9 citations · 17 across the 14 of their papers we have counts for
9 papers · 1 filter
MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation
Carmen Oliver Huidobro, Mikolaj Czerkawski, Kevin Monsalvez-Pozo +3
Pretraining generalist Earth observation foundation models requires data that combine several sensors, varied temporal sampling and, increasingly, text, yet open datasets rarely pr…
EarthEmbeddingExplorer: A Web Application for Cross-Modal Retrieval of Global Satellite Images
Yijie Zheng, Weijie Wu, Zhuoyao He +5
While the Earth observation community has witnessed a surge in high-impact foundation models and global Earth embedding datasets, a significant barrier remains in translating these…
COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data
Miguel Espinosa, Eva Gmelich Meijling, Valerio Marsocci +2
Earth observation applications increasingly rely on data from multiple sensors, including optical, radar, elevation, and land-cover. Relationships between modalities are fundamenta…
TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data
Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci +8
Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public da…
Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space
Mikolaj Czerkawski, Marcin Kluczek, Jędrzej S. Bojanowski
With the ever-increasing volumes of the Earth observation data present in the archives of large programmes such as Copernicus, there is a growing need for efficient vector represen…
Robustness of Deep Neural Networks for Micro-Doppler Radar Classification
Mikolaj Czerkawski, Carmine Clemente, Craig Michie +1
With the great capabilities of deep classifiers for radar data processing come the risks of learning dataset-specific features that do not generalize well. In this work, the robust…
Major TOM: Expandable Datasets for Earth Observation
Alistair Francis, Mikolaj Czerkawski
Deep learning models are increasingly data-hungry, requiring significant resources to collect and compile the datasets needed to train them, with Earth Observation (EO) models bein…
From LAION-5B to LAION-EO: Filtering Billions of Images Using Anchor Datasets for Satellite Image Extraction
Mikolaj Czerkawski, Alistair Francis
Large datasets, such as LAION-5B, contain a diverse distribution of images shared online. However, extraction of domain-specific subsets of large image corpora is challenging. The…
Detecting Cloud Presence in Satellite Images Using the RGB-based CLIP Vision-Language Model
Mikolaj Czerkawski, Robert Atkinson, Christos Tachtatzis
This work explores capabilities of the pre-trained CLIP vision-language model to identify satellite images affected by clouds. Several approaches to using the model to perform clou…