Publications associées à Cross-Modal Learning of Housing Quality in Amsterdam

swissALPS: a Dataset for Imbalanced Land Cover Mapping in Alpine Regions

Our dataset consists of very high-resolution aerial images (50cm) and a digital elevation model (50cm) that covers approx. 2300 km2 of land above 2000m altitude in the southwestern part of Switzerland. Our land cover labels focus on alpine land cover that ...

IEEE DataPort2023

Stay close, but not too close: aerial image analysis reveals patterns of social distancing in seal colonies

Devis Tuia, Benjamin Alexander Kellenberger

Many species aggregate in dense colonies. Species-specific spatial patterns provide clues about how colonies are shaped by various (a)biotic factors, including predation, temperature regulation or disease transmission. Using aerial imagery, we examined the ...

2023

Time Series Analysis of Urban Liveability

Devis Tuia, Diego Marcos Gonzalez, Alex Hubertus Levering

In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level. Our liveability reference data is defined by a country-wise yearly survey based on a set of indicators combined into a liv ...

IEEE2023

Multi-task prompt-RSVQA to explicitly count objects on aerial images

Devis Tuia, Christel Marie Tartini-Chappuis, Sylvain Lobry, Javiera Francisca Castillo Navarro, Nicola Antonio Santacroce

Introduced to enable a wider use of Earth Observation images using natural language, Remote Sensing Visual Question Answering (RSVQA) remains a challenging task, in particular for questions related to counting. To address this specific challenge, we propos ...

2023

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

The Foldable Drone: A Morphing Quadrotor That Can Squeeze and Fly

Dario Floreano, Stefano Mintchev, Davide Scaramuzza

The recent advances in state estimation, perception, and navigation algorithms have significantly contributed to the ubiquitous use of quadrotors for inspection, mapping, and aerial imaging. To further increase the versatility of quadrotors, recent works i ...

2019

Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks

Roberto Castello, Jean-Louis Scartezzini, Adrian Guerra, Martin Esguerra

Mapping the location and size of solar installations in urban areas can be a valuable input for policymakers and for investing in distributed energy infrastructures. Machine Learning techniques, combined with satellite and aerial imagery, allow to overcome ...

IOP Science2019

Lake Imaging and Monitoring Aerial Drone

David Andrew Barry, Ludovic Zulliger, Jean-Luc Liardon, Karl Johannes Kangur, Nawaaz Sidharth Gujja Shaik, Lukas Oliver Hostettler

We describe the development of a BVLOS (Beyond Visual Line-Of-Sight) model aircraft (UAV). The broad design requirements included (i) fuselage capable of accommodating an imaging package or other instrumentation, (ii) suitability for over-lake BVLOS author ...

2018

Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency

Pascal Fua, Engin Türetken, Raphael Sznitman, Jan Kybic, Agata Justyna Mosinska, Przemyslaw Rafal Glowacki, Miguel Amável Dos Santos Pinheiro, Daniel Lebrecht

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously proces ...

2017