Publications

High-fidelity antarctic UAV simulation with thermal terrain modelling

J. Sandino, S. Boiteau, D. Brown, B. Bollard, and F. Gonzalez in 2026 International Conference on Unmanned Aircraft Systems (ICUAS) , Corfu, Greece , pp. 1216–1223 , Jun. 2026. doi: 10.1109/icuas69441.2026.11598730

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Drone hyperspectral imaging and artificial intelligence for monitoring moss and lichen in Antarctica

J. Sandino, J. Barthelemy, A. Doshi, K. Randall, S. A. Robinson, B. Bollard, and F. Gonzalez Sci. Rep. , vol. 15, no. 1 , pp. 27244 , Jul. 2025. doi: 10.1038/s41598-025-11535-4

Published in Scientific Reports, Nature Portfolio

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Revolutionising Antarctic Flora Monitoring with AI and Drones

J. Sandino, J. Barthelemy, A. Doshi, K. Randall, S. A. Robinson, B. Bollard, and F. Gonzalez in NVIDIA GTC 2025 , San Jose, CA, US , Mar. 2025.

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Reducing Object Detection Uncertainty from RGB and Thermal Data for UAV Outdoor Surveillance

J. Sandino, P. A. Caccetta, C. Sanderson, F. Maire, and F. Gonzalez in 2022 IEEE Aerospace Conference (AERO) , pp. 1–16 , Mar. 2022. doi: 10.1109/AERO53065.2022.9843611

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Fly high or fly low? Selecting time-efficient UAV search strategies for high-recall aerial detection

F. Loewenich, F. Maire, J. Sandino, and F. Gonzalez Remote Sens. (Basel) , vol. 18, no. 18 , pp. 3129 , Sep. 2026. doi: 10.3390/rs18183129

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Planning-to-execution evaluation of multi-UAV path planning for Antarctic remote sensing

D. Debnath, F. Vanegas, S. Boiteau, J. Galvez-Serna, J. Sandino, and F. Gonzalez Drones , vol. 10, no. 8 , pp. 574 , Jul. 2026. doi: 10.3390/drones10080574

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Aerial Visual Place Recognition in Antarctica: Towards Robust Monitoring in Extreme Environments

A. Fontan, J. Sandino, J. Civera, T. Fischer, F. Gonzalez, and M. Milford , pp. 183–190 , Jun. 2026.

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Vision Transformers and Drones for Antarctic Hyperspectral Vegetation Mapping

J. Sandino, J. Barthelemy, A. Doshi, K. Randall, S. A. Robinson, B. Bollard, and F. Gonzalez in NVIDIA GTC 2026 , San Jose, CA, US , Mar. 2026.

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Advances in machine learning approaches for UAV-based remote sensing in data-deficient Antarctic environments

B. Gorry, J. Sandino, P. Moghadam, F. Gonzalez, and J. Roberts Remote Sens. (Basel) , vol. 18, no. 3 , pp. 459 , Feb. 2026. doi: 10.3390/rs18030459

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Advancing real-time aerial wildfire detection through plume recognition and knowledge distillation

P. Keerthinathan, J. Sandino, S. Mahendren, A. Uthayasooriyan, J. Galvez, G. Hamilton, and F. Gonzalez Drones , vol. 9, no. 12 , pp. 827 , Nov. 2025. doi: 10.3390/drones9120827

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Detection and mapping of Antarctic lichen using drones, multispectral cameras, and supervised deep learning

N. Amarasingam, J. Sandino, A. Doshi, D. King, E. Blackman, J. Barthelemy, B. Bollard, S. A. Robinson, and F. Gonzalez Int. J. Appl. Earth Obs. Geoinf. , vol. 141, no. 104577 , pp. 104577 , Jul. 2025. doi: 10.1016/j.jag.2025.104577

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DECK-GA: A hybrid clustering and distance efficient genetic algorithm for scalable multi-UAV path planning

D. Debnath, F. Vanegas, J. Sandino, and F. Gonzalez in 2025 International Conference on Unmanned Aircraft Systems (ICUAS) , Charlotte, NC, USA , pp. 301–308 , May 2025. doi: 10.1109/icuas65942.2025.11007785

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Advancing sparse vegetation monitoring in the arctic and antarctic: A review of satellite and UAV remote sensing, machine learning, and sensor fusion

A. Platel, J. Sandino, J. Shaw, B. Bollard, and F. Gonzalez Remote Sens. (Basel) , vol. 17, no. 9 , pp. 1513 , Apr. 2025. doi: 10.3390/rs17091513

