EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping

• URL: http://arxiv.org/abs/2607.06105v1
• Date: Tue, 07 Jul 2026 10:16:31 GMT
• Title: EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
• Abstract summary: The framework targeted two ecologically significant halophytic grasses, Spartina maritima and Puccinellia maritima.<n>In order to identify these plants from the imagery, our segmentation yielded reliable species masks.<n>The developed system, named EcoVision, establishes a practical foundation for scalable, high-resolution salt marsh monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: High-resolution RGB imagery acquired from low-altitude UAV surveys was processed through a modular pipeline incorporating transformer-based semantic segmentation, connected-component vegetation extraction, fine-grained species classification using a ConvNeXt architecture, and grid-based dominance scoring at 2x2m resolution. The framework targeted two ecologically significant halophytic grasses, Spartina maritima and Puccinellia maritima, and was trained using a curated and manually annotated UAV imagery, along with biodiversity imagery sourced from publicly accessible datasets. In order to identify these plants from the imagery, our segmentation yielded reliable species masks (mean IoU = 0.56; pixel-level accuracy = 0.96), while object-level classification achieved very good discrimination (F1 = 0.99). Dominance estimates closely matched quadrat-based field surveys, with mean absolute differences below 8%, preserving fine-scale spatial structure under realistic survey conditions. The developed system, named EcoVision, establishes a practical foundation for scalable, high-resolution salt marsh monitoring, demonstrating how AI-driven workflows can translate pixel-level predictions into ecologically interpretable metrics.

Related papers

• Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples [0.0]
  Coastal algal bloom monitoring requires frequent, spatially detailed, and globally consistent observations, provided by Landsat-8/9 and Sentinel-2 A/B/C.<n>Their use in aquatic environments remains challenging due to limited spectral coverage and a lack of harmonized reflectance products.<n>This study presents the first successful implementation of vision transformer-based coastal algal bloom mapping using 30-m Landsat-Sentinel-2 images.
  arXiv  Detail & Related papers  (2026-06-15T19:40:18Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• Habitat Classification from Ground-Level Imagery Using Deep Neural Networks [1.3408365072149797]
  This study applies state-of-the-art deep neural network architectures to ground-level habitat imagery.<n>We evaluate two families of models -- convolutional neural networks (CNNs) and vision transformers (ViTs)<n>Our results demonstrate that ViTs consistently outperform state-of-the-art CNN baselines on key classification metrics.
  arXiv  Detail & Related papers  (2025-07-05T12:07:13Z)
• YH-MINER: Multimodal Intelligent System for Natural Ecological Reef Metric Extraction [23.4289262373633]
  Coral reefs, crucial for sustaining marine biodiversity and ecological processes, face escalating threats.<n>This study develops the YH-MINER system, establishing an intelligent framework for "object detection-semantic segmentation-prior input"<n>The system achieves genus-level classification accuracy of 88% and simultaneously extracting core ecological metrics.
  arXiv  Detail & Related papers  (2025-05-28T11:36:18Z)
• 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)
• Image-Based Relocalization and Alignment for Long-Term Monitoring of Dynamic Underwater Environments [57.59857784298534]
  We propose an integrated pipeline that combines Visual Place Recognition (VPR), feature matching, and image segmentation on video-derived images.<n>This method enables robust identification of revisited areas, estimation of rigid transformations, and downstream analysis of ecosystem changes.
  arXiv  Detail & Related papers  (2025-03-06T05:13:19Z)
• Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss [2.6489824612123716]
  We tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology.
  arXiv  Detail & Related papers  (2024-08-31T17:40:17Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• 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)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

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.