Publications by Benjamin Alexander Kellenberger

Related publications (20)

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

Devis Tuia, Benjamin Alexander Kellenberger, Thiên-Anh Claris Nguyen

Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Com ...

Elsevier2022

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Devis Tuia, Benjamin Alexander Kellenberger

Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretati ...

2020

Counting using deep learning regression gives value to ecological surveys

Devis Tuia, Benjamin Alexander Kellenberger

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, whi ...

Nature Research2021

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

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

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 ...

Elsevier2024

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

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

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

Perspectives in machine learning for wildlife conservation

Devis Tuia, Alexander Mathis, Mackenzie Mathis, Benjamin Alexander Kellenberger

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into ...

Nature Research2022

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

21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning

Devis Tuia, Benjamin Alexander Kellenberger

We address the task of automatically detecting and counting seabirds in unmanned aerial vehicle (UAV) imagery using deep convolutional neural networks (CNNs). Our study area, the coast of West Africa, harbours significant breeding colonies of terns and gul ...

2021

Exploring neural networks and their potential for species distribution modeling

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

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023

Deep Domain Adaptation in Earth Observation

Devis Tuia, Benjamin Alexander Kellenberger

When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during ...

Wiley2021

Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning

Devis Tuia, Benjamin Alexander Kellenberger

The goal of habitat suitability mapping is to predict the lo-cations in which a given species could be present. This is typically accomplished by statistical models which use envi-ronmental variables to predict species observation data. The relationship be ...

IEEE2022

Blue ice in Antarctica: The gateway to travel in time and space

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

Antarctica has unique areas that expose blue ice, which contrast to most of the continent (~98%) that is covered by snow. Some of these blue ice areas (BIAs) contain meteorite concentrations and (very) old ice, making them very valuable for understanding o ...

GFZ German Research Centre for Geosciences2023

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

Devis Tuia, Benjamin Alexander Kellenberger

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

Meteor: Meta-learning connecting earth problems observed from space

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

2023

Fine-grained population mapping from coarse census counts and open geodata

Devis Tuia, Benjamin Alexander Kellenberger

Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countries only aggregate census counts over large spatial units are collected, mor ...

Nature Research2022

POLO -Point-based, multi-class animal detection

Devis Tuia, Benjamin Alexander Kellenberger, Giacomo Günter May

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

Benjamin Alexander Kellenberger

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Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

Counting using deep learning regression gives value to ecological surveys

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

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

Meta-learning to address diverse Earth observation problems across resolutions

Perspectives in machine learning for wildlife conservation

Multi-Scale and Multimodal Species Distribution Modeling

21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning

Exploring neural networks and their potential for species distribution modeling

Deep Domain Adaptation in Earth Observation

Training Techniques for Presence-Only Habitat Suitability Mapping with Deep Learning

Blue ice in Antarctica: The gateway to travel in time and space

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

Meteor: Meta-learning connecting earth problems observed from space

Fine-grained population mapping from coarse census counts and open geodata

POLO -Point-based, multi-class animal detection

How to find a good image- text embedding for remote sensing visual question answering?

Mapping vulnerable populations with AI

Mapping forest cover dynamics in the Swiss Alps using 70 years of aerial imagery