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Mapping of insect pest infestation for precision agriculture: A UAV-based multispectral imaging and deep learning techniques

N. Amarasingam, K. Powell, J. Sandino, D. Bratanov, A. S. Ashan Salgadoe, and F. Gonzalez Int. J. Appl. Earth Obs. Geoinf. , vol. 137, no. 104413 , pp. 104413 , Mar. 2025. doi: 10.1016/j.jag.2025.104413

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Unmanned Aerial Vehicles for real-time vegetation monitoring in Antarctica: A review

K. Lockhart, J. Sandino, N. Amarasingam, R. Hann, B. Bollard, and F. Gonzalez Remote Sens. (Basel) , vol. 17, no. 2 , pp. 304 , Jan. 2025. doi: 10.3390/rs17020304

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QUT Vice Chancellor’s Awards, Research and Innovation Excellence Award: “High Tech for Low Temperatures Team – Drones and AI for Conservation in Antarctica”

J. Sandino, N. Amarasingam, D. Bratanov, G. Broadbent, D. Gilligan, F. Gonzalez, J. Shaw, J. Galvez, and M. Swan , Dec. 2024.

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Remote Sensing Science in Antarctica and Sub-Antarctic Islands using Drones, Satellite, and AI: From Data Acquisition to Management/Policy

J. Sandino, B. Bollard, J. Shaw, F. Gonzalez, and S. A. Robinson in Australian Antarctic Research Conference , Hobart, Australia , Nov. 2024.

A review of UAV path-planning algorithms and obstacle avoidance methods for remote sensing applications

D. Debnath, F. Vanegas, J. Sandino, A. F. Hawary, and F. Gonzalez Remote Sens. (Basel) , vol. 16, no. 21 , pp. 4019 , Oct. 2024. doi: 10.3390/rs16214019

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Heatwaves and rapid freezing: does micro-topography influence how often Antarctic mosses exceed temperature thresholds?

K. Randall, X. Zheng, J. Sandino, and S. A. Robinson in 11th SCAR Open Science Conference , Pucon, Chile , Aug. 2024.

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Optimising UAS protocols for Vegetation Monitoring in Antarctica: Key Insights and Case Studies from the Windmill Islands, and Bunger Hills

J. Sandino, M. Swan, J. Galvez-Serna, N. Amarasingam, A. Doshi, K. Randall, S. A. Robinson, B. Bollard, and F. Gonzalez in 11th SCAR Open Science Conference , Pucon, Chile , Aug. 2024.

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Bitou bush detection and mapping using UAV-based multispectral and hyperspectral imagery and artificial intelligence

N. Amarasingam, J. E Kelly, J. Sandino, M. Hamilton, F. Gonzalez, R. L Dehaan, L. Zheng, and H. Cherry Remote Sens. Appl. Soc. Environ. , vol. 34, no. 101151 , pp. 101151 , Apr. 2024. doi: 10.1016/j.rsase.2024.101151

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Monitoring of Antarctica's fragile vegetation using drone-based remote sensing, multispectral imagery and AI

D. Raniga, N. Amarasingam, J. Sandino, A. Doshi, J. Barthelemy, K. Randall, S. A. Robinson, F. Gonzalez, and B. Bollard Sensors (Basel) , vol. 24, no. 4 , pp. 1063 , Feb. 2024. doi: 10.3390/s24041063

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A green fingerprint of Antarctica: Drones, hyperspectral imaging, and machine learning for moss and lichen classification

J. Sandino, B. Bollard, A. Doshi, K. Randall, J. Barthelemy, S. A. Robinson, and F. Gonzalez Remote Sens. (Basel) , vol. 15, no. 24 , pp. 5658 , Dec. 2023. doi: 10.3390/rs15245658

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Enhancing antarctic conservation with advanced remote sensing and machine learning techniques for ASPA scale mapping and monitoring

J. Sandino, B. Bollard, A. Doshi, J. Barthelemy, and F. Gonzalez in XIII SCAR Biology Symposium 2023 , Pucon, Chile , Jul. 2023. [In Press]

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Photos from the field: spying on Antarctic moss using drones, MossCam, smart sensors and AI

B. Bollard, J. Barthélemy, J. Sandino, and K. Randall The Conversation , May 2023.

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Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning

