EarthShift is a new benchmark using paired datasets to measure robustness of geospatial foundation models to realistic distribution shifts, finding consistent 15-20% performance drops out-of-distribution across 8 models and 11 tasks.
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
6 Pith papers cite this work. Polarity classification is still indexing.
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OSMGraphCLIP learns global location embeddings from OSM graphs via multi-scale graph encoding and contrastive alignment that match or exceed satellite baselines on many socioeconomic, health, and environmental tasks.
Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
AdaGC adaptively applies gradient calibration with dual EMA in SPML for RS imagery to recover full labels from single-positive annotations and reports SOTA results on two benchmarks.
DiffuSAM fuses diffusion-based localization cues with SAM models to deliver over 14% higher Acc@0.5 in zero-shot object grounding for remote sensing imagery compared to prior methods.
Introduces SGR and CGR refinement pipelines plus majority-voting ensemble to improve visual grounding accuracy in remote sensing by combining RemoteSAM and SAM3.
citing papers explorer
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EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
EarthShift is a new benchmark using paired datasets to measure robustness of geospatial foundation models to realistic distribution shifts, finding consistent 15-20% performance drops out-of-distribution across 8 models and 11 tasks.
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OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs
OSMGraphCLIP learns global location embeddings from OSM graphs via multi-scale graph encoding and contrastive alignment that match or exceed satellite baselines on many socioeconomic, health, and environmental tasks.
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Sentinel2Cap: A Human-Annotated Benchmark Dataset for Multimodal Remote Sensing Image Captioning
Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
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Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification
AdaGC adaptively applies gradient calibration with dual EMA in SPML for RS imagery to recover full labels from single-positive annotations and reports SOTA results on two benchmarks.
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DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery
DiffuSAM fuses diffusion-based localization cues with SAM models to deliver over 14% higher Acc@0.5 in zero-shot object grounding for remote sensing imagery compared to prior methods.
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Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting
Introduces SGR and CGR refinement pipelines plus majority-voting ensemble to improve visual grounding accuracy in remote sensing by combining RemoteSAM and SAM3.