arXiv Machine Learning By Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas Corpetti

A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

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arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.

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arXiv AI
Jul 8

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

arXiv:2607. 06105v1 Announce Type: cross 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.

By Innocent Onyenonachi, Peter J. Lawerance, Nadia Kanwal
arXiv AI
Aug 24

WeedNet: A Foundation Model-Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

arXiv:2505.18930v2 Announce Type: replace-cross Abstract: Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increa...

By Yanben Shen, Timilehin T. Ayanlade, Venkata Naresh Boddepalli, Mojdeh Saadati, Ashlyn Rairdin, Zi K. Deng, Muhammad Arbab Arshad, Aditya Balu, Daren Mueller, Asheesh K Singh, Wesley Everman, Nirav Merchant, Baskar Ganapathysubramanian, Meaghan Anderson, Soumik Sarkar, Arti Singh
arXiv Computer Vision
Aug 28

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

The paper presents a framework that enhances deep‑learning tree‑cover mapping in New South Wales by fusing multiple imagery sources and normalizing image quality. It introduces an image‑composition technique that removes defects and a prediction‑fusion method that reduces reliance on any single image, together cutting errors by 38.2 % and 53.6 % respectively. Label transfer across diverse imagery further boosts data efficiency, yielding error reductions of 28.1 %–76.2 % and a 13‑fold decrease in performance variability across dates.

By Kal Backman, Jared Wood, Adam Roff
arXiv AI
Jun 2

CAFOSat: A Strongly Annotated Dataset for Infrastructure-Aware CAFO Mapping Using High-Resolution Imagery

arXiv:2606. 00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public health, and disease surveillance concerns.

By Oishee Bintey Hoque, Nibir Chandra Mandal, Mandy L Wilson, Samarth Swarup, Madhav Marathe, Abhijin Adiga