N. Amarasingam, M. Hamilton, J. E. Kelly, L. Zheng, J. Sandino, F. Gonzalez, R. L. Dehaan, and H. Cherry Remote Sensing , vol. 15, no. 6 , pp. 1633 , Mar. 2023. doi: 10.3390/rs15061633

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Detection of white leaf disease in sugarcane crops using UAV-derived RGB imagery with existing deep learning models

N. Amarasingam, F. Gonzalez, A. S. A. Salgadoe, J. Sandino, and K. Powell Remote Sens. (Basel) , vol. 14, no. 23 , pp. 6137 , Dec. 2022. doi: 10.3390/rs14236137

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UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration

J. Galvez-Serna, F. Vanegas, S. Brar, J. Sandino, D. Flannery, and F. Gonzalez Drones , vol. 6, no. 12 , pp. 391 , Dec. 2022. doi: 10.3390/drones6120391

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Detection of Parthenium weed (Parthenium hysterophorus L.) and its growth stages using artificial intelligence

B. Costello, O. O. Osunkoya, J. Sandino, W. Marinic, P. Trotter, B. Shi, F. Gonzalez, and K. Dhileepan Collect. FAO Agric. , vol. 12, no. 11 , pp. 1838 , Nov. 2022. doi: 10.3390/agriculture12111838

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Weed managers guide to remote detection: Understanding opportunities and limitations of technologies for remote detection of weeds

J. Kelly, M. Rahaman, J. Sandino, L. Zheng, H. Cherry, M. A. Hamilton, R. Dehaan, F. Gonzalez, W. Menz, and L. Grant in 22nd Australasian Weeds Conference , Adelaide, Australia , pp. 58–62 , Sep. 2022.

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Autonomous decision-making for UAVs operating under environmental and object detection uncertainty

J. Sandino , Jun. 2022. doi: 10.5204/thesis.eprints.232513

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Design and Flight Testing of a Two DOF Decoupled Pantograph Aerial Manipulator for Commercial UAVs

C. Luxton, J. Sandino, F. Vanegas, and F. Gonzalez in 2022 International Conference on Unmanned Aircraft Systems (ICUAS) , pp. 1–10 , Jun. 2022. doi: 10.1109/ICUAS54217.2022.9836224

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Design and Flight Test of an Aerial Manipulator for Applications in GPS-Denied Environments

M. Ng, F. Vanegas, K. Morton, J. Sandino, and F. Gonzalez in 2022 International Conference on Unmanned Aircraft Systems (ICUAS) , pp. 20–29 , Jun. 2022. doi: 10.1109/ICUAS54217.2022.9836060

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Real-time segmentation of desiccation cracks onboard UAVs for planetary exploration

J. Galvez-Serna, N. Mandel, J. Sandino, F. Vanegas, N. Ly, D. T. Flannery, and F. Gonzalez in 2022 IEEE Aerospace Conference (AERO) , Big Sky, MT, USA , pp. 1–12 , Mar. 2022. doi: 10.1109/aero53065.2022.9843515

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Resolution-adaptive Quadtrees for Semantic Segmentation Mapping in UAV Applications

N. Mandel, J. Sandino, J. Galvez-Serna, F. Vanegas, M. Milford, and F. Gonzalez in 2022 IEEE Aerospace Conference (AERO) , Big Sky, MT, USA , pp. 1–17 , Mar. 2022. doi: 10.1109/AERO53065.2022.9843498

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Autonomous Mapping of Desiccation Cracks via a Probabilistic-based Motion Planner Onboard UAVs

J. Sandino, J. Galvez–Serna, N. Mandel, F. Vanegas, and F. Gonzalez in 2022 IEEE Aerospace Conference (AERO) , Big Sky, MT, USA , pp. 1–14 , Mar. 2022. doi: 10.1109/AERO53065.2022.9843299

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Drone-based autonomous motion planning system for outdoor environments under object detection uncertainty

J. Sandino, F. Maire, P. Caccetta, C. Sanderson, and F. Gonzalez Remote Sens. (Basel) , vol. 13, no. 21 , pp. 4481 , Nov. 2021. doi: 10.3390/rs13214481

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UAV framework for autonomous onboard navigation and people/object detection in cluttered indoor environments

J. Sandino, F. Vanegas, F. Maire, P. Caccetta, C. Sanderson, and F. Gonzalez Remote Sens. (Basel) , vol. 12, no. 20 , pp. 3386 , Oct. 2020. doi: 10.3390/rs12203386

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Autonomous UAV navigation for active perception of targets in uncertain and cluttered environments

J. Sandino, F. Vanegas, F. Gonzalez, and F. Maire in IEEE Aerospace Conference , Big Sky, United States , pp. 1–12 , Mar. 2020. doi: 10.1109/AERO47225.2020.9172808

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Phylloxera: Improving pest surveillance in vineyards through drones, hyperspectral and spatial data

F. Vanegas, D. Bratanov, J. Sandino, K. Powell, J. Weiss, and F. Gonzalez Wine \& Viticulture Journal , vol. 33, no. 4 , pp. 42–52 , Sep. 2018.

