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Publications associées (98)

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor-intensive and time-consuming to produce. Moreover, professional photograph interpreter ...

2022

How to Swoop and Grasp Like a Bird With a Passive Claw for a High-Speed Grasping

Dario Floreano, William John Stewart, Enrico Ajanic, Matthias Müller

There is a growing interest in unmanned aerial vehicles (UAVs) grasping, perching, and interacting with their surroundings by means of claws, arms, hooks, and other appendages. While multirotor vehicles can slowly lower onto a target object and grasp it, w ...

2022

Deep Learning Detection of GPS Spoofing

Mirjana Stojilovic, Olivia Jullian Parra

Unmanned aerial vehicles (UAVs) are widely deployed in air navigation, where numerous applications use them for safety-of-life and positioning, navigation, and timing tasks. Consequently, GPS spoofing attacks are more and more frequent. The aim of this wor ...

Springer, Cham2022

Cross-Modal Learning of Housing Quality in Amsterdam

Devis Tuia, Alex Hubertus Levering

In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts th ...

ACM2021

Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images

Devis Tuia, Benjamin Alexander Kellenberger, Xiaochen Zheng, Rui Gong

Automated animal censuses with aerial imagery are a vital ingredient towards wildlife conservation. Recent models are generally based on deep learning and thus require vast amounts of training data. Due to their scarcity and minuscule size, annotating anim ...

IEEE2021

Liveability from Above: Understanding Quality of Life with Overhead Imagery and Deep Neural Networks

Devis Tuia, Alex Hubertus Levering

Urban planners are increasingly interested in understanding what makes a neighbourhood pleasant and liveable. In this paper, we use the overhead perspective as a new way to describe and understand liveability of city neighborhoods. We predict building qual ...

IEEE2021

Multiscale Very High Resolution Topographic Models in Alpine Ecology: Pros and Cons of Airborne LiDAR and Drone-Based Stereo-Photogrammetry Technologies

Stéphane Joost, Estelle Rochat, Kevin Leempoel, Annie Sandrine Guillaume, Aude Rogivue

The vulnerability of alpine environments to climate change presses an urgent need to accurately model and understand these ecosystems. Popularity in the use of digital elevation models (DEMs) to derive proxy environmental variables has increased over the p ...

2021

Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient

Devis Tuia

Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildi ...

2021

Calibration of Fixed-Wing UAV Aerodynamic Coefficients with Photogrammetry for VDM-based Navigation

Jan Skaloud, Gabriel François Laupré

Autonomous navigation is a crucial aspect for fixed-wing unmanned aerial vehicle operation when encountering disturbance or denial of GNSS signal reception. Vehicle dynamic model-based navigation improves the performance of methods based exclusively on int ...

2021

Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

Vitor Pessoa Colombo

The World Health Organization (WHO) has stated that effective vector control measures are critical to achieving and sustaining reduction of vector-borne infectious disease incidence. Unmanned aerial vehicles (UAVs), popularly known as drones, can be an imp ...

2021

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