Jan Dirk Wegner
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MT-GSR4B: Multi-Temporally Guided Super-Resolution for Above-Ground Biomass Estimation Open
Above-ground biomass (AGB) is a critical indicator of various ecological parameters, such as forest degradation or carbon storage. However, obtaining ground-truth AGB measurements is costly and time-intensive, requires expert knowledge, an…
View article: Assessment of tree species specific phenology metrics from Sentinel-2 data to complement in situ monitoring
Assessment of tree species specific phenology metrics from Sentinel-2 data to complement in situ monitoring Open
Monitoring tree phenology is key to understanding forest dynamics under climate change. Events like leaf unfolding and senescence affect ecosystem productivity, tree mortality, and species interactions. While in situ phenology observations…
AGBD: A Global-scale Biomass Dataset Open
Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity’s biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they f…
GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance Open
Accurate Above-Ground Biomass (AGB) mapping at both large scale and high spatio-temporal resolution is essential for applications ranging from climate modeling to biodiversity assessment, and sustainable supply chain monitoring. At present…
View article: Assessing Intraspecific Variation of Tree Species Based on Sentinel-2 Vegetation Indices Across Space and Time
Assessing Intraspecific Variation of Tree Species Based on Sentinel-2 Vegetation Indices Across Space and Time Open
Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—whic…
Stability and transferability of broadly trained phenology models in a changing climate Open
A variety of phenology process-based models have been developed to simulate environmental influences on the timing of spring and autumn phenophases. Similar performances between different types of mechanistic models have raised questions a…
Deep learning meets tree phenology modelling: <span>PhenoFormer</span> versus process‐based models Open
Predicting phenology, that is the timing of seasonal events of plant life such as leaf emergence and colouration in relation to climate fluctuations, is essential for anticipating future changes in carbon sequestration and tree vitality in…
Towards ROXAS AI: automatic multi-species ring boundaries segmentation as regression in anatomical images Open
Introduction Quantitative wood anatomy (QWA) along a time series of tree rings (known as tree-ring anatomy or dendroanatomy) has proven to be very valuable for reconstructing climate and for investigating the responses of trees and shrubs …
SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology Open
With the exacerbation of the biodiversity and climate crises, macroecological pursuits such as global biodiversity mapping become more urgent. Remote sensing offers a wealth of Earth observation data for ecological studies, but the scarcit…
Climplicit: Climatic Implicit Embeddings for Global Ecological Tasks Open
Deep learning on climatic data holds potential for macroecological applications. However, its adoption remains limited among scientists outside the deep learning community due to storage, compute, and technical expertise barriers. To addre…
GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance Open
Accurate Above-Ground Biomass (AGB) mapping at both large scale and high spatio-temporal resolution is essential for applications ranging from climate modeling to biodiversity assessment, and sustainable supply chain monitoring. At present…
Lossy Neural Compression for Geospatial Analytics: A review Open
International audience
An open-source tool for mapping war destruction at scale in Ukraine using Sentinel-1 time series Open
Access to detailed war impact assessments is crucial for humanitarian organizations to assist affected populations effectively. However, maintaining a comprehensive understanding of the situation on the ground is challenging, especially in…
Advances in Historical Aerial Image Analysis: Boosting SfM Pipelines with Learned Models Open
Trend determination for earth surface processes requires long and continuous and certain measurements, but long-term records of landscape change are often limited in temporal and spatial extent. Scanned historical aerial imagery serve as a…
SeCo-Eco: Global multiband seasonal pre-training dataset and self-supervised model for ecological applications Open
With the biodiversity crisis and land use intensification, macroecological questions related to biodiversity assessment and conservation are becoming increasingly pressing. Such questions require global datasets such as satellite imagery. …
View article: Mapping Tea Plantations in Africa with Computer Vision
Mapping Tea Plantations in Africa with Computer Vision Open
Tea, Camellia sinensis (L.) Kuntze is a globally significant crop, with approximately 6.6 billion cups consumed daily, making it the second most consumed beverage after water. It supports millions of livelihoods and contributes significant…
FlorID – A nationwide identification service for plants from photos and habitat information Open
Citizen science has become key to biodiversity monitoring but critically depends on accurate quality control that is scalable and tailored to the focal region. We developed FlorID, a free-to-use identification service for all native and ma…
Deep learning meets tree phenology modeling: PhenoFormer vs. process-based models Open
Phenology, the timing of cyclical plant life events such as leaf emergence and coloration, is crucial in the bio-climatic system. Climate change drives shifts in these phenological events, impacting ecosystems and the climate itself. Accur…
Tackling deforestation with deep-learning-based cattle counts on satellite images Open
Cattle ranching and linked tropical deforestation remain intractable global sustainability challenges with large implications for climate security and rural wellbeing. Despite the large area devoted to cattle in the tropics, cattle numbers…
Interactive snow avalanche segmentation from webcam imagery: results, potential, and limitations Open
For many safety-related applications such as hazard mapping or road management, well-documented avalanche events are crucial. Nowadays, despite the variety of research directions, the available data are mostly restricted to isolated locati…
Sat-SINR: High-Resolution Species Distribution Models Through Satellite Imagery Open
We propose a deep learning approach for high-resolution species distribution modelling (SDM) at large scale combining point-wise, crowd-sourced species observation data and environmental data with Sentinel-2 satellite imagery. What makes t…
AGBD: A Global-scale Biomass Dataset Open
Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity's biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they f…
Comment on egusphere-2024-498, Interactive Snow Avalanche Segmentation from Webcam Imagery: results, potential and limitations Open
Abstract. For many safety-related applications such as hazard mapping or road management, well documented avalanche events are crucial. Nowadays, despite research into different directions, the available data is mostly restricted to isolat…
Flood Water Depth Prediction with Convolutional Temporal Attention Networks Open
Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use fo…