Marc Conrad Russwurm

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Data imputation of incomplete image sequences is an essential prerequisite for analyzing and monitoring all development stages of plants in precision agriculture. For this purpose, we propose a conditional Wasserstein generative adversarial network TransGr ...

Plastic litter is a major environmental hazard that endangers human, animal, and plant health on the planet. A substantial portion of plastic pollutants is washed from rivers and beaches into the oceans and aggregates at the surface as marine debris before ...

Tropical deforestation is a major environmental problem with severe consequences such as carbon emissions or biodiversity loss. While much research focuses on monitoring and mapping deforestation, less attention is paid to understanding the various reasons ...

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: ...

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 ...

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 ...

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 ...

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, ...

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 ...

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

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 ...

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 ...

Detecting and quantifying marine pollution and macroplastics is an increasingly pressing ecological issue that directly impacts ecology and human health. Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and d ...

In this work, we propose an approach to aid in mapping small settlements, which are often misclassified by models trained on a large-scale context (global or regional). We leverage pre-trained land cover models and few-shot learning to enhance the detectio ...

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

Few-Shot Object Detection (FSOD) allows fast adaptation of an object detection model to new classes of objects using few examples per class. This has many applications, in particular in satellite and aerial observation, as it allows learning from experts w ...

Marc Conrad Russwurm

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Time Dependent Image Generation of Plants from Incomplete Sequences with CNN-Transformer

Improved marine debris detection in satellite imagery with automatic refinement of coarse hand annotations

Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

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

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

Meta-learning to address diverse Earth observation problems across resolutions

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

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

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

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

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

Meteor: Meta-learning connecting earth problems observed from space

Large-scale detection of marine debris in coastal areas with Sentinel-2

Detection of Settlements in Tanzania and Mozambique by Many Regional Few-Shot Models

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

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

Scene and animal attributes retrieval from camera trap data with domain-adapted language-vision models

Improving Few-Shot Object Detection with Object Part Proposals