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20182025
most citedMeta-Learning for Few-Shot Land Cover Classification

21 citations · 22 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.CV20251 cited

Assessing the Effectiveness of Deep Embeddings for Tree Species Classification in the Dutch Forest Inventory

Takayuki Ishikawa, Carmelo Bonannella, Bas J. W. Lerink +1

National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry ex…

cs.CV2023

SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery

Konstantin Klemmer, Esther Rolf, Caleb Robinson +2

Geographic information is essential for modeling tasks in fields ranging from ecology to epidemiology. However, extracting relevant location characteristics for a given task can be…

cs.CV2023

Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2

Marc Rußwurm, Sushen Jilla Venkatesa, Devis Tuia

Detecting and quantifying marine pollution and macro-plastics is an increasingly pressing ecological issue that directly impacts ecology and human health. Efforts to quantify marin…

cs.CV2018

MultiNet: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

Tim G. J. Rudner, Marc Rußwurm, Jakub Fil +4

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural networ…

cs.CV2018

Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery

Marc Rußwurm, Marco Körner

Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods. The vast majority of remote sensing approaches either selectivel…

cs.CV2018

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Marc Rußwurm, Marco Körner

Earth observation (EO) sensors deliver data with daily or weekly temporal resolution. Most land use and land cover (LULC) approaches, however, expect cloud-free and mono-temporal o…