Wildlife Ecology and Conservation

Derniers papiers

• What Does Animal Re-Identification Learn? Linear Biological Concepts and Their Origins in Visual Representations

  Robert Nolting, Alexandra Schild, Moritz Weckbecker, Maximilian Schall, Gerard de Melo · 9 septembre 2026

  Conservation increasingly relies on camera traps that collect more wildlife imagery than experts can manually analyze, making animal re-identification (Re-ID) essential for monitoring individuals and populations. Yet understanding which cues drive model decisions is challenging for ViT-based Re-ID m…

• Counting Animals in Camera-Traps Image Sequences without Count Labels: Winning Solution to the iWildCam 2021 Challenge

  Fagner Cunha, Juan G. Colonna, Eulanda M. dos Santos · 4 septembre 2026

  Camera traps have become an essential tool for wildlife monitoring, motivating the development of computer vision methods for the automated extraction of information from these data. While most prior work has focused on species identification, many ecological applications also require estimating the…

• Decentralized Vision-Based Autonomous Aerial Wildlife Monitoring

  Makram Chahine, William Yang, Alaa Maalouf, Justin Siriska, Ninad Jadhav, Daniel Vogt, Stephanie Gil, Robert Wood, Daniela Rus · 4 septembre 2026

  Wildlife field operations demand efficient parallel deployment methods to identify and interact with specific individuals, enabling simultaneous collective behavioral analysis, and health and safety interventions. Previous robotics solutions approach the problem from the herd perspective, or are man…

• Visual-Prompt Guided Wildlife Instance-Level Recognition

  Mufhumudzi Muthivhi, Jiahao Huo, Terence van Zyl, Fredrik Gustafsson · 20 août 2026

  Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and re-identification pipeline. We propose a one-stage end-to-end detection and re-identification model that performs identity searching within the latent space. We a…

• When One Modality Is Not Enough: Multimodal Sex and Life-Stage Classification of Red Deer from Aerial RGB-Thermal Video

  Hugo Markoff, Christoph Praschl, Ivan Ludoški, Sara Beery, Michael Ørsted, David C. Schedl · 10 août 2026

  Aerial drone surveys increasingly support wildlife population estimation, yet a useful census is more than a count: population dynamics are defined by species composition, sex ratios and age structure, that is, by which species are present and how a herd splits into adult males, adult females and ju…

• Oh Deer, How Should I Handle This? Seasonal Priors for Selective Wildlife Annotation and Classification

  Hugo Markoff, Christoph Praschl, Anton Hjalte Jørgensen, Christian Emil Mogensen, Mathias Bech Skadhauge, Sara Beery, Michael Ørsted, David C. Schedl · 5 août 2026

  Fine-grained wildlife classification in aerial imagery is limited not only by model performance, but also by unreliable labels: animals occupy few pixels, key visual cues vary seasonally, and modality-specific evidence can be ambiguous. We study adult-male identification in red deer ($\textit{Cervus…

• Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring

  Bharath Pillai, Varun Viswapriyan, Christopher Stewart, Tanya Berger-Wolf, Jenna Kline · 23 juin 2026

  Scaling wildlife monitoring for real-world conservation deployments requires automated analysis of smart sensors that operate under severe annotation scarcity. We propose leveraging expert knowledge of species activity patterns as an annotation-free validation signal for multimodal monitoring pipeli…

• Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals

  Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver, Katie Appleby, Sarah Beatham, Naomi Davies Walsh, Stuart Nixon, Naomi Matthews, Chris Sutherland, Kelly Hitchcock · 10 juin 2026

  Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles. In an attempt to remove b…

• Transformer-Based Wildlife Species Classification from Daily Movement Trajectories

  Obed Irakoze, Prasenjit Mitra · 11 mai 2026

  Inferring the identity of wildlife species from daily movement data alone is a challenging task. We train sequence models on large-scale, 7-species GPS trajectories from the Movebank platform. Trajectories models are evaluated using a protocol in which entire telemetry studies or regions are heldout…

• WildLIFT: Lifting monocular drone video to 3D for species-agnostic wildlife monitoring

  Vandita Shukla, Fabio Remondino, Blair Costelloe, Benjamin Risse · 28 avril 2026

  Monocular RGB cameras mounted on drones are widely used for wildlife monitoring, yet most analytical pipelines remain confined to two-dimensional image space, leaving geometric information in video underexploited. We present WildLIFT, a computational framework that integrates three-dimensional scene…

• RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery

  Bowen Zhang, Jesse T. Boulerice, Charvi Mendiratta, Nikhil Kuniyil, Satish Kumar, Hila Shamon, B. S. Manjunath · 23 avril 2026

  Automated wildlife monitoring from aerial imagery is vital for conservation but remains limited by two persistent challenges: the difficulty of detecting small, rare species and the high cost of large-scale expert annotation. Prairie dogs exemplify this problem -- they are ecologically important yet…

• Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan

  Shivarth Rai, Tejeswar Pokuri · 20 avril 2026

  Atmospheric haze significantly degrades wildlife imagery, impeding computer vision applications critical for conservation, such as animal detection, tracking, and behavior analysis. To address this challenge, we introduce AnimalHaze3k a synthetic dataset comprising of 3,477 hazy images generated fro…

