Devis Tuia

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Related publications (151)

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

2025

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Devis Tuia, Jonathan Paul Sauder

Coral reefs are crucial for biodiversity and provide vital resources for humankind. But despite such a central role, they are confronted to increasing threats linked to climate change, pollution, and local stressors. To ensure effective conservation, effic ...

Springer Science and Business Media Deutschland GmbH2025

MaskSDM: Adaptive Species Distribution Modeling Through Data Masking

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden

Species distribution models (SDMs) correlate species occurrences with environmental conditions and underpin much of ecological research. A key consideration in developing SDMs is selecting the optimal set of environmental predictor variables, which vary de ...

Springer Science and Business Media Deutschland GmbH2025

POLO – Point-Based, Multi-class Animal Detection

Devis Tuia

Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology. Most detectors require training images with annotated bounding boxes, which are tedious, expensive, and ...

Springer Science and Business Media Deutschland GmbH2025

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Devis Tuia

Social media is increasingly being employed to develop Cultural Ecosystem Services (CES) indicators. The image-sharing platform Flickr has been one of the most popular sources of data. Most large-scale studies, however, tend to only use the number of image ...

2024

Scalable semantic 3D mapping of coral reefs with deep learning

Anders Meibom, Devis Tuia, Guilhem Maurice Louis Banc-Prandi, Jonathan Paul Sauder

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existen ...

2024

Regional uniqueness of tree species composition and response to forest loss and climate change

Devis Tuia, Nina Marion Aurélia Van Tiel, Loïc Pellissier

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global oc ...

Nature Portfolio2024

Multi-Scale and Multimodal Species Distribution Modeling

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Springer Nature Link2024

Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward

Devis Tuia

Earth observation (EO) is increasingly used for mapping and monitoring processes occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a global view, consistent in time, of the state of our forests, oceans, and growing ur ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Springernature2024

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