiency and robustness of deep learning models using data fusion techniques to segment woody vegetation defined as vegetation over the height of 2m across the state of New South Wales, Australia. To improve robustness against varying image quality, we propose an image composition method that normalizes the imagery and removes defects, whilst also minimizing the reliance on individual image quality by proposing a prediction fusion method. The two methods resulted in an error reduction of 38.2% and 53.6% respectively compared to single-source imagery. To address deep learning approaches' limitation of requiring large amounts of data, we apply label transfer to multiple sources of imagery as a form of data augmentation to improve data efficiency. Learning from multiple image sources was shown to be the biggest improvement in performance, resulting in an error reduction between 28.1% to 76.2% across the different validation experiments, whilst reducing the standard deviation of performance across image dates by a factor of 13.

Related papers

• Multispectral Blind Image Super-Resolution for Standing Dead Tree Segmentation [3.164363223464948]
  We propose a generic blind super-resolution framework to learn the mapping from low-resolution to high-resolution multispectral image domains.<n>Our approach operates solely on unpaired samples, mimicking real-world conditions.<n>This is the first study to perform real-world and generic super-resolution for multispectral data in the scope of standing dead tree segmentation.
  arXiv  Detail & Related papers  (2026-05-04T11:13:50Z)
• Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation [49.83611963142304]
  Images manipulated using image editing tools can mislead viewers and pose significant risks to social security.<n>One of the main barriers in this area is the high cost of data acquisition and the severe lack of high-quality annotated datasets.<n>We utilize a large collection of manually forged images from the web, as well as automatically generated annotations.<n>We construct MIMLv2, a large-scale, diverse, and high-quality dataset containing 246,212 manually forged images with pixel-level mask annotations.
  arXiv  Detail & Related papers  (2025-08-28T16:44:40Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion [3.399289369740637]
  This paper presents a pioneering study on post-training pruning of Stable Diffusion 2. It addresses the critical need for model compression in text-to-image domain. We propose an optimal pruning configuration that prunes the text encoder to 47.5% and the diffusion generator to 35%.
  arXiv  Detail & Related papers  (2024-11-22T18:29:37Z)
• Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
  We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images. Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness. Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
  arXiv  Detail & Related papers  (2024-11-12T01:17:27Z)
• Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization [56.95046107046027]
  We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for scene coordinate regression. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain.
  arXiv  Detail & Related papers  (2023-10-10T20:11:13Z)
• Randomize to Generalize: Domain Randomization for Runway FOD Detection [1.4249472316161877]
  Tiny Object Detection is challenging due to small size, low resolution, occlusion, background clutter, lighting conditions and small object-to-image ratio. We propose a novel two-stage methodology Synthetic Image Augmentation (SRIA) to enhance generalization capabilities of models encountering 2D datasets. We report that detection accuracy improved from an initial 41% to 92% for OOD test set.
  arXiv  Detail & Related papers  (2023-09-23T05:02:31Z)
• Searching a Compact Architecture for Robust Multi-Exposure Image Fusion [55.37210629454589]
  Two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. This study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19% improvement in PSNR for general scenarios and an impressive 23.5% enhancement in misaligned scenarios.
  arXiv  Detail & Related papers  (2023-05-20T17:01:52Z)
• Image augmentation improves few-shot classification performance in plant disease recognition [0.0]
  I tackle the problem of plant image data scarcity by exploring the efficacy of various data augmentation techniques. I propose an augmentation scheme utilizing a sequence of different augmentations which consistently improves accuracy through many trials.
  arXiv  Detail & Related papers  (2022-08-25T00:53:19Z)
• Towards Unpaired Depth Enhancement and Super-Resolution in the Wild [121.96527719530305]
  State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- and high-resolution depth maps of the same scenes. We consider an approach to depth map enhancement based on learning from unpaired data.
  arXiv  Detail & Related papers  (2021-05-25T16:19:16Z)
• Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks [28.32775611169636]
  We introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. A novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. Our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU)
  arXiv  Detail & Related papers  (2020-08-10T11:00:19Z)

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.