• CHIRP dataset: towards long-term, individual-level, behavioral monitoring of bird populations in the wild

  Alex Hoi Hang Chan, Neha Singhal, Onur Kocahan, Andrea Meltzer, Saverio Lubrano, Miyako H. Warrington, Michel Griesser, Fumihiro Kano, Hemal Naik · 27 mars 2026

  Long-term behavioral monitoring of individual animals is crucial for studying behavioral changes that occur over different time scales, especially for conservation and evolutionary biology. Computer vision methods have proven to benefit biodiversity monitoring, but automated behavior monitoring in w…

• MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification

  William Grolleau, Achraf Chaouch, Astrid Sabourin, Guillaume Lapouge, Catherine Achard · 5 mars 2026

  Animal re-identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial-Ground (AG-ReID) settings where models must match individuals across drastic elevation changes. However, existing datasets lack the precise angular annotations required to systematically ana…

• Degradation-based augmented training for robust individual animal re-identification

  Thanos Polychronou, Lukáš Adam, Viktor Penchev, Kostas Papafitsoros · 5 mars 2026

  Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based on their fine-scale unique morphological characteristics. Current state-of-the-art models for multispecies re- identification are based on deep metric le…

• Tracking Feral Horses in Aerial Video Using Oriented Bounding Boxes

  Saeko Takizawa, Tamao Maeda, Shinya Yamamoto, Hiroaki Kawashima · 5 mars 2026

  The social structures of group-living animals such as feral horses are diverse and remain insufficiently understood, even within a single species. To investigate group dynamics, aerial videos are often utilized to track individuals and analyze their movement trajectories, which are essential for eva…

• Improving Wildlife Out-of-Distribution Detection: Africas Big Five

  Mufhumudzi Muthivhi, Jiahao Huo, Fredrik Gustafsson, Terence L. van Zyl · 3 mars 2026

  Mitigating human-wildlife conflict seeks to resolve unwanted encounters between these parties. Computer Vision provides a solution to identifying individuals that might escalate into conflict, such as members of the Big Five African animals. However, environments often contain several varied species…

• Weakly supervised framework for wildlife detection and counting in challenging Arctic environments: a case study on caribou (Rangifer tarandus)

  Ghazaleh Serati, Samuel Foucher, Jerome Theau · 28 janvier 2026

  Caribou across the Arctic has declined in recent decades, motivating scalable and accurate monitoring approaches to guide evidence-based conservation actions and policy decisions. Manual interpretation from this imagery is labor-intensive and error-prone, underscoring the need for automatic and reli…

• Beyond Off-the-Shelf Models: A Lightweight and Accessible Machine Learning Pipeline for Ecologists Working with Image Data

  Clare Chemery, Hendrik Edelhoff, Ludwig Bothmann · 23 janvier 2026

  We introduce a lightweight experimentation pipeline designed to lower the barrier for applying machine learning (ML) methods for classifying images in ecological research. We enable ecologists to experiment with ML models independently, thus they can move beyond off-the-shelf models and generate ins…

• Animal Re-Identification on Microcontrollers

  Yubo Chen, Di Zhao, Yun Sing Koh, Talia Xu · 10 décembre 2025

  Camera-based animal re-identification (Animal Re-ID) can support wildlife monitoring and precision livestock management in large outdoor environments with limited wireless connectivity. In these settings, inference must run directly on collar tags or low-power edge nodes built around microcontroller…

• ShadowWolf -- Automatic Labelling, Evaluation and Model Training Optimised for Camera Trap Wildlife Images

  Jens Dede, Anna Förster · 9 décembre 2025

  The continuous growth of the global human population is leading to the expansion of human habitats, resulting in decreasing wildlife spaces and increasing human-wildlife interactions. These interactions can range from minor disturbances, such as raccoons in urban waste bins, to more severe consequen…

• CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx

  Lukas Picek, Elisa Belotti, Michal Bojda, Ludek Bufka, Vojtech Cermak, Martin Dula, Rostislav Dvorak, Luboslav Hrdy, Miroslav Jirik, Vaclav Kocourek, Josefa Krausova, Jir{\i} Labuda, Jakub Straka, Ludek Toman, Vlado Trul{\i}k, Martin Vana, Miroslav Kutal · 1 décembre 2025

  We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and c…

• The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification

  Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall, Pablo Palencia, Chris Beirne, Tilo Burghardt, Majid Mirmehdi, Hjalmar K\"uhl, Mimi Arandjelovic, Sam Pottie, Peter Bermant, Brandon Asheim, Yi Jin Toh, Adam Elzinga, Jason Holmberg, Andrew Whitworth, Eleanor Flatt, Laura Gustafson, Chaitanya Ryali, Yuan-Ting Hu, Baishan Guo, Andrew Westbury, Kate Saenko, Didac Suris · 25 novembre 2025

  Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual re-identification and behavior recognition. However, existing datasets are limited in scale, constrained to a few species…

• Lacking Data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: a case study with muskox (Ovibos moschatus)

  Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau · 18 novembre 2025

  Accurate population estimates are essential for wildlife management, providing critical insights into species abundance and distribution. Traditional survey methods, including visual aerial counts and GNSS telemetry tracking, are widely used to monitor muskox populations in Arctic regions. These app…