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A novel approach for invasive weeds and vegetation surveys using UAS and artificial intelligence

J. Sandino, and F. Gonzalez in 2018 23rd International Conference on Methods \& Models in Automation \& Robotics (MMAR) , Miedzyzdroje , pp. 515–520 , Aug. 2018. doi: 10.1109/mmar.2018.8485874

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Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence

J. Sandino, G. Pegg, F. Gonzalez, and G. Smith Sensors , vol. 18, no. 4 , pp. 944 , Mar. 2018. doi: 10.3390/s18040944

Over 150 citations.

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UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands

J. Sandino, F. Gonzalez, K. Mengersen, and K. J. Gaston Sensors , vol. 18, no. 2 , pp. 605 , Feb. 2018. doi: 10.3390/s18020605

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Revolutionising the detection and mapping of exotic weeds using UASs and machine learning

J. Sandino, S. Harris, P. Trotter, A. Shukla, and F. Gonzalez in International Master Class in Plant Biosecurity , Jan. 2018.

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Optimising surveillance protocols using unmanned aerial systems

B. McCornack, G. P. Bhattarai, T. Witt, F. Gonzalez, G. Hamilton, F. Vanegas, J. Froese, J. Sandino, J. Weiss, and G. Pegg , pp. 25 , 2018.

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Prediction of reproductive system affectation in Sprague Dawley rats by food intake exposed with Fenthion, using Naïve Bayes classifier and genetic algorithms

J. Sandino, D. Amaya Hurtado, and O. Lucia Ramos Biosci. Biotechnol. Res. Asia , vol. 14, no. 4 , pp. 1291–1297 , Dec. 2017. doi: 10.13005/bbra/2572

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UAS remote sensing efforts for Myrtle rust management

F. Vanegas, G. Pegg, J. Kok, J. Sandino, and F. Gonzalez in Science Protecting Plant Health 2017 , Sep. 2017.

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Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery

J. Sandino, A. Wooler, and F. Gonzalez Sensors , vol. 17, no. 10 , pp. 2196 , Sep. 2017. doi: 10.3390/s17102196

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Bitou bush surveillance UAV trial

S. Harris, P. Trotter, F. Gonzalez, and J. Sandino in 14th Queensland Weed Symposium , 2017.

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Prediction of Endocrine System Affectation in Fisher 344 Rats by Food Intake Exposed with Malathion, Applying Naïve Bayes Classifier and Genetic Algorithms

J. Sandino, D. A. Hurtado, and O. L. R. Sandoval Int. J. Prev. Med. , vol. 7, no. 1 , pp. 111 , Sep. 2016. doi: 10.4103/2008-7802.190611

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Method for estimating leaf coverage in strawberry plants using digital image processing

J. Sandino, O. L. Ramos-Sandoval, and D. Amaya-Hurtado Rev. Bras. Eng. Agric. Ambient./Braz. J. Agric. Environ. Eng. , vol. 20, no. 8 , pp. 716–721 , Aug. 2016. doi: 10.1590/1807-1929/agriambi.v20n8p716-721

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Monitoreo Preliminar de Incidencia de Fisiopatías en Cultivos de Fresa usando Procesamiento Digital de Imágenes

J. Sandino, D. Amaya Hurtado, and O. L. Ramos Sandoval Biotecnología en el Sector Agropecuario y Agroindustrial , vol. 14, no. 1 , pp. 45–52 , 2016. doi: 10.18684/BSAA(14)45-52

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Método preliminar de detección de patógenos biológicos en cultivos de fresa por medio del procesamiento digital de imágenes

D. Amaya-Hurtado, and J. Sandino Rev. Investig. Agrar. Ambient. , vol. 6, no. 1 , pp. 111 , Mar. 2015. doi: 10.22490/21456453.1267

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Desarrollo de proyectos en monitoreo de cultivos agrícolas a través de la visión de máquina

J. Sandino , Dec. 2014.

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