86 citations · 150 across the 6 of their papers we have counts for
7 papers
Pixel-Level Transformers in Remote Sensing: A Canopy Height Case Study
Sven Ligensa, Jan Pauls, Karsten Schrödter +2
Predicting canopy height from medium-resolution satellite imagery is a common and scalable approach for assessing the condition of the world's forests, which play a crucial role in…
Optimizing GEDI Simulator Configuration for European Temperate Forests
Selim Behloul, Nikola Besic, Steven Hancock +5
Accurate estimation of aboveground biomass density is essential for quantifying forest carbon stocks. NASA's GEDI mission provides valuable canopy structure data, but its sparse sa…
SERA-H: Super-Resolution of Sentinel Time Series for Fine-Scale Canopy Height Mapping
Thomas Boudras, Martin Schwartz, Rasmus Fensholt +6
High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods usi…
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
Ibrahim Fayad, Max Zimmer, Martin Schwartz +6
Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-s…
Open-Canopy: Towards Very High Resolution Forest Monitoring
Fajwel Fogel, Yohann Perron, Nikola Besic +8
Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However,…
Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights
Ibrahim Fayad, Philippe Ciais, Martin Schwartz +8
Accurate and timely monitoring of forest canopy heights is critical for assessing forest dynamics, biodiversity, carbon sequestration as well as forest degradation and deforestatio…
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
Martin Schwartz, Philippe Ciais, Catherine Ottlé +11
In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) i…