activity
20232026
most citedTaking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions

9 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2026

The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series

Jan Hemmerling, Marcel Schwieder, Philippe Rufin +4

Organic farming is a key element in achieving more sustainable agriculture. For a better understanding of the development and impact of organic farming, comprehensive, spatially ex…

cs.CV2025

Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale

Mirela G. Tulbure, Julio Caineta, Mark Broich +6

Floods are among the most damaging weather-related hazards, and in 2024, the warmest year on record, extreme flood events affected communities across five continents. Earth observa…

cs.CV2025

National level satellite-based crop field inventories in smallholder landscapes

Philippe Rufin, Pauline Lucie Hammer, Leon-Friedrich Thomas +5

The design of science-based policies to improve the sustainability of smallholder agriculture is challenged by a limited understanding of fundamental system properties, such as the…

cs.CV2024★ 2 cited

Responsible AI for Earth Observation

Pedram Ghamisi, Weikang Yu, Andrea Marinoni +9

The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has brought geoscience and remote sensing into an era of unparalleled capabilities. AI's tra…

econ.GN2024

Explaining the emergence of land-use frontiers

Patrick Meyfroidt, Dilini Abeygunawardane, Matthias Baumann +12

Land use expansion is linked to major sustainability concerns including climate change, food security and biodiversity loss. This expansion is largely concentrated in so-called fro…

cs.CV2023★ 9 cited

Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions

Philippe Rufin, Sherrie Wang, Sá Nogueira Lisboa +3

Transfer learning allows for resource-efficient geographic transfer of pre-trained field delineation models. However, the scarcity of labeled data for complex and dynamic smallhold…