558 citations · 2k across the 15 of their papers we have counts for
16 papers
Learning to Forecast Crop Growth from Earth Observation Data
Dominik Senti, Mehmet Ozgur Turkoglu, Michele Volpi +1
Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we inve…
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
Joaquin Gajardo, Michele Volpi, Marko Mihajlovic +3
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intri…
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
Samantha Biegel, David Brüggemann, Francesco Grossi +3
Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we…
Evaluating the Role of Training Data Origin for Country-Scale Cropland Mapping in Data-Scarce Regions: A Case Study of Nigeria
Joaquin Gajardo, Michele Volpi, Daniel Onwude +1
Cropland maps are essential for remote sensing-based agricultural monitoring, providing timely insights without extensive field surveys. Machine learning enables large-scale mappin…
Self-supervised learning unveils change in urban housing from street-level images
Steven Stalder, Michele Volpi, Nicolas Büttner +3
Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress…
What You See is What You Classify: Black Box Attributions
Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta +2
An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, d…