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Devis Tuia

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Publications associées (151)

POLO -Point-based, multi-class animal detection

Devis Tuia, Benjamin Alexander Kellenberger

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 ...

2024

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Devis Tuia, Jan Pisl, Gaston Jean Lenczner

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

Institute of Electrical and Electronics Engineers Inc.2024

Multilingual Vision-Language Pre-training for the Remote Sensing Domain

Devis Tuia

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on ...

Association for Computing Machinery, Inc2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Devis Tuia, Jan Pisl, Marc Conrad Russwurm, Lloyd Haydn Hughes, Gaston Jean Lenczner, Jan Dirk Wegner

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

Iop Publishing Ltd2024

WildCLIP: Scene and Animal Attribute Retrieval from Camera Trap Data with Domain-Adapted Vision-Language Models

Devis Tuia, Valentin Alexandre Guy Gabeff, Alexander Mathis, Marc Conrad Russwurm

Wildlife observation with camera traps has great potential for ethology and ecology, as it gathers data non-invasively in an automated way. However, camera traps produce large amounts of uncurated data, which is time-consuming to annotate. Existing methods ...

2024

Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

Devis Tuia, Marc Conrad Russwurm

In some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, ...

Amer Geophysical Union2024

Estimating Fine-Grained Population Growth Rates from Coarse Census Data

Devis Tuia

Fine-grained population estimation is important for several domains, such as urban planning, public health, and humanitarian action. Due to limited resources, population maps with sufficient spatial resolution and temporal frequency are not available for m ...

Institute of Electrical and Electronics Engineers Inc.2024

Knowledge-Aware Text–Image Retrieval for Remote Sensing Images

Devis Tuia, Li Mi, Javiera Francisca Castillo Navarro

Image-based retrieval in large Earth observation archives is challenging because one needs to navigate across thousands of candidate matches only with the query image as a guide. By using text as information supporting the visual query, the retrieval syste ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Devis Tuia

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in datascarce regions. To address it we have develop ...

Springer Science and Business Media LLC2024

On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

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

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited ...

2024

Visual question answering on remote sensing images

Devis Tuia

Remote sensing visual question answering (RSVQA) aims at predicting an answer to a question (both in natural language) about an overhead image. Through natural language processing, this task allows end users to extract high-level information from remote se ...

Elsevier2024

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Devis Tuia, Antoine Bosselut, Li Mi, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

Springer Cham2024

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

Devis Tuia, Antoine Bosselut, Li Mi, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

Asking questions about visual environments is a crucial way for intelligent agents to understand rich multi-faceted scenes, raising the importance of Visual Question Generation (VQG) systems. Apart from being grounded to the image, existing VQG systems can ...

Association for the Advancement of Artificial Intelligence (AAAI)2024

Training Visual Language Models with Object Detection: Grounded Change Descriptions in Satellite Images

Devis Tuia, Antoine Bosselut, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol

Recently, generalist Vision Language Models (VLMs) have shown exceptional progress in tasks previously dominated by specialized computer vision models. This becomes more prevalent when visual grounding capabilities, such as the ability to reason over input ...

Institute of Electrical and Electronics Engineers Inc.2024

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Devis Tuia

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

PUBLIC LIBRARY SCIENCE2024

Knowledge-Aware Visual Question Generation for Remote Sensing Images

Devis Tuia, Li Mi, Javiera Francisca Castillo Navarro

With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing image retrieval. However, automatically generated image-based questions tend to be simpli ...

IEEE2024

Better, Not Just More: Data-centric machine learning for Earth observation

Devis Tuia

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchm ...

2024

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Devis Tuia, Valentin Alexandre Guy Gabeff, Alexander Mathis, Marc Conrad Russwurm

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models ############# Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis Affiliation: EPFL Date: January, 2024 Link to the article: ...

Zenodo2024

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