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Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization.
Benchmarking Individual Tree Mapping with Sub-meter Imagery
There is a rising interest in mapping trees using satellite or aerial imagery, but there is no standardized evaluation protocol for comparing and enhancing methods.
Mega-cities dominate China's urban greening
Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being.
Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model
Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space.
High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach
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) is arguably needed to capture the differences in canopy height.
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data
We evaluate our approaches on a dataset that is based on Sentinel~2 satellite imagery and OpenStreetMap vector data.
