Data imbalance is ubiquitous when applying machine learning to real-worl...
Clouds and haze often occlude optical satellite images, hindering contin...
In this technical report we compare different deep learning models for
p...
The worldwide variation in vegetation height is fundamental to the globa...
We propose the first accurate digitization and color reconstruction proc...
The synergistic combination of deep learning models and Earth observatio...
Monitoring and managing Earth's forests in an informed manner is an impo...
The increasing demand for commodities is leading to changes in land use
...
In the last years we have witnessed the fields of geosciences and remote...
Style transfer aims to render the content of a given image in the
graphi...
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate
m...
We introduce PC2WF, the first end-to-end trainable deep network architec...
Automated semantic segmentation and object detection are of great import...
We investigate active learning in the context of deep neural network mod...
In this paper we propose a novel supervised image classification method ...
We introduce an approach for updating older tree inventories with geogra...
We propose to predict histograms of object sizes in crowded scenes direc...
We propose a new stackable recurrent cell (STAR) for recurrent neural
ne...
Up-to-date catalogs of the urban tree population are important for
munic...
Sentinel-2 multi-spectral images collected over periods of several month...
Guided super-resolution is a unifying framework for several computer vis...
We propose a method to leapfrog pixel-wise, semantic segmentation of (ae...
Very High Spatial Resolution (VHSR) large-scale SAR image databases are ...
This study deals with semantic segmentation of high-resolution (aerial)
...
In this paper, we discuss and review how combined multi-view imagery fro...
We present an end-to-end trainable deep convolutional neural network (DC...