TTE replaces fixed spherical bases with differentiable Voronoi partitions plus shared semantic tokens to create adaptive geolocation encoders that reach new SOTA on geospatial tasks and iNaturalist species classification.
and Zhu, Xiao Xiang , year=
4 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
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abstract
Self-supervised pre-training bears potential to generate expressive representations without human annotation. Most pre-training in Earth observation (EO) are based on ImageNet or medium-size, labeled remote sensing (RS) datasets. We share an unlabeled RS dataset SSL4EO-S12 (Self-Supervised Learning for Earth Observation - Sentinel-1/2) to assemble a large-scale, global, multimodal, and multi-seasonal corpus of satellite imagery from the ESA Sentinel-1 \& -2 satellite missions. For EO applications we demonstrate SSL4EO-S12 to succeed in self-supervised pre-training for a set of methods: MoCo-v2, DINO, MAE, and data2vec. Resulting models yield downstream performance close to, or surpassing accuracy measures of supervised learning. In addition, pre-training on SSL4EO-S12 excels compared to existing datasets. We make openly available the dataset, related source code, and pre-trained models at https://github.com/zhu-xlab/SSL4EO-S12.
representative citing papers
Concept-based abductive and contrastive explanations find minimal high-level concepts that causally determine vision model outcomes on individual images or groups sharing a specified behavior.
Self-supervised satellite imagery representations encode physically meaningful environmental signals (ERA5 variables) that correlate with downstream task performance, particularly for agriculture and disaster domains.
TESSERA learns robust label-efficient embeddings from irregular multi-modal EO time series via Barlow Twins plus global shuffling and mix-based regularizers, delivering SOTA accuracy on classification, segmentation and regression tasks while releasing planetary-scale embeddings and code.
citing papers explorer
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Tessellating The Earth
TTE replaces fixed spherical bases with differentiable Voronoi partitions plus shared semantic tokens to create adaptive geolocation encoders that reach new SOTA on geospatial tasks and iNaturalist species classification.
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Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
Concept-based abductive and contrastive explanations find minimal high-level concepts that causally determine vision model outcomes on individual images or groups sharing a specified behavior.
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Probing Geospatial SSL Representations with Environmental Signals
Self-supervised satellite imagery representations encode physically meaningful environmental signals (ERA5 variables) that correlate with downstream task performance, particularly for agriculture and disaster domains.
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TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
TESSERA learns robust label-efficient embeddings from irregular multi-modal EO time series via Barlow Twins plus global shuffling and mix-based regularizers, delivering SOTA accuracy on classification, segmentation and regression tasks while releasing planetary-scale embeddings and code.