activity
20202026
collaborators

5 papers

cs.CV2026

Monitoring Pasture Restoration from Satellite Image Time Series: Caveats and Opportunities

Linnea Sartorius, Isak Randahl, Delia Fano Yela +3

Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (SITS) have been widely used f…

cs.LG2026

From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps

Ghjulia Sialelli, Robin Young, Yuchang Jiang +9

Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (…

cs.CV2025

Grazing Detection using Deep Learning and Sentinel-2 Time Series Data

Aleksis Pirinen, Delia Fano Yela, Smita Chakraborty +1

Grazing shapes both agricultural production and biodiversity, yet scalable monitoring of where grazing occurs remains limited. We study seasonal grazing detection from Sentinel-2 L…

cs.CV2020

Embodied Visual Active Learning for Semantic Segmentation

David Nilsson, Aleksis Pirinen, Erik Gärtner +1

We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding by actively selecting vi…

cs.CV2020

Deep Reinforcement Learning for Active Human Pose Estimation

Erik Gärtner, Aleksis Pirinen, Cristian Sminchisescu

Most 3d human pose estimation methods assume that input -- be it images of a scene collected from one or several viewpoints, or from a video -- is given. Consequently, they focus o…